diff --git a/docs/time_crystals/time_crystal_circuit_generation.ipynb b/docs/time_crystals/time_crystal_circuit_generation.ipynb new file mode 100644 index 00000000..b8b7d50d --- /dev/null +++ b/docs/time_crystals/time_crystal_circuit_generation.ipynb @@ -0,0 +1,526 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "10a61b4d73a5" + }, + "outputs": [], + "source": [ + "# Copyright 2021 Google\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7763e5d20492" + }, + "source": [ + "# Time Crystal Circuit Generation\n", + "This notebook covers how Many Body Local Discrete Time Crystal circuit lists are created, from the paper: Observation of Time-Crystalline Eigenstate Order on a Quantum Processor ([Nature](https://www.nature.com/articles/s41586-021-04257-w)). \n", + "\n", + "Quantum computers and gate-based quantum circuits turn out to be well suited for crafting systems that exhibit time-crystalline behavior. Behavior is crystalline with respect to time if it has some consistent and stable pattern over time. This system's pattern must be resilient against perturbation in the same way that a space-crystalline object, like a diamond, is still a diamond if moved or heated. \n", + "\n", + "The quantum computer supplies a system of many qubits, locally connected to each other in a chain. A many-body local system like this is critical for the existence of a time crystal. Without an MBL system, it is expected that the system's state would decay into a maximum entropy state that is incompatible with the goal of stable and consistent time structure. \n", + "\n", + "The time-crystalline behavior that the DTC circuits demonstrate is perhaps the simplest kind of time-structured behavior, oscillation. Each circuit is built with some number of identical $U$-cycles. Time is represented by a circuit list where each circuit is ordered with increasingly many $U$-cycles; each cycle is a discrete time step. The eventual effect of these $U$-cycles is consistent oscillations of each qubits' polarizations. The experiments performed demonstrate that this time-crystalline oscillation behavior is stable in spite of different initial states and introduced random potentials. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f53722cb0850" + }, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "0e4595827ec0" + }, + "outputs": [], + "source": [ + "!pip install cirq --pre --quiet\n", + "try:\n", + " import recirq\n", + "except ImportError:\n", + " !pip install --quiet git+https://github.com/quantumlib/ReCirq" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "e5e4f66e8e67" + }, + "outputs": [], + "source": [ + "import cirq\n", + "import recirq.time_crystals as time_crystals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a12b700f009e" + }, + "source": [ + "## Circuit Construction\n", + "Each DTC circuit is created with symbolic parameters. Parameter values are supplied near run/simulation time with a `cirq.ParamResolver`, which means the circuit list needs to be generated only once for potentially many different experimental parameter configurations. \n", + "\n", + "The code below uses an IPython-specific utility to inspect the code of the key function that creates the symbolic circuit list, `recirq.time_crystals.symbolic_dtc_circuit_list()`. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "c0f1c1475908" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
def symbolic_dtc_circuit_list(\n",
+       "        qubits: Sequence[cirq.Qid],\n",
+       "        cycles: int\n",
+       "        ) -> List[cirq.Circuit]:\n",
+       "\n",
+       "    """ Create a list of symbolically parameterized dtc circuits, with increasing cycles\n",
+       "    Args:\n",
+       "        qubits: ordered sequence of available qubits, which are connected in a chain\n",
+       "        cycles: maximum number of cycles to generate up to\n",
+       "    Returns:\n",
+       "        list of circuits with `0, 1, 2, ... cycles` many cycles\n",
+       "    """\n",
+       "\n",
+       "    num_qubits = len(qubits)\n",
+       "\n",
+       "    # Symbol for g\n",
+       "    g_value = sp.Symbol('g')\n",
+       "\n",
+       "    # Symbols for random variance (h) and initial state, one per qubit\n",
+       "    local_fields = sp.symbols('local_field_:' + str(num_qubits))\n",
+       "    initial_state = sp.symbols('initial_state_:' + str(num_qubits))\n",
+       "\n",
+       "    # Symbols used for PhasedFsimGate, one for every qubit pair in the chain\n",
+       "    thetas = sp.symbols('theta_:' + str(num_qubits - 1))\n",
+       "    zetas = sp.symbols('zeta_:' + str(num_qubits - 1))\n",
+       "    chis = sp.symbols('chi_:' + str(num_qubits - 1))\n",
+       "    gammas = sp.symbols('gamma_:' + str(num_qubits - 1))\n",
+       "    phis = sp.symbols('phi_:' + str(num_qubits - 1))\n",
+       "\n",
+       "    # Initial moment of Y gates, conditioned on initial state\n",
+       "    initial_operations = cirq.Moment([cirq.Y(qubit) ** initial_state[index] for index, qubit in enumerate(qubits)])\n",
+       "\n",
+       "    # First component of U cycle, a moment of XZ gates.\n",
+       "    sequence_operations = []\n",
+       "    for index, qubit in enumerate(qubits):\n",
+       "        sequence_operations.append(cirq.PhasedXZGate(\n",
+       "                x_exponent=g_value, axis_phase_exponent=0.0,\n",
+       "                z_exponent=local_fields[index])(qubit))\n",
+       "\n",
+       "    # Initialize U cycle\n",
+       "    u_cycle = [cirq.Moment(sequence_operations)]\n",
+       "\n",
+       "    # Second and third components of U cycle, a chain of 2-qubit PhasedFSim gates\n",
+       "    #   The first component is all the 2-qubit PhasedFSim gates starting on even qubits\n",
+       "    #   The second component is the 2-qubit gates starting on odd qubits\n",
+       "    operation_list, other_operation_list = [],[]\n",
+       "    previous_qubit, previous_index = None, None\n",
+       "    for index, qubit in enumerate(qubits):\n",
+       "        if previous_qubit is None:\n",
+       "            previous_qubit, previous_index = qubit, index\n",
+       "            continue\n",
+       "\n",
+       "        # Add an fsim gate\n",
+       "        coupling_gate = cirq.ops.PhasedFSimGate(\n",
+       "            theta=thetas[previous_index],\n",
+       "            zeta=zetas[previous_index],\n",
+       "            chi=chis[previous_index],\n",
+       "            gamma=gammas[previous_index],\n",
+       "            phi=phis[previous_index]\n",
+       "        )\n",
+       "        operation_list.append(coupling_gate.on(previous_qubit, qubit))\n",
+       "\n",
+       "        # Swap the operation lists, to avoid two-qubit gate overlap\n",
+       "        previous_qubit, previous_index = qubit, index\n",
+       "        operation_list, other_operation_list = other_operation_list, operation_list\n",
+       "\n",
+       "    # Add the two components into the U cycle\n",
+       "    u_cycle.append(cirq.Moment(operation_list))\n",
+       "    u_cycle.append(cirq.Moment(other_operation_list))\n",
+       "\n",
+       "    # Prepare a list of circuits, with n=0,1,2,3 ... cycles many cycles\n",
+       "    circuit_list = []\n",
+       "    total_circuit = cirq.Circuit(initial_operations)\n",
+       "    circuit_list.append(total_circuit.copy())\n",
+       "    for c in range(cycles):\n",
+       "        for m in u_cycle:\n",
+       "            total_circuit.append(m)\n",
+       "        circuit_list.append(total_circuit.copy())\n",
+       "\n",
+       "    return circuit_list\n",
+       "
\n" + ], + "text/latex": [ + "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", + "\\PY{k}{def} \\PY{n+nf}{symbolic\\PYZus{}dtc\\PYZus{}circuit\\PYZus{}list}\\PY{p}{(}\n", + " \\PY{n}{qubits}\\PY{p}{:} \\PY{n}{Sequence}\\PY{p}{[}\\PY{n}{cirq}\\PY{o}{.}\\PY{n}{Qid}\\PY{p}{]}\\PY{p}{,}\n", + " \\PY{n}{cycles}\\PY{p}{:} \\PY{n+nb}{int}\n", + " \\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{List}\\PY{p}{[}\\PY{n}{cirq}\\PY{o}{.}\\PY{n}{Circuit}\\PY{p}{]}\\PY{p}{:}\n", + "\n", + " \\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{} Create a list of symbolically parameterized dtc circuits, with increasing cycles}\n", + "\\PY{l+s+sd}{ Args:}\n", + "\\PY{l+s+sd}{ qubits: ordered sequence of available qubits, which are connected in a chain}\n", + "\\PY{l+s+sd}{ cycles: maximum number of cycles to generate up to}\n", + "\\PY{l+s+sd}{ Returns:}\n", + "\\PY{l+s+sd}{ list of circuits with `0, 1, 2, ... cycles` many cycles}\n", + "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", + "\n", + " \\PY{n}{num\\PYZus{}qubits} \\PY{o}{=} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{qubits}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Symbol for g}\n", + " \\PY{n}{g\\PYZus{}value} \\PY{o}{=} \\PY{n}{sp}\\PY{o}{.}\\PY{n}{Symbol}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{g}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Symbols for random variance (h) and initial state, one per qubit}\n", + " \\PY{n}{local\\PYZus{}fields} \\PY{o}{=} \\PY{n}{sp}\\PY{o}{.}\\PY{n}{symbols}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{local\\PYZus{}field\\PYZus{}:}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o}{+} \\PY{n+nb}{str}\\PY{p}{(}\\PY{n}{num\\PYZus{}qubits}\\PY{p}{)}\\PY{p}{)}\n", + " \\PY{n}{initial\\PYZus{}state} \\PY{o}{=} \\PY{n}{sp}\\PY{o}{.}\\PY{n}{symbols}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{initial\\PYZus{}state\\PYZus{}:}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o}{+} \\PY{n+nb}{str}\\PY{p}{(}\\PY{n}{num\\PYZus{}qubits}\\PY{p}{)}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Symbols used for PhasedFsimGate, one for every qubit pair in the chain}\n", + " \\PY{n}{thetas} \\PY{o}{=} \\PY{n}{sp}\\PY{o}{.}\\PY{n}{symbols}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{theta\\PYZus{}:}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o}{+} \\PY{n+nb}{str}\\PY{p}{(}\\PY{n}{num\\PYZus{}qubits} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{)}\\PY{p}{)}\n", + " \\PY{n}{zetas} \\PY{o}{=} \\PY{n}{sp}\\PY{o}{.}\\PY{n}{symbols}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{zeta\\PYZus{}:}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o}{+} \\PY{n+nb}{str}\\PY{p}{(}\\PY{n}{num\\PYZus{}qubits} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{)}\\PY{p}{)}\n", + " \\PY{n}{chis} \\PY{o}{=} \\PY{n}{sp}\\PY{o}{.}\\PY{n}{symbols}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{chi\\PYZus{}:}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o}{+} \\PY{n+nb}{str}\\PY{p}{(}\\PY{n}{num\\PYZus{}qubits} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{)}\\PY{p}{)}\n", + " \\PY{n}{gammas} \\PY{o}{=} \\PY{n}{sp}\\PY{o}{.}\\PY{n}{symbols}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{gamma\\PYZus{}:}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o}{+} \\PY{n+nb}{str}\\PY{p}{(}\\PY{n}{num\\PYZus{}qubits} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{)}\\PY{p}{)}\n", + " \\PY{n}{phis} \\PY{o}{=} \\PY{n}{sp}\\PY{o}{.}\\PY{n}{symbols}\\PY{p}{(}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{phi\\PYZus{}:}\\PY{l+s+s1}{\\PYZsq{}} \\PY{o}{+} \\PY{n+nb}{str}\\PY{p}{(}\\PY{n}{num\\PYZus{}qubits} \\PY{o}{\\PYZhy{}} \\PY{l+m+mi}{1}\\PY{p}{)}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Initial moment of Y gates, conditioned on initial state}\n", + " \\PY{n}{initial\\PYZus{}operations} \\PY{o}{=} \\PY{n}{cirq}\\PY{o}{.}\\PY{n}{Moment}\\PY{p}{(}\\PY{p}{[}\\PY{n}{cirq}\\PY{o}{.}\\PY{n}{Y}\\PY{p}{(}\\PY{n}{qubit}\\PY{p}{)} \\PY{o}{*}\\PY{o}{*} \\PY{n}{initial\\PYZus{}state}\\PY{p}{[}\\PY{n}{index}\\PY{p}{]} \\PY{k}{for} \\PY{n}{index}\\PY{p}{,} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{n+nb}{enumerate}\\PY{p}{(}\\PY{n}{qubits}\\PY{p}{)}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} First component of U cycle, a moment of XZ gates.}\n", + " \\PY{n}{sequence\\PYZus{}operations} \\PY{o}{=} \\PY{p}{[}\\PY{p}{]}\n", + " \\PY{k}{for} \\PY{n}{index}\\PY{p}{,} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{n+nb}{enumerate}\\PY{p}{(}\\PY{n}{qubits}\\PY{p}{)}\\PY{p}{:}\n", + " \\PY{n}{sequence\\PYZus{}operations}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{cirq}\\PY{o}{.}\\PY{n}{PhasedXZGate}\\PY{p}{(}\n", + " \\PY{n}{x\\PYZus{}exponent}\\PY{o}{=}\\PY{n}{g\\PYZus{}value}\\PY{p}{,} \\PY{n}{axis\\PYZus{}phase\\PYZus{}exponent}\\PY{o}{=}\\PY{l+m+mf}{0.0}\\PY{p}{,}\n", + " \\PY{n}{z\\PYZus{}exponent}\\PY{o}{=}\\PY{n}{local\\PYZus{}fields}\\PY{p}{[}\\PY{n}{index}\\PY{p}{]}\\PY{p}{)}\\PY{p}{(}\\PY{n}{qubit}\\PY{p}{)}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Initialize U cycle}\n", + " \\PY{n}{u\\PYZus{}cycle} \\PY{o}{=} \\PY{p}{[}\\PY{n}{cirq}\\PY{o}{.}\\PY{n}{Moment}\\PY{p}{(}\\PY{n}{sequence\\PYZus{}operations}\\PY{p}{)}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Second and third components of U cycle, a chain of 2\\PYZhy{}qubit PhasedFSim gates}\n", + " \\PY{c+c1}{\\PYZsh{} The first component is all the 2\\PYZhy{}qubit PhasedFSim gates starting on even qubits}\n", + " \\PY{c+c1}{\\PYZsh{} The second component is the 2\\PYZhy{}qubit gates starting on odd qubits}\n", + " \\PY{n}{operation\\PYZus{}list}\\PY{p}{,} \\PY{n}{other\\PYZus{}operation\\PYZus{}list} \\PY{o}{=} \\PY{p}{[}\\PY{p}{]}\\PY{p}{,}\\PY{p}{[}\\PY{p}{]}\n", + " \\PY{n}{previous\\PYZus{}qubit}\\PY{p}{,} \\PY{n}{previous\\PYZus{}index} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,} \\PY{k+kc}{None}\n", + " \\PY{k}{for} \\PY{n}{index}\\PY{p}{,} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{n+nb}{enumerate}\\PY{p}{(}\\PY{n}{qubits}\\PY{p}{)}\\PY{p}{:}\n", + " \\PY{k}{if} \\PY{n}{previous\\PYZus{}qubit} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n", + " \\PY{n}{previous\\PYZus{}qubit}\\PY{p}{,} \\PY{n}{previous\\PYZus{}index} \\PY{o}{=} \\PY{n}{qubit}\\PY{p}{,} \\PY{n}{index}\n", + " \\PY{k}{continue}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Add an fsim gate}\n", + " \\PY{n}{coupling\\PYZus{}gate} \\PY{o}{=} \\PY{n}{cirq}\\PY{o}{.}\\PY{n}{ops}\\PY{o}{.}\\PY{n}{PhasedFSimGate}\\PY{p}{(}\n", + " \\PY{n}{theta}\\PY{o}{=}\\PY{n}{thetas}\\PY{p}{[}\\PY{n}{previous\\PYZus{}index}\\PY{p}{]}\\PY{p}{,}\n", + " \\PY{n}{zeta}\\PY{o}{=}\\PY{n}{zetas}\\PY{p}{[}\\PY{n}{previous\\PYZus{}index}\\PY{p}{]}\\PY{p}{,}\n", + " \\PY{n}{chi}\\PY{o}{=}\\PY{n}{chis}\\PY{p}{[}\\PY{n}{previous\\PYZus{}index}\\PY{p}{]}\\PY{p}{,}\n", + " \\PY{n}{gamma}\\PY{o}{=}\\PY{n}{gammas}\\PY{p}{[}\\PY{n}{previous\\PYZus{}index}\\PY{p}{]}\\PY{p}{,}\n", + " \\PY{n}{phi}\\PY{o}{=}\\PY{n}{phis}\\PY{p}{[}\\PY{n}{previous\\PYZus{}index}\\PY{p}{]}\n", + " \\PY{p}{)}\n", + " \\PY{n}{operation\\PYZus{}list}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{coupling\\PYZus{}gate}\\PY{o}{.}\\PY{n}{on}\\PY{p}{(}\\PY{n}{previous\\PYZus{}qubit}\\PY{p}{,} \\PY{n}{qubit}\\PY{p}{)}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Swap the operation lists, to avoid two\\PYZhy{}qubit gate overlap}\n", + " \\PY{n}{previous\\PYZus{}qubit}\\PY{p}{,} \\PY{n}{previous\\PYZus{}index} \\PY{o}{=} \\PY{n}{qubit}\\PY{p}{,} \\PY{n}{index}\n", + " \\PY{n}{operation\\PYZus{}list}\\PY{p}{,} \\PY{n}{other\\PYZus{}operation\\PYZus{}list} \\PY{o}{=} \\PY{n}{other\\PYZus{}operation\\PYZus{}list}\\PY{p}{,} \\PY{n}{operation\\PYZus{}list}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Add the two components into the U cycle}\n", + " \\PY{n}{u\\PYZus{}cycle}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{cirq}\\PY{o}{.}\\PY{n}{Moment}\\PY{p}{(}\\PY{n}{operation\\PYZus{}list}\\PY{p}{)}\\PY{p}{)}\n", + " \\PY{n}{u\\PYZus{}cycle}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{cirq}\\PY{o}{.}\\PY{n}{Moment}\\PY{p}{(}\\PY{n}{other\\PYZus{}operation\\PYZus{}list}\\PY{p}{)}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Prepare a list of circuits, with n=0,1,2,3 ... cycles many cycles}\n", + " \\PY{n}{circuit\\PYZus{}list} \\PY{o}{=} \\PY{p}{[}\\PY{p}{]}\n", + " \\PY{n}{total\\PYZus{}circuit} \\PY{o}{=} \\PY{n}{cirq}\\PY{o}{.}\\PY{n}{Circuit}\\PY{p}{(}\\PY{n}{initial\\PYZus{}operations}\\PY{p}{)}\n", + " \\PY{n}{circuit\\PYZus{}list}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{total\\PYZus{}circuit}\\PY{o}{.}\\PY{n}{copy}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)}\n", + " \\PY{k}{for} \\PY{n}{c} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{cycles}\\PY{p}{)}\\PY{p}{:}\n", + " \\PY{k}{for} \\PY{n}{m} \\PY{o+ow}{in} \\PY{n}{u\\PYZus{}cycle}\\PY{p}{:}\n", + " \\PY{n}{total\\PYZus{}circuit}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{m}\\PY{p}{)}\n", + " \\PY{n}{circuit\\PYZus{}list}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{total\\PYZus{}circuit}\\PY{o}{.}\\PY{n}{copy}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)}\n", + "\n", + " \\PY{k}{return} \\PY{n}{circuit\\PYZus{}list}\n", + "\\end{Verbatim}\n" + ], + "text/plain": [ + "def symbolic_dtc_circuit_list(\n", + " qubits: Sequence[cirq.Qid],\n", + " cycles: int\n", + " ) -> List[cirq.Circuit]:\n", + "\n", + " \"\"\" Create a list of symbolically parameterized dtc circuits, with increasing cycles\n", + " Args:\n", + " qubits: ordered sequence of available qubits, which are connected in a chain\n", + " cycles: maximum number of cycles to generate up to\n", + " Returns:\n", + " list of circuits with `0, 1, 2, ... cycles` many cycles\n", + " \"\"\"\n", + "\n", + " num_qubits = len(qubits)\n", + "\n", + " # Symbol for g\n", + " g_value = sp.Symbol('g')\n", + "\n", + " # Symbols for random variance (h) and initial state, one per qubit\n", + " local_fields = sp.symbols('local_field_:' + str(num_qubits))\n", + " initial_state = sp.symbols('initial_state_:' + str(num_qubits))\n", + "\n", + " # Symbols used for PhasedFsimGate, one for every qubit pair in the chain\n", + " thetas = sp.symbols('theta_:' + str(num_qubits - 1))\n", + " zetas = sp.symbols('zeta_:' + str(num_qubits - 1))\n", + " chis = sp.symbols('chi_:' + str(num_qubits - 1))\n", + " gammas = sp.symbols('gamma_:' + str(num_qubits - 1))\n", + " phis = sp.symbols('phi_:' + str(num_qubits - 1))\n", + "\n", + " # Initial moment of Y gates, conditioned on initial state\n", + " initial_operations = cirq.Moment([cirq.Y(qubit) ** initial_state[index] for index, qubit in enumerate(qubits)])\n", + "\n", + " # First component of U cycle, a moment of XZ gates.\n", + " sequence_operations = []\n", + " for index, qubit in enumerate(qubits):\n", + " sequence_operations.append(cirq.PhasedXZGate(\n", + " x_exponent=g_value, axis_phase_exponent=0.0,\n", + " z_exponent=local_fields[index])(qubit))\n", + "\n", + " # Initialize U cycle\n", + " u_cycle = [cirq.Moment(sequence_operations)]\n", + "\n", + " # Second and third components of U cycle, a chain of 2-qubit PhasedFSim gates\n", + " # The first component is all the 2-qubit PhasedFSim gates starting on even qubits\n", + " # The second component is the 2-qubit gates starting on odd qubits\n", + " operation_list, other_operation_list = [],[]\n", + " previous_qubit, previous_index = None, None\n", + " for index, qubit in enumerate(qubits):\n", + " if previous_qubit is None:\n", + " previous_qubit, previous_index = qubit, index\n", + " continue\n", + "\n", + " # Add an fsim gate\n", + " coupling_gate = cirq.ops.PhasedFSimGate(\n", + " theta=thetas[previous_index],\n", + " zeta=zetas[previous_index],\n", + " chi=chis[previous_index],\n", + " gamma=gammas[previous_index],\n", + " phi=phis[previous_index]\n", + " )\n", + " operation_list.append(coupling_gate.on(previous_qubit, qubit))\n", + "\n", + " # Swap the operation lists, to avoid two-qubit gate overlap\n", + " previous_qubit, previous_index = qubit, index\n", + " operation_list, other_operation_list = other_operation_list, operation_list\n", + "\n", + " # Add the two components into the U cycle\n", + " u_cycle.append(cirq.Moment(operation_list))\n", + " u_cycle.append(cirq.Moment(other_operation_list))\n", + "\n", + " # Prepare a list of circuits, with n=0,1,2,3 ... cycles many cycles\n", + " circuit_list = []\n", + " total_circuit = cirq.Circuit(initial_operations)\n", + " circuit_list.append(total_circuit.copy())\n", + " for c in range(cycles):\n", + " for m in u_cycle:\n", + " total_circuit.append(m)\n", + " circuit_list.append(total_circuit.copy())\n", + "\n", + " return circuit_list" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import inspect\n", + "from IPython.display import Code\n", + "Code(inspect.getsource(time_crystals.symbolic_dtc_circuit_list), language='python')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e13de6da1fc6" + }, + "source": [ + "The construction of each circuit is surprisingly succinct. \n", + "\n", + "The circuit expects the quantum computer to be in the all-zeros state, and starts with a sequence of `cirq.Y` gates conditioned on the provided `initial state` parameter, after initializing the necessary symbolic variables. \n", + "\n", + "Each $U$-cycle consists of three moments. First, a moment of `cirq.PhasedXZGate`s, with one for each qubit. Each `cirq.PhasedXZGate` takes the control parameter `g` as its X-exponent, and the random potentials necessary for many-body localization provided by `local_fields` for its Y-exponent.\n", + "\n", + "The second and third moments both cause the oscillation behavior and compensate for the first disorder moment. The qubits are connected in a chain, and each qubit pair connection in that chain is coupled with a `cirq.PhasedFSimGate` that uses the parameters `[theta, zetas, chi, gamma, phi]`. To keep gates from overlapping on the same qubit, this chain of gates is split into the second and third moments, such that no two gates share a qubit within each moment. \n", + "\n", + "Finally, `symbolic_dtc_circuit_list()` builds and returns a list of circuits with $0,1,2,..., cycles$ many $U$-cycles in them. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "8397a5eab5f7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "circuit of length 1\n", + "\n", + "(0, 0): ───Y^initial_state_0───\n", + "\n", + "(0, 1): ───Y^initial_state_1───\n", + "\n", + "(0, 2): ───Y^initial_state_2───\n", + "\n", + "(0, 3): ───Y^initial_state_3───\n", + "\n", + "circuit of length 4\n", + "\n", + "(0, 0): ───Y^initial_state_0───PhXZ(a=0,x=g,z=local_field_0)────────────────────────────────────────────────────PhFSim(theta_0, zeta_0, chi_0, gamma_0, phi_0)───\n", + " │\n", + "(0, 1): ───Y^initial_state_1───PhXZ(a=0,x=g,z=local_field_1)───PhFSim(theta_1, zeta_1, chi_1, gamma_1, phi_1)───PhFSim(theta_0, zeta_0, chi_0, gamma_0, phi_0)───\n", + " │\n", + "(0, 2): ───Y^initial_state_2───PhXZ(a=0,x=g,z=local_field_2)───PhFSim(theta_1, zeta_1, chi_1, gamma_1, phi_1)───PhFSim(theta_2, zeta_2, chi_2, gamma_2, phi_2)───\n", + " │\n", + "(0, 3): ───Y^initial_state_3───PhXZ(a=0,x=g,z=local_field_3)────────────────────────────────────────────────────PhFSim(theta_2, zeta_2, chi_2, gamma_2, phi_2)───\n", + "\n", + "circuit of length 7\n", + "\n", + "(0, 0): ───Y^initial_state_0───PhXZ(a=0,x=g,z=local_field_0)────────────────────────────────────────────────────PhFSim(theta_0, zeta_0, chi_0, gamma_0, phi_0)───PhXZ(a=0,x=g,z=local_field_0)────────────────────────────────────────────────────PhFSim(theta_0, zeta_0, chi_0, gamma_0, phi_0)───\n", + " │ │\n", + "(0, 1): ───Y^initial_state_1───PhXZ(a=0,x=g,z=local_field_1)───PhFSim(theta_1, zeta_1, chi_1, gamma_1, phi_1)───PhFSim(theta_0, zeta_0, chi_0, gamma_0, phi_0)───PhXZ(a=0,x=g,z=local_field_1)───PhFSim(theta_1, zeta_1, chi_1, gamma_1, phi_1)───PhFSim(theta_0, zeta_0, chi_0, gamma_0, phi_0)───\n", + " │ │\n", + "(0, 2): ───Y^initial_state_2───PhXZ(a=0,x=g,z=local_field_2)───PhFSim(theta_1, zeta_1, chi_1, gamma_1, phi_1)───PhFSim(theta_2, zeta_2, chi_2, gamma_2, phi_2)───PhXZ(a=0,x=g,z=local_field_2)───PhFSim(theta_1, zeta_1, chi_1, gamma_1, phi_1)───PhFSim(theta_2, zeta_2, chi_2, gamma_2, phi_2)───\n", + " │ │\n", + "(0, 3): ───Y^initial_state_3───PhXZ(a=0,x=g,z=local_field_3)────────────────────────────────────────────────────PhFSim(theta_2, zeta_2, chi_2, gamma_2, phi_2)───PhXZ(a=0,x=g,z=local_field_3)────────────────────────────────────────────────────PhFSim(theta_2, zeta_2, chi_2, gamma_2, phi_2)───\n" + ] + } + ], + "source": [ + "qubits = [cirq.GridQubit(0,i) for i in range(4)]\n", + "circuit_list = time_crystals.symbolic_dtc_circuit_list(qubits, 2)\n", + "for circuit in circuit_list: \n", + " print('\\ncircuit of length ' + str(len(circuit)) + \"\\n\")\n", + " print(circuit)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3ba0e882df83" + }, + "source": [ + "After the initial line of `cirq.Y` gates, each consecutive circuit in the list has an additional cycle of `cirq.PhasedXZGate`s, followed by the chain of `cirq.PhasedFSimGate`s on alternating qubit pairs. Each cycle of three moments becomes one time step in the later analysis of stable oscillations over time. \n", + "\n", + "The next step is to perform experiments to collect evidence of the time-crystalline behavior of the quantum state's polarizations. See the [Time Crystal Data Collection](time_crystal_data_collection.ipynb) notebook for the experiments, and the [Time Crystal Data Analysis](time_crystal_data_analysis.ipynb) notebook for the graphed data and results. " + ] + } + ], + "metadata": { + "colab": { + "name": "time_crystal_circuit_generation.ipynb", + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/time_crystals/time_crystal_data_analysis.ipynb b/docs/time_crystals/time_crystal_data_analysis.ipynb new file mode 100644 index 00000000..d64a2739 --- /dev/null +++ b/docs/time_crystals/time_crystal_data_analysis.ipynb @@ -0,0 +1,565 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "10a61b4d73a5" + }, + "outputs": [], + "source": [ + "# Copyright 2021 Google\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "05b86673e72c" + }, + "source": [ + "# Time Crystal Data Analysis\n", + "\n", + "This notebook acts as a script to plot Figures 2d through 3d in the paper: Observation of Time-Crystalline Eigenstate Order on a Quantum Processor ([Nature](https://www.nature.com/articles/s41586-021-04257-w)). It uses the data collected and saved by the notebook [Time Crystal Data Collection](time_crystal_data_collection.ipynb). \n", + "\n", + "Each of the five figures serve to exemplify the time-crystalline nature of the Many Body Local Discrete Time Crystal system being emulated by the experiment's circuit. This occurs as consistent and stable oscillation in spite of variances in `local_fields` and `initial_state`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f53722cb0850" + }, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EkaIfQIIkXjE" + }, + "outputs": [], + "source": [ + "!pip install cirq --pre --quiet\n", + "try:\n", + " import recirq\n", + "except ImportError:\n", + " !pip install --quiet git+https://github.com/quantumlib/ReCirq" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "5tTJoyYMk0bK" + }, + "outputs": [], + "source": [ + "import cirq\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import recirq.time_crystals as time_crystals" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "b0258eae9f52" + }, + "outputs": [], + "source": [ + "# directory to pull data from\n", + "base_dir = time_crystals.DEFAULT_BASE_DIR" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ec6074bae38d" + }, + "source": [ + "## Figure 2d\n", + "Figure 2d demonstrates polarizations over time ($U$-cycles), comparing different values of the constant `g`.\n", + "\n", + "Two datasets were generated with values of `g` as $0.6$ and $0.94$. Each of which are of shape `(num_cycles + 1, num_qubits)`. Plot each matrix as an image, on the same color scale. \n", + "\n", + "At $g = 0.6$, every qubit quickly loses polarization and the measurements for each qubit after the first couple cycles is close to completely random. This case matches the expectation of rapid decay to maximum entropy for systems that are not many-body local. \n", + "\n", + "At $g = 0.94$, every qubit's polarization is nearly maximal and switches consistently, every two cycles, revealing the oscillation typical to an MBL-DTC system. There is slight variance, indicated by the streaks of green (0.75) visible among the yellow (1.0), but this variance doesn't interfere with or prevent the stability of the oscillations. At this value, the oscillations are mostly unaffected by any introduced randomness.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "3b21e7084d0a" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# retrieve data\n", + "filename = f'{base_dir}/2d.json'\n", + "average_polarizations = cirq.read_json(filename)\n", + "\n", + "# prepare subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8), sharey=True, sharex=True)\n", + "\n", + "# prepare labels\n", + "g_labels = ['0.6', '0.94']\n", + "\n", + "for g, axis, polarizations in zip(g_labels, axes, average_polarizations):\n", + "\n", + " # switch axes\n", + " polarizations = np.asarray(polarizations).transpose()\n", + " \n", + " # plot polarizations as an image\n", + " artist = axis.imshow(polarizations, aspect = 2.0, vmin=-1.0, vmax=1.0)\n", + "\n", + " # add labels and colorbar and title\n", + " axis.set_xlabel('Cycles')\n", + " axis.set_ylabel('Qubits')\n", + " axis.set_title('Local Polarizations, g = ' + str(g))\n", + " \n", + "# add colorbar\n", + "fig.colorbar(artist, ax=axes.ravel().tolist())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9140773a6998" + }, + "source": [ + "## Figure 3a\n", + "Figure 3a demonstrates the interaction between `phis` and `initial_state`, when each are random or fixed. \n", + "\n", + "Six datasets were generated with random and fixed of `phis`, and with one of three different `initial_state`s, autocorrelated, and averaged over all qubit sites. The figure plots each configuration's polarizations over time (cycles). \n", + "\n", + "With a randomly generated, disordered `phi`, all initial states maintain relatively maximal average polarization values, which oscillate with a period of two cycles. After the first ten cycles, the polarizations are stable around about $\\pm 0.75$. \n", + "\n", + "With a fixed phi of $-0.4$, the same behavior is observed for the polarized (all zeros) and disturbed polarized (all zeros except index 11) initial states. However, the random initial state's average polarizations continue to decay until around cycle 20, after which they remains around $\\pm 0.25$. In this case, the oscillation is maintained, but is much less well defined. In a real system with noise and decoherence, the oscillation decays and disappears much more quickly than in the other initial_states' cases, as shown in the [paper](https://www.nature.com/articles/s41586-021-04257-w). " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "f8b3f3cdd09f" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# retrieve data\n", + "filename = f'{base_dir}/3a.json'\n", + "average_polarizations = cirq.read_json(filename)\n", + "\n", + "# prepare subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=2, sharey=True, figsize=(20, 6))\n", + "\n", + "# prepare labels\n", + "phi_labels = ['phi_i in [-1.5 * pi, -0.5 * pi]', 'phi_i = -0.4']\n", + "initial_state_labels = ['neel', 'polarized', 'random']\n", + "\n", + "# plot and label\n", + "for axis, cycles_by_initial_state in zip(axes, average_polarizations):\n", + " for initial_state_label, cycles in zip(initial_state_labels, cycles_by_initial_state):\n", + " axis.plot(cycles, marker='o', label=initial_state_label)\n", + "\n", + "# add phi labels and legend to each subplot\n", + "for phi_label, axis in zip(phi_labels, axes): \n", + " axis.text(0.99, 0.99, phi_label, horizontalalignment='right', verticalalignment='top', transform=axis.transAxes)\n", + " axis.legend(loc='lower right')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cc39a3ebea16" + }, + "source": [ + "## Figure 3b\n", + "Figure 3b reinforces the observation of Figure 3a, comparing fixed and random `phis` over many different random `initial_state`s. \n", + "\n", + "$40$ datasets were generated as the product of $20$ initial states and two (fixed and random) options for phis. The figure plots a histogram of the **absolute value of** the polarizations, averaged over all qubits and over cycles $30$ and $31$. \n", + "\n", + "As noted in Figure 3a, the oscillations have likely stabilized by cycles $30$ and $31$. The average absolute value polarizations for the random phis is consistently high, around $0.75$. For the fixed phis, they are consistently lower, most likely to be around $0.45$. This confirms that the behavior observed in Figure 3a is also present over many different random initial states, which indicates that randomness in the phis parameter improves the strength of the oscillations. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "61b7d2021ecd" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# retrieve data\n", + "filename = f'{base_dir}/3b.json'\n", + "average_polarizations = cirq.read_json(filename)\n", + "\n", + "# prepare subplots\n", + "fig, axis = plt.subplots(nrows=1, ncols=1, figsize=(20,10))\n", + "\n", + "# prepare labels\n", + "phi_labels = ['phi_i in [-1.5 * pi, -0.5 * pi]', 'phi_i = -0.4']\n", + "\n", + "# plot in histograms\n", + "for phi_label, average_polarizations in zip(phi_labels, average_polarizations):\n", + " axis.hist(average_polarizations, label=phi_label, bins=np.linspace(0.15, 1.0, 20))\n", + " \n", + "# add legend and show\n", + "fig.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9a7297003589" + }, + "source": [ + "## Figure 3c\n", + "Figure 3c analyzes the effect of a disturbed `initial_state`'s interaction with `phi`. \n", + "\n", + "Four datasets, the product of two phi options (random and fixed) and two initial state options (polarized and polarized but disturbed at qubit 11) are plotted separately for cycles $30$ through $60$ and qubits $11$ through $14$. \n", + "\n", + "For the random phis, the disturbed initial state, by cycle $30$, seems not to have interfered the with the oscillations' strength. For both the disturbed qubit, $11$, and its neighboring qubits, the oscillations have maintained high polarity. \n", + "\n", + "For the fixed phis, the disturbed initial state polarizations have deteriorated significantly by cycle $30$, relative to those of the polarized state, and stay poorly polarized through to cycle $60$. This reflects the observations in Figures 3a and 3b. The nearby qubits, $12$ through $14$, also show deterioration, but meaningfully less. The nearby qubits seem to be interfered with by the adjacent disturbed qubit, but this interference loses its influence with distance. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "675152b85463" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# retrieve data\n", + "filename = f'{base_dir}/3c.json'\n", + "average_polarizations = cirq.read_json(filename)\n", + "\n", + "# prepare initial qubits as legend label strings\n", + "num_qubits = 16\n", + "disturb_qubit = 11\n", + "polarized_initial_state = [0]*num_qubits\n", + "disturbed_polarized_initial_state = list(polarized_initial_state)\n", + "disturbed_polarized_initial_state[disturb_qubit] = 1\n", + "initial_states = [''.join(map(str,polarized_initial_state)), ''.join(map(str,disturbed_polarized_initial_state))]\n", + "\n", + "# prepare subplots\n", + "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20,8), sharey=True, sharex=True)\n", + "\n", + "# plot according to phi and qubit options\n", + "for phi_axes, data_by_initial_state in zip(axes, average_polarizations): \n", + " for initial_state, data in zip(initial_states, data_by_initial_state):\n", + " for qubit_index, axis in enumerate(phi_axes):\n", + " # consider only qubit index subset, swap indices, and plot\n", + " points = np.asarray(data)[:, qubit_index]\n", + " axis.plot(range(30, 61, 1), points, marker='s', label=initial_state)\n", + "\n", + "# prepare labels\n", + "phi_labels = ['phi_i in [-1.5 * pi, -0.5 * pi]', 'phi_i = -0.4']\n", + "\n", + "# add phi labels\n", + "for phi_label, axis in zip(phi_labels, [axes[0][3], axes[1][3]]): \n", + " fig.text(0.99, 0.01, phi_label, horizontalalignment='right', verticalalignment='bottom', transform=axis.transAxes)\n", + "\n", + "# add legend\n", + "handles, labels = axis.get_legend_handles_labels()\n", + "labels_dict = dict(zip(labels, handles))\n", + "fig.legend(labels_dict.values(), labels_dict.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e8f5e692c13a" + }, + "source": [ + "## Figure 3d.a\n", + "Figure 3d.a again compares random and fixed phis, this time for the signal ratio between the polarized and disturbed polarized initial states. \n", + "\n", + "Four datasets, the product of two phi options (random and fixed) for two initial state options (polarized and disturbed-at-qubit-11 polarized) are plotted in two images, one for each phi option. Each image is the signal ratio between the data for the polarized and disturbed polarized states, defined as: \n", + "$$\\zeta = \\frac{|\\zeta_1 - \\zeta_2|}{|\\zeta_1| + |\\zeta_2|}$$\n", + "\n", + "This serves to demonstrate the influence of the disturbed qubit on its own and its neighbors' polarization strengths. \n", + "\n", + "For the random phis, the disturbed qubit's polarizations maintain strength and is consistently distinct and inverted relative to the the polarizations of the remaining zero-initialized qubits. For the fixed phis, the signal ratio of the disturbed qubits seems to leak out and influence the neighboring qubits, eventually diffusing to affect the polarization ratio of almost every qubit. This fixed phis case is much less capable of consistently maintaining the expected polarizations, relative to the random phis case. With randomness in the phis, the system is able to maintain it's distinct oscillatory behavior much more consistently. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "556e51d28649" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# retrieve data\n", + "filename = f'{base_dir}/3d.json'\n", + "average_polarizations = np.asarray(cirq.read_json(filename))\n", + "\n", + "# number of cycles used in generation\n", + "num_cycles = 100\n", + "\n", + "# the size of window to average cycles over\n", + "cycle_window = 10\n", + "\n", + "# prepare subplots\n", + "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8), sharey=True, sharex=True)\n", + "\n", + "# plot according to phi option\n", + "for average_polarizations_for_phi, axis in zip(average_polarizations, axes):\n", + "\n", + " # compare the two polarization signals with signal_ratio\n", + " polarization_ratio = time_crystals.signal_ratio(*average_polarizations_for_phi)\n", + "\n", + " # define window indices\n", + " subdivisions = np.arange(cycle_window, num_cycles + 1, cycle_window)\n", + "\n", + " # divide into cycles_window width sections and discard a final window that isn't full size\n", + " polarization_ratio_windows = np.split(polarization_ratio, subdivisions, axis=0)\n", + " if not polarization_ratio_windows[0].shape == polarization_ratio_windows[-1].shape: \n", + " polarization_ratio_windows = polarization_ratio_windows[:-1]\n", + " \n", + " # stack windows into a new axis\n", + " polarization_ratio_by_windows = np.stack(polarization_ratio_windows, axis=0)\n", + " \n", + " # average over cycle windows\n", + " average_polarization_ratio_by_windows = np.mean(polarization_ratio_by_windows, axis=1)\n", + "\n", + " # repeat along the cycles axis, to return to original shape\n", + " average_polarization_ratio = np.repeat(average_polarization_ratio_by_windows, cycle_window, axis=0)\n", + "\n", + " # plot polarizations as an image\n", + " artist = axis.imshow(average_polarization_ratio.transpose(), aspect = 2.0, vmin=0, vmax=1.0)\n", + "\n", + " # add labels and colorbar and title\n", + " axis.set_xlabel('Cycles')\n", + " axis.set_ylabel('Qubits')\n", + " axis.set_title('Polarization Ratio')\n", + " \n", + "# add colorbar\n", + "fig.colorbar(artist, ax=axes.ravel().tolist())\n", + "\n", + "# set phi labels\n", + "phi_labels = ['phi_i in [-1.5 * pi, -0.5 * pi]', 'phi_i = -0.4']\n", + "\n", + "# add phi labels\n", + "for phi_label, axis in zip(phi_labels, axes): \n", + " fig.text(0.01, 0.99, phi_label, horizontalalignment='left', verticalalignment='top', transform=axis.transAxes, color='white')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ce491a17b2fa" + }, + "source": [ + "## Figure 3d.b\n", + "Figure 3d.b is a vertical slice of Figure 3d.a, over a limited number of cycles. \n", + "\n", + "The signal ratio, averaged over cycles $51$ through $60$ is plotted for each qubit. \n", + "\n", + "While the disturbed qubit $11$ maintains a high signal ratio in both cases, the neighboring qubits in the random phis case are not significantly affected. In the fixed phis case, the other qubits have higher signal ratio the closer they are to the disturbed qubit. This reflects the results seen in Figure 3d.a, again indicating that randomness in phis helps the system oscillate consistently. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "d78e506462f8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# prepare subplots\n", + "fig, axis = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))\n", + "\n", + "# plot according to phi option\n", + "for polarizations, phi_label in zip(average_polarizations, phi_labels):\n", + "\n", + " # compare the two polarization signals with signal_ratio\n", + " polarization_ratio = time_crystals.signal_ratio(*polarizations)\n", + "\n", + " # only consider one window's worth of cycles\n", + " restricted_polarization_ratio = polarization_ratio[51:61, :]\n", + "\n", + " # average over cycles\n", + " average_polarization_ratio = np.mean(restricted_polarization_ratio, axis=0)\n", + " \n", + " # plot line\n", + " axis.plot(average_polarization_ratio, label=phi_label)\n", + "\n", + "# add legend\n", + "fig.legend(loc='upper center')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a0c26b92c388" + }, + "source": [ + "## Further Reading\n", + "For more detail and analysis on these experiments, including their results on actual hardware, refer back to the original paper: Observation of Time-Crystalline Eigenstate Order on a Quantum Processor ([Nature](https://www.nature.com/articles/s41586-021-04257-w)). Also provided in the paper are many other experiments and figures which are uniquely informative when run in a noisy quantum hardware environment" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "time_crystal_data_analysis.ipynb", + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/time_crystals/time_crystal_data_collection.ipynb b/docs/time_crystals/time_crystal_data_collection.ipynb new file mode 100644 index 00000000..3d2e3dda --- /dev/null +++ b/docs/time_crystals/time_crystal_data_collection.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "10a61b4d73a5" + }, + "outputs": [], + "source": [ + "# Copyright 2021 Google\n", + "#\n", + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4bdef84a5ef5" + }, + "source": [ + "# Time Crystal Data Collection\n", + "\n", + "This notebook acts as a script to run the experiments and save the data associated with Figures 2d through 3d in the paper: Observation of Time-Crystalline Eigenstate Order on a Quantum Processor ([Nature](https://www.nature.com/articles/s41586-021-04257-w)). \n", + "\n", + "Each of the five experiments are built using `recirq.time_crystals.dtctasks.CompareDTCTask`, which defines the experiment parameters that are to be compared. `CompareDTCTask.dtctasks()` then creates `recirq.time_crystals.dtctasks.DTCTask`s with all of the requisite parameters for an instance of the experiment. \n", + "\n", + "A `DTCTask` has the following attributes and default values: \n", + "- `qubits`: Sequence of qubits connected in a chain. Defaults to a line of $16$ connected `cirq.GridQubits`. \n", + "- `disorder_instances`: Number of disorder instances to simulate and average resulting polarizations over. Defaults to $36$.\n", + "- `g`: Control parameter which influences how well the system is able to maintain time-crystalline behavior. Used in `cirq.PhasedXZGate`s in the circuit. Defaults to $0.94$.\n", + "- `initial_state` or `initial_states`: Only one should be supplied. Defines the input state of the system and is implemented with `cirq.Y` gates in the circuit. If `initial_state` is supplied, it will be repeated and used for every disorder instance. Defaults to a different random bit string for each disorder instance.\n", + "- `local_fields`: Random potentials critical to enable many-body local behavior. Used in `cirq.PhasedXZGate`s in the circuit. Defaults to uniformly selected float values between $-1.0$ and $1.0$.\n", + "- `thetas`, `zetas` and `chis`: Parameters used in the FSim gates in the circuit. Defaults to zero in all cases.\n", + "- `phis`: Parameter used in the FSim gates in the circuit. Affects the stability of the time-crystalline behavior. Defaults to uniformly selected float values between $-1.5*\\pi$ and $-0.5*\\pi$. \n", + "- `gammas`: Parameter used in the FSim gates in the circuit. `Gammas` and `phis` are interdependent such that they satisfy $gammas = -2*phis$; One is set according to this equation if the other is supplied, otherwise use the default `phis` and calculate `gammas` from that.\n", + "\n", + "`CompareDTCTask.dtctasks()` takes the product over the values of the `options_dict` supplied to the `CompareDTCTask` object, and passes those values as parameters to the `__init__()` function for `DTCTask`. Any parameter not supplied takes it's default value, meaning only the different parameter options that are to be compared need to be supplied to `CompareDTCTask`'s `options_dict`. This also means that supplying one parameter option for a parameter fixes that parameter option across all cases, instead of using the defaults, which may randomly generate values for each different `DTCTask`. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f53722cb0850" + }, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EkaIfQIIkXjE" + }, + "outputs": [], + "source": [ + "!pip install cirq --pre --quiet\n", + "try:\n", + " import recirq\n", + "except ImportError:\n", + " !pip install --quiet git+https://github.com/quantumlib/ReCirq" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "5tTJoyYMk0bK" + }, + "outputs": [], + "source": [ + "import cirq\n", + "import itertools\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import recirq.time_crystals as time_crystals" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c1bae56e4403" + }, + "source": [ + "## Variables used in all experiments\n", + "Defines the qubits, number of DTC cycles (time steps) to evaluate, and the `base_dir` to store experiment results in." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "D8ntbOBa4MKZ" + }, + "outputs": [], + "source": [ + "# define the qubits to use\n", + "qubit_locations = [(3, 9), (3, 8), (3, 7), (4, 7), (4, 8), (5, 8), (5, 7), (5, 6), (6, 6), (6, 5), (7, 5), (8, 5),\n", + " (8, 4), (8, 3), (7, 3), (6, 3)]\n", + "\n", + "qubits = [cirq.GridQubit(*idx) for idx in qubit_locations]\n", + "num_qubits = len(qubits)\n", + "\n", + "# number of cycles to evaluate over\n", + "num_cycles = 100\n", + "\n", + "# directory to store data in\n", + "base_dir = time_crystals.DEFAULT_BASE_DIR" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "17506537dabc" + }, + "source": [ + "## Figure 2d's Experiment\n", + "This experiment considers the constant `g`, which affects the ability for the DTC system to oscillate consistently.\n", + "\n", + "It compares two different values for `g`, $0.6$ and $0.94$, but uses the same disorder instances (randomly selected parameter values) for each of the two values of `g`. \n", + "\n", + "Define this with `options_dict` below, which results takes a product over the values of the dictionary. The result is the following two `recirq.time_crystals.DTCTasks`, with different values of `g`, but using the same `initial_states`, `local_fields`, and `gammas`: \n", + "- `DTCTask(g = 0.6, initial_states = initial_states, local_fields = local_fields, gammas = gammas)`\n", + "- `DTCTask(g = 0.94, initial_states = initial_states, local_fields = local_fields, gammas = gammas)`\n", + "\n", + "These two `DTCTask`s are each simulated over 36 disorder instances, have the polarizations for each qubit calculated, autocorrelated with the initial state, averaged, and finally saved as a json. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "8_rImPnElMGJ" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2min 48s, sys: 84.9 ms, total: 2min 48s\n", + "Wall time: 2min 48s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# number of disorder instances to average over\n", + "disorder_instances = 36\n", + "\n", + "# disorder instances h\n", + "local_fields = np.random.uniform(-1.0, 1.0, (disorder_instances, num_qubits))\n", + "\n", + "# initial states, one for each disorder instance\n", + "initial_states = np.random.choice(2, (disorder_instances, num_qubits))\n", + "\n", + "# gammas for phased FSim gates\n", + "gammas = np.random.uniform(-0.5*np.pi, -1.5*np.pi, (disorder_instances, num_qubits - 1))\n", + "\n", + "# create comparison task\n", + "options_dict = {\n", + " 'g': [0.6, 0.94],\n", + " 'initial_states': [initial_states], \n", + " 'local_fields' : [local_fields],\n", + " 'gammas': [gammas]\n", + "}\n", + "comparedtctask = time_crystals.CompareDTCTask(qubits, num_cycles, disorder_instances, options_dict)\n", + "\n", + "# create polarizations generator\n", + "polarizations_generator = time_crystals.run_comparison_experiment(comparedtctask, autocorrelate=True, take_abs=False)\n", + "\n", + "# collect polarizations by g option\n", + "average_polarizations = np.empty((2, num_cycles+1, num_qubits))\n", + "\n", + "for g_index, disorder_averaged_polarizations in enumerate(polarizations_generator):\n", + " average_polarizations[g_index, :, :] = disorder_averaged_polarizations\n", + "\n", + "# save data in json format\n", + "filename = f'{base_dir}/2d.json'\n", + "with open(filename, 'w+') as f:\n", + " cirq.to_json(average_polarizations, file_or_fn=f)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "116c8af588df" + }, + "source": [ + "## Figure 3a's Experiment\n", + "This experiment compares six options: the product between:\n", + "- Two options for `phis`: Uniformly, randomly selected `phis` between $-1.5\\pi$ and $-0.5\\pi$, and a fixed value of $-0.4$ for all `phis`. \n", + "- Three options for `initial_state`: \n", + " - The polarized state of all zeros: `0000000000000000`\n", + " - The Néel state of alternating zeros and ones: `0101010101010101`\n", + " - A state with randomly selected zeros and ones: `00111000010011001111` (the first $16$ qubits)\n", + "\n", + "It uses the same `local_fields` for all cases, autocorrelates the polarizations relative to the initial states, and averages over $24$ disorder instances. \n", + "\n", + "It also averages the `disorder_averaged_polarizations` over all $16$ qubit states before storing the results." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "c9ef4b74d2ea" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 5min 37s, sys: 140 ms, total: 5min 37s\n", + "Wall time: 5min 37s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# number of disorder instances to average over\n", + "disorder_instances = 24\n", + "\n", + "# disorder instances h\n", + "local_fields = np.random.uniform(-1.0, 1.0, (disorder_instances, num_qubits))\n", + "\n", + "# prepare 3 initial states to compare\n", + "neel_initial_state = np.tile([0,1], num_qubits//2)\n", + "polarized_initial_state = np.full(num_qubits, 0)\n", + "random_initial_state = [0,0,1,1,1,0,0,0,0,1,0,0,1,1,0,0,1,1,1,1][:num_qubits]\n", + "initial_states = [neel_initial_state, polarized_initial_state, random_initial_state]\n", + "\n", + "# prepare random and fixed phis to compare\n", + "disordered_phis = np.random.uniform(-1.5*np.pi, -0.5*np.pi, (disorder_instances, num_qubits - 1))\n", + "fixed_phis = np.full((disorder_instances, num_qubits - 1), -0.4)\n", + "\n", + "# create comparison task\n", + "options_dict = {\n", + " 'local_fields': [local_fields], \n", + " 'initial_state': initial_states,\n", + " 'phis': [disordered_phis, fixed_phis]\n", + "}\n", + "options_order = ['local_fields', 'phis', 'initial_state']\n", + "comparedtctask = time_crystals.CompareDTCTask(qubits, num_cycles, disorder_instances, options_dict, options_order)\n", + "\n", + "# prepare polarizations and indices generators\n", + "polarizations_generator = time_crystals.run_comparison_experiment(comparedtctask, autocorrelate=True, take_abs=False)\n", + "indices_iterator = itertools.product(range(2), range(len(initial_states)))\n", + "\n", + "# collect polarizations averaged over qubit sites by phi and initial state options\n", + "average_polarizations = np.empty((2, len(initial_states), num_cycles+1))\n", + "\n", + "for (phi_index, initial_state_index), disorder_averaged_polarizations in zip(indices_iterator, polarizations_generator):\n", + " # store average over all qubit sites\n", + " average_polarizations[phi_index, initial_state_index, :] = np.mean(disorder_averaged_polarizations, axis=1)\n", + "\n", + "# save data in json format\n", + "filename = f'{base_dir}/3a.json'\n", + "with open(filename, 'w+') as f:\n", + " cirq.to_json(average_polarizations, file_or_fn=f)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a9efcbf86bd4" + }, + "source": [ + "## Figure 3b's Experiment\n", + "This experiment compares $40$ different cases, the product between: \n", + "- Two options for `phis`: Uniformly, randomly selected `phis` and `phis` fixed at $-0.4$.\n", + "- 20 options for `initial_state`: 20 randomly selected bit string initial states.\n", + "\n", + "Again, the same random potentials, `local_fields`, are used in all cases. The resulting polarizations are autocorrelated with the initial states, **have their absolute value taken**, and are averaged over $24$ disorder instances. \n", + "\n", + "This time, store the average over all of the $16$ qubits, but only for cycles $30$ and $31$. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "0f8ed2aed81c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 37min 47s, sys: 925 ms, total: 37min 48s\n", + "Wall time: 37min 48s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# instance counts for disorder and random initial states\n", + "disorder_instances = 24\n", + "initial_state_instances = 20 # this is 500 in the paper\n", + "\n", + "# disorder instances h\n", + "local_fields = np.random.uniform(-1.0, 1.0, (disorder_instances, num_qubits))\n", + "\n", + "# prepare random initial states\n", + "initial_states = np.random.choice(2, (initial_state_instances, num_qubits))\n", + "\n", + "# prepare random and fixed phis to compare\n", + "disordered_phis = np.random.uniform(-1.5*np.pi, -0.5*np.pi, (disorder_instances, num_qubits - 1))\n", + "fixed_phis = np.full((disorder_instances, num_qubits - 1), -0.4)\n", + "\n", + "# create comparison task\n", + "options_dict = {\n", + " 'local_fields': [local_fields],\n", + " 'initial_state': initial_states,\n", + " 'phis': [disordered_phis, fixed_phis]\n", + "}\n", + "options_order = ['local_fields', 'phis', 'initial_state']\n", + "comparedtctask = time_crystals.CompareDTCTask(qubits, num_cycles, disorder_instances, options_dict, options_order)\n", + "\n", + "# prepare polarizations and indices generators\n", + "polarizations_generator = time_crystals.run_comparison_experiment(comparedtctask, autocorrelate=True, take_abs=True)\n", + "indices_iterator = itertools.product(range(2), range(len(initial_states)))\n", + "\n", + "# collect polarizations, averaged over qubit sites and cycles 30 and 31, by phi and initial state options\n", + "average_polarizations = np.empty((2, initial_state_instances))\n", + "\n", + "for (phi_index, initial_state_index), disorder_averaged_polarizations in zip(indices_iterator, polarizations_generator):\n", + " # store average over qubit sites and cycles 30 and 31\n", + " average_polarizations[phi_index, initial_state_index] = np.mean(disorder_averaged_polarizations[30:32, :])\n", + " \n", + "# save data in json format\n", + "filename = f'{base_dir}/3b.json'\n", + "with open(filename, 'w+') as f:\n", + " cirq.to_json(average_polarizations, file_or_fn=f)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "30bde5b12339" + }, + "source": [ + "## Figure 3c's Experiment\n", + "This experiment compares $4$ different cases, the product between: \n", + "- Two options for `phis`: Uniformly, randomly selected `phis` and `phis` fixed at $-0.4$.\n", + "- Two options for `initial_state`: \n", + " - The polarized initial state of all zeros: `0000000000000000`\n", + " - The polarized initial state, but disturbed at qubit index $11$: `0000000000010000`\n", + "\n", + "The same `local_fields`, are used in all cases. The polarizations are **not** autocorrelated, and are averaged over $24$ disorder instances. \n", + "\n", + "Store the polarizations matrix of shape `(num_cycles + 1, num_qubits)` without averaging, but only over cycles $30$ through $60$, and only over qubits $11$ through $14$. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "9e1c8aa67147" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3min 44s, sys: 108 ms, total: 3min 44s\n", + "Wall time: 3min 44s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# number of disorder instances to average over\n", + "disorder_instances = 24\n", + "\n", + "# disorder parameters h\n", + "local_fields = np.random.uniform(-1.0, 1.0, (disorder_instances, num_qubits))\n", + "\n", + "# prepare random and fixed phis to compare\n", + "disordered_phis = np.random.uniform(-1.5*np.pi, -0.5*np.pi, (disorder_instances, num_qubits - 1))\n", + "fixed_phis = np.full((disorder_instances, num_qubits - 1), -0.4)\n", + "\n", + "# prepare two initial states to compare\n", + "polarized_initial_state = [0]*num_qubits\n", + "disturb_qubit = 11\n", + "disturbed_polarized_initial_state = list(polarized_initial_state)\n", + "disturbed_polarized_initial_state[disturb_qubit] = 1\n", + "initial_states = [polarized_initial_state, disturbed_polarized_initial_state]\n", + "\n", + "# create comparison task\n", + "options_dict = {\n", + " 'local_fields': [local_fields],\n", + " 'initial_state': initial_states,\n", + " 'phis': [disordered_phis, fixed_phis]\n", + " \n", + "}\n", + "options_order = ['local_fields', 'phis', 'initial_state']\n", + "comparedtctask = time_crystals.CompareDTCTask(qubits, num_cycles, disorder_instances, options_dict, options_order)\n", + "\n", + "# prepare polarizations and indices generators\n", + "polarizations_generator = time_crystals.run_comparison_experiment(comparedtctask, autocorrelate=False, take_abs=False)\n", + "indices_iterator = itertools.product(range(2), range(len(initial_states)))\n", + "\n", + "# collect polarizations, averaged over cycles 30 through 60 and qubits 11 through 14, by phi and initial state options\n", + "average_polarizations = np.empty((2, 2, 31, 4))\n", + "\n", + "for (phi_index, initial_state_index), disorder_averaged_polarizations in zip(indices_iterator, polarizations_generator):\n", + " # store average over cycles 30 and 31, and qubits 11 through 14\n", + " average_polarizations[phi_index, initial_state_index, :, :] = disorder_averaged_polarizations[30:61, 11:15]\n", + "\n", + "# save data in json format\n", + "filename = f'{base_dir}/3c.json'\n", + "with open(filename, 'w+') as f:\n", + " cirq.to_json(average_polarizations, file_or_fn=f)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cb509ac9594b" + }, + "source": [ + "## Figure 3d's Experiment\n", + "This again experiment compares $4$ different cases, the product between: \n", + "- Two options for `phis`: Uniformly, randomly selected `phis` and `phis` fixed at $-0.4$.\n", + "- Two options for `initial_state`: \n", + " - The polarized initial state of all zeros: `0000000000000000`\n", + " - The polarized initial state, but disturbed at qubit index $11$: `0000000000010000`\n", + "\n", + "However, this experiment differs from 3c's in that, for each of the two `initial_state`s, different random `local_fields` and `phis` are generated. Do this by creating two different `CompareDTCTasks` for the different `initial_state`s, each of which compares `phis`. The collected polarizations are **not** autocorrelated, and are averaged over $24$ disorder instances. \n", + "\n", + "Store the polarizations matrix of shape `(num_cycles + 1, num_qubits)` without averaging, indexed by `phis` option and `initial_state` option. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "a19b4c927509" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 11min 25s, sys: 272 ms, total: 11min 25s\n", + "Wall time: 11min 25s\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# prepare two initial states to compare\n", + "polarized_initial_state = np.full(num_qubits, 0)\n", + "disturb_qubit = 11\n", + "disturbed_polarized_initial_state = polarized_initial_state.copy()\n", + "disturbed_polarized_initial_state[disturb_qubit] = 1\n", + "initial_states = [polarized_initial_state, disturbed_polarized_initial_state]\n", + "\n", + "# use different disorder instances for the two initial states\n", + "disorder_instances_options = [64, 81]\n", + "\n", + "# collect polarizations by phi and initial state options\n", + "average_polarizations = np.empty((2, 2, num_cycles + 1, num_qubits))\n", + "\n", + "# iterate over initial states and their associated number of disorder instances\n", + "for initial_state_index, (initial_state, disorder_instances) in enumerate(zip(initial_states, disorder_instances_options)): \n", + " \n", + " # disorder parameter h\n", + " local_fields = np.random.uniform(-1.0, 1.0, (disorder_instances, num_qubits))\n", + " \n", + " # prepare random and fixed phis to compare\n", + " disordered_phis = np.random.uniform(-1.5*np.pi, -0.5*np.pi, (disorder_instances, num_qubits - 1))\n", + " fixed_phis = np.full((disorder_instances, num_qubits - 1), -0.4)\n", + "\n", + " # create comparison task\n", + " options_dict = {\n", + " 'initial_state': [initial_state],\n", + " 'local_fields': [local_fields],\n", + " 'phis': [disordered_phis, fixed_phis]\n", + " }\n", + " options_order = ['local_fields', 'phis', 'initial_state']\n", + " comparedtctask = time_crystals.CompareDTCTask(qubits, num_cycles, disorder_instances, options_dict, options_order)\n", + "\n", + " # prepare polarizations and indices generators\n", + " polarizations_generator = time_crystals.run_comparison_experiment(comparedtctask, autocorrelate=False, take_abs=False)\n", + " \n", + " for phi_index, disorder_averaged_polarizations in enumerate(polarizations_generator):\n", + " # store average polarizations\n", + " average_polarizations[phi_index, initial_state_index, :, :] = disorder_averaged_polarizations\n", + "\n", + "# save data in json format\n", + "filename = f'{base_dir}/3d.json'\n", + "with open(filename, 'w+') as f:\n", + " cirq.to_json(average_polarizations, file_or_fn=f)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "833e5329b35e" + }, + "source": [ + "## Next Steps\n", + "With the data collected and saved, move on to the [Time Crystal Data Analysis](time_crystal_data_analysis.ipynb) notebook to generate the plots and evaluate their results." + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "time_crystal_data_collection.ipynb", + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/recirq/time_crystals/__init__.py b/recirq/time_crystals/__init__.py new file mode 100644 index 00000000..d196895b --- /dev/null +++ b/recirq/time_crystals/__init__.py @@ -0,0 +1,16 @@ +# Copyright 2021 Google +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from recirq.time_crystals.dtctask import * +from recirq.time_crystals.dtc_utilities import * diff --git a/recirq/time_crystals/dtc_utilities.py b/recirq/time_crystals/dtc_utilities.py new file mode 100644 index 00000000..da357697 --- /dev/null +++ b/recirq/time_crystals/dtc_utilities.py @@ -0,0 +1,236 @@ +# Copyright 2021 Google +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Sequence, Tuple, List + +import cirq +import functools +from recirq.time_crystals.dtctask import DTCTask, CompareDTCTask +import numpy as np +import sympy as sp + +def symbolic_dtc_circuit_list( + qubits: Sequence[cirq.Qid], + cycles: int + ) -> List[cirq.Circuit]: + + """ Create a list of symbolically parameterized dtc circuits, with increasing cycles + Args: + qubits: ordered sequence of available qubits, which are connected in a chain + cycles: maximum number of cycles to generate up to + Returns: + list of circuits with `0, 1, 2, ... cycles` many cycles + """ + + num_qubits = len(qubits) + + # Symbol for g + g_value = sp.Symbol('g') + + # Symbols for random variance (h) and initial state, one per qubit + local_fields = sp.symbols('local_field_:' + str(num_qubits)) + initial_state = sp.symbols('initial_state_:' + str(num_qubits)) + + # Symbols used for PhasedFsimGate, one for every qubit pair in the chain + thetas = sp.symbols('theta_:' + str(num_qubits - 1)) + zetas = sp.symbols('zeta_:' + str(num_qubits - 1)) + chis = sp.symbols('chi_:' + str(num_qubits - 1)) + gammas = sp.symbols('gamma_:' + str(num_qubits - 1)) + phis = sp.symbols('phi_:' + str(num_qubits - 1)) + + # Initial moment of Y gates, conditioned on initial state + initial_operations = cirq.Moment([cirq.Y(qubit) ** initial_state[index] for index, qubit in enumerate(qubits)]) + + # First component of U cycle, a moment of XZ gates. + sequence_operations = [] + for index, qubit in enumerate(qubits): + sequence_operations.append(cirq.PhasedXZGate( + x_exponent=g_value, axis_phase_exponent=0.0, + z_exponent=local_fields[index])(qubit)) + + # Initialize U cycle + u_cycle = [cirq.Moment(sequence_operations)] + + # Second and third components of U cycle, a chain of 2-qubit PhasedFSim gates + # The first component is all the 2-qubit PhasedFSim gates starting on even qubits + # The second component is the 2-qubit gates starting on odd qubits + operation_list, other_operation_list = [],[] + previous_qubit, previous_index = None, None + for index, qubit in enumerate(qubits): + if previous_qubit is None: + previous_qubit, previous_index = qubit, index + continue + + # Add an fsim gate + coupling_gate = cirq.ops.PhasedFSimGate( + theta=thetas[previous_index], + zeta=zetas[previous_index], + chi=chis[previous_index], + gamma=gammas[previous_index], + phi=phis[previous_index] + ) + operation_list.append(coupling_gate.on(previous_qubit, qubit)) + + # Swap the operation lists, to avoid two-qubit gate overlap + previous_qubit, previous_index = qubit, index + operation_list, other_operation_list = other_operation_list, operation_list + + # Add the two components into the U cycle + u_cycle.append(cirq.Moment(operation_list)) + u_cycle.append(cirq.Moment(other_operation_list)) + + # Prepare a list of circuits, with n=0,1,2,3 ... cycles many cycles + circuit_list = [] + total_circuit = cirq.Circuit(initial_operations) + circuit_list.append(total_circuit.copy()) + for c in range(cycles): + for m in u_cycle: + total_circuit.append(m) + circuit_list.append(total_circuit.copy()) + + return circuit_list + +def simulate_dtc_circuit_list(circuit_list: Sequence[cirq.Circuit], param_resolver: cirq.ParamResolver, qubit_order: Sequence[cirq.Qid]) -> np.ndarray: + """ Simulate a dtc circuit list for a particular param_resolver + Depends on the fact that simulating the last circuit in the list also simulates each previous circuit along the way + Args: + circuit_list: DTC circuit list; each element is a circuit with increasingly many cycles + param_resolver: `cirq.ParamResolver` to resolve symbolic parameters + qubit_order: ordered sequence of qubits connected in a chain + Returns: + `np.ndarray` of shape (len(circuit_list), 2**number of qubits) representing the probability of measuring each bit string, for each circuit in the list + """ + + # prepare simulator + simulator = cirq.Simulator() + + # record lengths of circuits in list + circuit_positions = [len(c) - 1 for c in circuit_list] + + # only simulate one circuit, the last one + circuit = circuit_list[-1] + + # use simulate_moment_steps to recover all of the state vectors necessary, while only simulating the circuit list once + probabilities = [] + for k, step in enumerate(simulator.simulate_moment_steps(circuit=circuit, param_resolver=param_resolver, qubit_order=qubit_order)): + # add the state vector if the number of moments simulated so far is equal to the length of a circuit in the circuit_list + if k in circuit_positions: + probabilities.append(np.abs(step.state_vector()) ** 2) + + return np.asarray(probabilities) + +def simulate_dtc_circuit_list_sweep(circuit_list: Sequence[cirq.Circuit], param_resolvers: Sequence[cirq.ParamResolver], qubit_order: Sequence[cirq.Qid]): + """ Simulate a dtc circuit list over a sweep of param_resolvers + Args: + circuit_list: DTC circuit list; each element is a circuit with increasingly many cycles + param_resolvers: list of `cirq.ParamResolver`s to sweep over + qubit_order: ordered sequence of qubits connected in a chain + Yields: + for each param_resolver, `np.ndarray`s of shape (len(circuit_list), 2**number of qubits) representing the probability of measuring each bit string, for each circuit in the list + """ + + # iterate over param resolvers and simulate for each + for param_resolver in param_resolvers: + yield simulate_dtc_circuit_list(circuit_list, param_resolver, qubit_order) + +def get_polarizations(probabilities: np.ndarray, num_qubits: int, cycles_axis: int = -2, probabilities_axis: int = -1, initial_states: np.ndarray = None) -> np.ndarray: + """ Get polarizations from matrix of probabilities, possibly autocorrelated on the initial state + Args: + probabilities: `np.ndarray` of shape (:, cycles, probabilities) representing probability to measure each bit string + num_qubits: the number of qubits in the circuit the probabilities were generated from + cycles_axis: the axis that represents the dtc cycles (if not in -2 indexed axis) + probabilities_axis: the axis that represents the probabilities for each bit string (if not in -1 indexed axis) + initial_states: `np.ndarray` of shape (:, qubits) representing the initial state for each dtc circuit list + Returns: + `np.ndarray` of shape (:, cycles, qubits) that represents each qubit's polarization + """ + + # prepare list of polarizations for each qubit + polarizations = [] + for qubit_index in range(num_qubits): + # select all indices in range(2**num_qubits) for which the associated element of the statevector has qubit_index as zero + shift_by = num_qubits - qubit_index - 1 + state_vector_indices = [i for i in range(2 ** num_qubits) if not (i >> shift_by) % 2] + + # sum over all amplitudes for qubit states for which qubit_index is zero, and rescale them to [-1,1] + polarization = 2.0 * np.sum(probabilities.take(indices=state_vector_indices, axis=probabilities_axis), axis=probabilities_axis) - 1.0 + polarizations.append(polarization) + + # turn polarizations list into an array, and move the new, leftmost axis for qubits to probabilities_axis + polarizations = np.moveaxis(np.asarray(polarizations), 0, probabilities_axis) + + # flip polarizations according to the associated initial_state, if provided + # this means that the polarization of a qubit is relative to it's initial state + if initial_states is not None: + initial_states = 1 - 2.0 * initial_states + polarizations = initial_states * polarizations + + return polarizations + + +def signal_ratio(zeta_1: np.ndarray, zeta_2: np.ndarray): + ''' Calculate signal ratio between two signals + Args: + zeta_1: signal (`np.ndarray` to represent polarization over time) + zeta 2: signal (`np.ndarray` to represent polarization over time) + Returns: + computed ratio signal of zeta_1 and zeta_2 (`np.ndarray` to represent polarization over time) + ''' + + return np.abs(zeta_1 - zeta_2)/(np.abs(zeta_1) + np.abs(zeta_2)) + + +def simulate_for_polarizations(dtctask: DTCTask, circuit_list: Sequence[cirq.Circuit], autocorrelate: bool = True, take_abs: bool = False): + """ Simulate and get polarizations for a single DTCTask and circuit list + Args: + dtctask: DTCTask noting the parameters to simulate over some number of disorder instances + circuit_list: symbolic dtc circuit list + autocorrelate: whether or not to autocorrelate the polarizations with their respective initial states + take_abs: whether or not to take the absolute value of the polarizations + Returns: + simulated polarizations (np.ndarray of shape (num_cycles, num_qubits)) from the experiment, averaged over disorder instances + """ + + # create param resolver sweep + param_resolvers = dtctask.param_resolvers() + + # prepare simulation generator + probabilities_generator = simulate_dtc_circuit_list_sweep(circuit_list, param_resolvers, dtctask.qubits) + + # map get_polarizations over probabilities_generator + polarizations_generator = map(lambda probabilities, initial_state: + get_polarizations(probabilities, num_qubits=len(dtctask.qubits), cycles_axis=0, probabilities_axis=1, initial_states=(initial_state if autocorrelate else None)), + probabilities_generator, dtctask.initial_states) + + # take sum of (absolute value of) polarizations over different disorder instances + polarization_sum = functools.reduce(lambda x,y: x+(np.abs(y) if take_abs else y), polarizations_generator, np.zeros((len(circuit_list), len(dtctask.qubits)))) + + # get average over disorder instances + disorder_averaged_polarizations = polarization_sum / dtctask.disorder_instances + + return disorder_averaged_polarizations + + +def run_comparison_experiment(comparedtctask: CompareDTCTask, autocorrelate: bool = True, take_abs: bool = False): + """ Run comparison experiment from a CompareDTCTask + Args: + comparedtctask: CompareDTCTask which notes which dtc arguments to compare, and default arguments + autocorrelate: whether or not to autocorrelate the polarizations with their respective initial states + take_abs: whether or not to take the absolute value of the polarizations + Yields: + disorder averaged polarizations, in order of the product of options supplied to comparedtctask, with all other parameters default + """ + + for dtctask in comparedtctask.dtctasks(): + yield simulate_for_polarizations(dtctask=dtctask, circuit_list=comparedtctask.circuit_list, autocorrelate=autocorrelate, take_abs=take_abs) diff --git a/recirq/time_crystals/dtctask.py b/recirq/time_crystals/dtctask.py new file mode 100644 index 00000000..addec1ad --- /dev/null +++ b/recirq/time_crystals/dtctask.py @@ -0,0 +1,254 @@ +# Copyright 2021 Google +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import cirq +import recirq +import datetime +import itertools +import numpy as np +from typing import Sequence, Optional, Dict +import os + +EXPERIMENT_NAME = "time_crystals" +DEFAULT_BASE_DIR = os.path.expanduser(f'~/cirq_results/{EXPERIMENT_NAME}') + + +@recirq.json_serializable_dataclass(namespace='recirq.readout_scan', + registry=recirq.Registry, + frozen=False) +class CompareDTCTask: + """ A task for managing inputs to a comparison Discrete Time Crystal experiment, comparing different options for parameters + + Attributes + dataset_id: unique identifier for this dataset + qubits: chain of connected qubits available for the circuit + cycles: number of DTC cycles to consider for circuits + circuit_list: symbolic DTC circuit list + disorder_instances: number of disorder instances averaged over + options_dict: dict mapping DTCTask attribute names to options for that attribute, to take a product over + options_order: sequence of keys in options_dict, defining order of product over options + """ + + # Task parameters + dataset_id: str + + # experiment parameters + qubits: Sequence[cirq.Qid] + cycles: int + disorder_instances: int + circuit_list: Sequence[cirq.Circuit] + + # options to take product over + options_dict: Dict[str, Sequence[np.ndarray]] + options_order: Sequence[str] + + def __init__( + self, + qubits: Sequence[cirq.Qid], + cycles: int, + disorder_instances: int, + options_dict: Dict[str, Sequence[np.ndarray]], + options_order: Optional[Sequence[str]] = None): + + self.dataset_id = datetime.datetime.utcnow() + + self.qubits = qubits + self.cycles = cycles + self.disorder_instances = disorder_instances + + # create symbolic circuit list from qubits and cycles count + self.circuit_list = recirq.time_crystals.symbolic_dtc_circuit_list(qubits, cycles) + + self.options_dict = options_dict + self.options_order = list(self.options_dict.keys()) if options_order is None else options_order + + # check that the input parameters are consistent + assert set(self.options_order) == set(self.options_dict.keys()), 'options_order and the keys of options_dict are not the same' + assert not {'initial_states', 'initial_state'} <= self.options_dict.keys(), 'do not supply both initial_states and initial_state' + + + @property + def fn(self): + fn = (f'{self.dataset_id}/' + f'{len(self.qubits)}/' + f'{self.cycles}/' + f'{self.disorder_instances}/' + f'{self.options_dict}') + return fn + + + def dtctasks(self): + """ Yield a sequence of DTCTasks that are the product of the options in self.options_dict. + All DTCTask attributes not in options_dict are taken to be their default values + Yields: + DTCTasks with parameters taken from self.options_dict + """ + + # take product over elements of options_dict, in the order of options_order + for components in itertools.product(*(self.options_dict[attribute_name] for attribute_name in self.options_order)): + # prepare arguments for DTCTask + kwargs = dict(zip(self.options_order, components)) + yield DTCTask(qubits=self.qubits, disorder_instances=self.disorder_instances, **kwargs) + + +@recirq.json_serializable_dataclass(namespace='recirq.readout_scan', + registry=recirq.Registry, + frozen=False) +class DTCTask: + """ A task for managing inputs to a Discrete Time Crystal experiment, over some number of disorder instances + + Attributes: + dataset_id: unique identifier for this dataset + qubits: a chain of connected qubits available for the circuit + disorder_instances: number of disorder instances averaged over + initial_states: initial state of the system used in circuit + g: control parameter used in circuit + local_fields: random potentials used in circuit + thetas: theta parameters for FSim Gate used in circuit + zetas: zeta parameters for FSim Gate used in circuit + chis: chi parameters for FSim Gate used in circuit + phis: phi parameters for FSim Gate used in circuit + gammas: gamma parameters for FSim Gate used in circuit + + """ + # Task parameters + dataset_id: str + + # experiment parameters + qubits: Sequence[cirq.Qid] + disorder_instances: int + + # FSim Gate parameters + # ndarrays in this section are in shape (disorder_instances, len(qubits) - 1) + g: int + initial_states: np.ndarray + local_fields: np.ndarray + + # FSim Gate Parameters + # ndarrays in this section are in shape (disorder_instances, len(qubits) - 1) + thetas: np.ndarray + zetas: np.ndarray + chis: np.ndarray + gammas: np.ndarray + phis: np.ndarray + + + def __init__( + self, + qubits: Optional[Sequence[cirq.Qid]] = None, + disorder_instances: Optional[int] = None, + g: Optional[int] = None, + initial_state: Optional[np.ndarray] = None, + initial_states: Optional[np.ndarray] = None, + local_fields: Optional[np.ndarray] = None, + thetas: Optional[np.ndarray] = None, + zetas: Optional[np.ndarray] = None, + chis: Optional[np.ndarray] = None, + gammas: Optional[np.ndarray] = None, + phis: Optional[np.ndarray] = None + ): + + self.dataset_id = datetime.datetime.utcnow() + + self.disorder_instances = 36 if disorder_instances is None else disorder_instances + + self.g = 0.94 if g is None else g + + if qubits is None: + qubit_locations = [(3, 9), (3, 8), (3, 7), (4, 7), (4, 8), (5, 8), (5, 7), (5, 6), (6, 6), (6, 5), (7, 5), (8, 5), (8, 4), (8, 3), (7, 3), (6, 3)] + self.qubits = [cirq.GridQubit(*idx) for idx in qubit_locations] + else: + self.qubits = qubits + + num_qubits = len(self.qubits) + + # only enable use of initial_state or initial_states + assert initial_state is None or initial_states is None, 'do not supply both initial_state and initial_states' + if initial_state is None and initial_states is None: + self.initial_states = np.random.choice(2, (self.disorder_instances, num_qubits)) + elif initial_states is None: + assert len(initial_state) == num_qubits, f'initial_state is of shape {str(len(initial_state))}, not (num_qubits,)' + self.initial_states = np.tile(initial_state, (self.disorder_instances, 1)) + elif initial_state is None: + assert initial_states.shape == (self.disorder_instances, num_qubits), f'initial_states is of shape {initial_states.shape}, not (disorder_instances, num_qubits)' + self.initial_states = initial_states + + if local_fields is None: + self.local_fields = np.random.uniform(-1.0, 1.0, (self.disorder_instances, num_qubits)) + else: + assert local_fields.shape == (self.disorder_instances, num_qubits), f'local_fields is of shape {local_fields.shape}, not (disorder_instnaces, num_qubits)' + self.local_fields = local_fields + + zero_params = [thetas, zetas, chis] + for index, zero_param in enumerate(zero_params): + if zero_param is None: + zero_params[index] = np.zeros((self.disorder_instances, num_qubits - 1)) + else: + assert zero_param.shape == (self.disorder_instances, num_qubits - 1), f'thetas, zetas or chis is of shape {zero_param.shape}, not (disorder_instances, num_qubits - 1)' + self.thetas, self.zetas, self.chis = zero_params + + # if gamma or phi is not supplied, generate it from the other such that phis == -2*gammas + if gammas is None and phis is None: + self.gammas = -np.random.uniform(0.5*np.pi, 1.5*np.pi, (self.disorder_instances, num_qubits - 1)) + self.phis = -2*self.gammas + elif phis is None: + assert gammas.shape == (self.disorder_instances, num_qubits - 1), f'gammas is of shape {gammas.shape}, not (disorder_instances, num_qubits - 1)' + self.gammas = gammas + self.phis = -2*self.gammas + elif gammas is None: + assert phis.shape == (self.disorder_instances, num_qubits - 1), f'phis is of shape {phis.shape}, not (disorder_instances, num_qubits - 1)' + self.phis = phis + self.gammas = -1/2*self.phis + else: + assert gammas.shape == (self.disorder_instances, num_qubits - 1), f'gammas is of shape {gammas.shape}, not (disorder_instances, num_qubits - 1)' + assert phis.shape == (self.disorder_instances, num_qubits - 1), f'phis is of shape {phis.shape}, not (disorder_instances, num_qubits - 1)' + self.phis = phis + self.gammas = gammas + + + @property + def fn(self): + fn = (f'{self.dataset_id}/' + f'{self.qubits}/' + f'{self.disorder_instances}/' + f'{self.g}/' + f'{self.initial_states}/' + f'{self.local_fields}/' + f'{self.thetas}/' + f'{self.zetas}/' + f'{self.chis}/' + f'{self.gammas}/' + f'{self.phis}/') + return fn + + + def param_resolvers(self): + """ return a sweep over param resolvers for the parameters of this task + Returns: + `cirq.Zip` object with self.disorder_instances many `cirq.ParamResolver`s + """ + + # initialize the dict and add the first, non-qubit-dependent parameter, g + factor_dict = {'g': np.full(self.disorder_instances, self.g).tolist()} + + # iterate over the different parameters + qubit_varying_factors = ["initial_states", "local_fields", "thetas", "zetas", "chis", "gammas", "phis"] + for factor in qubit_varying_factors: + factor_options = getattr(self, factor) + # iterate over each index in the qubit chain and the various options for that qubit + for index, qubit_factor_options in enumerate(factor_options.transpose()): + factor_name = factor[:-1] + factor_dict[f'{factor_name}_{index}'] = qubit_factor_options.tolist() + + return cirq.study.dict_to_zip_sweep(factor_dict) diff --git a/recirq/time_crystals/dtctask_test.py b/recirq/time_crystals/dtctask_test.py new file mode 100644 index 00000000..6a8db92d --- /dev/null +++ b/recirq/time_crystals/dtctask_test.py @@ -0,0 +1,69 @@ +# Copyright 2021 Google +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import recirq.time_crystals as time_crystals +import cirq +import numpy as np + +def test_DTCTask(): + np.random.seed(5) + qubit_locations = [(3, 9), (3, 8), (3, 7), (4, 7), (4, 8), (5, 8), (5, 7), (5, 6), (6, 6), (6, 5), (7, 5), (8, 5), + (8, 4), (8, 3), (7, 3), (6, 3)] + + qubits = [cirq.GridQubit(*idx) for idx in qubit_locations] + num_qubits = len(qubits) + g = 0.94 + instances = 36 + initial_state = np.random.choice(2, num_qubits) + local_fields = np.random.uniform(-1.0, 1.0, (instances, num_qubits)) + thetas = np.zeros((instances, num_qubits - 1)) + zetas = np.zeros((instances, num_qubits - 1)) + chis = np.zeros((instances, num_qubits - 1)) + gammas = -np.random.uniform(0.5*np.pi, 1.5*np.pi, (instances, num_qubits - 1)) + phis = -2*gammas + args = ['qubits', 'g', 'initial_state', 'local_fields', 'thetas', 'zetas', 'chis', 'gammas', 'phis'] + default_resolvers = time_crystals.DTCTask().param_resolvers() + for arg in args: + kwargs = {} + for name in args: + kwargs[name] = None if name is arg else locals()[name] + dtctask = time_crystals.DTCTask(disorder_instances=instances, **kwargs) + param_resolvers = dtctask.param_resolvers() + +def test_CompareDTCTask(): + np.random.seed(5) + qubit_locations = [(3, 9), (3, 8), (3, 7), (4, 7), (4, 8), (5, 8)]#, (5, 7), (5, 6), (6, 6), (6, 5), (7, 5), (8, 5), (8, 4), (8, 3), (7, 3), (6, 3)] + qubits = [cirq.GridQubit(*idx) for idx in qubit_locations] + num_qubits = len(qubits) + disorder_instances = 8 + initial_state_instances = 6 + cycles = 10 + + options = { + 'g': [0.6, 0.94], + 'local_fields': [np.random.uniform(-1.0, 1.0, (disorder_instances, num_qubits))], + 'initial_state': [[0]*num_qubits, [0,1]*(num_qubits//2), np.random.choice(2, num_qubits)], + 'initial_states': [np.random.choice(2, (disorder_instances, num_qubits))], + 'gammas': [np.random.uniform(-0.5*np.pi, -1.5*np.pi, (disorder_instances, num_qubits - 1))], + 'phis': [np.random.uniform(-1.5*np.pi, -0.5*np.pi, (disorder_instances, num_qubits - 1)), np.full((disorder_instances, num_qubits - 1), -0.4)], + } + for initial in ['initial_state', 'initial_states']: + for variable in ['gammas', 'phis']: + options_dict = {k:v for k,v in options.items() if k not in (initial, variable)} + compare_dtctask = time_crystals.CompareDTCTask(qubits, cycles, disorder_instances, options_dict) + for autocorrelate in [True,False]: + for take_abs in [True,False]: + for index, polarizations in enumerate(time_crystals.run_comparison_experiment(compare_dtctask, autocorrelate, take_abs)): + print(index, autocorrelate, take_abs, initial, variable) + print(polarizations)