Create workflow for Z-phase calibration

cirq-core/cirq/experiments/__init__.py

@@ -82,3 +82,6 @@
     parallel_two_qubit_xeb as parallel_two_qubit_xeb,
     run_rb_and_xeb as run_rb_and_xeb,
 )
+
+
+from cirq.experiments.z_phase_calibration import z_phase_calibration_workflow, calibrate_z_phases

cirq-core/cirq/experiments/two_qubit_xeb.py

@@ -38,6 +38,7 @@
 from cirq._compat import cached_method
 
 if TYPE_CHECKING:
+    import multiprocessing
     import cirq
 
 
@@ -358,6 +359,7 @@
     cycle_depths: Sequence[int] = (5, 25, 50, 100, 200, 300),
     random_state: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None,
     ax: Optional[plt.Axes] = None,
+    pool: Optional['multiprocessing.pool.Pool'] = None,
     **plot_kwargs,
 ) -> Tuple[pd.DataFrame, Sequence['cirq.Circuit'], pd.DataFrame]:
     """A utility method that runs the full XEB workflow.
@@ -373,6 +375,7 @@
         random_state: The random state to use.
         ax: the plt.Axes to plot the device layout on. If not given,
             no plot is created.
+        pool: An optional multiprocessing pool.
         **plot_kwargs: Arguments to be passed to 'plt.Axes.plot'.
 
     Returns:
@@ -426,7 +429,7 @@
     )
 
     fids = benchmark_2q_xeb_fidelities(
-        sampled_df=sampled_df, circuits=circuit_library, cycle_depths=cycle_depths
+        sampled_df=sampled_df, circuits=circuit_library, cycle_depths=cycle_depths, pool=pool
     )
 
     return fids, circuit_library, sampled_df

cirq-core/cirq/experiments/xeb_fitting.py

@@ -95,6 +95,11 @@ def benchmark_2q_xeb_fidelities(
     df['e_u'] = np.sum(pure_probs**2, axis=1)
     df['u_u'] = np.sum(pure_probs, axis=1) / D
     df['m_u'] = np.sum(pure_probs * sampled_probs, axis=1)
+    # Var[m_u] = Var[sum p(x) * p_sampled(x)]
+    #           = sum p(x)^2 Var[p_sampled(x)]
+    #           = sum p(x)^2 p(x) (1 - p(x))
+    #           = sum p(x)^3 (1 - p(x))
+    df['var_m_u'] = np.sum(pure_probs**3 * (1 - pure_probs), axis=1)
     df['y'] = df['m_u'] - df['u_u']
     df['x'] = df['e_u'] - df['u_u']
     df['numerator'] = df['x'] * df['y']
@@ -103,7 +108,11 @@ def benchmark_2q_xeb_fidelities(
     def per_cycle_depth(df):
         """This function is applied per cycle_depth in the following groupby aggregation."""
         fid_lsq = df['numerator'].sum() / df['denominator'].sum()
-        ret = {'fidelity': fid_lsq}
+        # Note: both df['denominator'] and df['x'] are constants.
+        # Var[f] = Var[df['numerator']] / (sum df['denominator'])^2
+        #           = sum (df['x']^2 * df['var_m_u']) / (sum df['denominator'])^2
+        var_fid = (df['var_m_u'] * df['x'] ** 2).sum() / df['denominator'].sum() ** 2
+        ret = {'fidelity': fid_lsq, 'fidelity_variance': var_fid}
 
         def _try_keep(k):
             """If all the values for a key `k` are the same in this group, we can keep it."""
@@ -385,16 +394,21 @@ def SqrtISwapXEBOptions(*args, **kwargs):
 
 
 def parameterize_circuit(
-    circuit: 'cirq.Circuit', options: XEBCharacterizationOptions
+    circuit: 'cirq.Circuit',
+    options: XEBCharacterizationOptions,
+    target_gatefamily: Optional[ops.GateFamily] = None,
 ) -> 'cirq.Circuit':
     """Parameterize PhasedFSim-like gates in a given circuit according to
     `phased_fsim_options`.
     """
+    if isinstance(target_gatefamily, ops.GateFamily):
+        should_parameterize = lambda op: op in target_gatefamily or options.should_parameterize(op)
+    else:
+        should_parameterize = options.should_parameterize
     gate = options.get_parameterized_gate()
     return circuits.Circuit(
         circuits.Moment(
-            gate.on(*op.qubits) if options.should_parameterize(op) else op
-            for op in moment.operations
+            gate.on(*op.qubits) if should_parameterize(op) else op for op in moment.operations
         )
         for moment in circuit.moments
     )
@@ -667,13 +681,16 @@ def _per_pair(f1):
         a, layer_fid, a_std, layer_fid_std = _fit_exponential_decay(
             f1['cycle_depth'], f1['fidelity']
         )
+        fidelity_variance = 0
+        if 'fidelity_variance' in f1:
+            fidelity_variance = f1['fidelity_variance'].values
         record = {
             'a': a,
             'layer_fid': layer_fid,
             'cycle_depths': f1['cycle_depth'].values,
             'fidelities': f1['fidelity'].values,
             'a_std': a_std,
-            'layer_fid_std': layer_fid_std,
+            'layer_fid_std': np.sqrt(layer_fid_std**2 + fidelity_variance),
         }
         return pd.Series(record)
 

cirq-core/cirq/experiments/z_phase_calibration.py

@@ -0,0 +1,271 @@
+# Copyright 2024 The Cirq Developers
+#
+# 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.
+
+"""Provides a method to do z-phase calibration for excitation-preserving gates."""
+from typing import Union, Optional, Sequence, Tuple, Dict, TYPE_CHECKING
+
+import multiprocessing
+import multiprocessing.pool
+import matplotlib.pyplot as plt
+import numpy as np
+
+from cirq.experiments import xeb_fitting
+from cirq.experiments.two_qubit_xeb import parallel_xeb_workflow
+from cirq import ops
+
+if TYPE_CHECKING:
+    import cirq
+    import pandas as pd
+
+
+def z_phase_calibration_workflow(
+    sampler: 'cirq.Sampler',
+    qubits: Optional[Sequence['cirq.GridQubit']] = None,
+    two_qubit_gate: 'cirq.Gate' = ops.CZ,
+    options: Optional[xeb_fitting.XEBPhasedFSimCharacterizationOptions] = None,
+    n_repetitions: int = 10**4,
+    n_combinations: int = 10,
+    n_circuits: int = 20,
+    cycle_depths: Sequence[int] = tuple(np.arange(3, 100, 20)),
+    random_state: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None,
+    atol: float = 1e-3,
+    pool_or_num_workers: Optional[Union[int, 'multiprocessing.pool.Pool']] = None,
+) -> Tuple[xeb_fitting.XEBCharacterizationResult, 'pd.DataFrame']:
+    """Perform z-phase calibration for excitation-preserving gates.
+
+    For a given excitation-preserving two-qubit gate we assume an error model that can be described
+    using Z-rotations:
+                0: ───Rz(a)───two_qubit_gate───Rz(c)───
+                                │
+                1: ───Rz(b)───two_qubit_gate───Rz(d)───
+    for some angles a, b, c, and d.
+
+    Since the two-qubit gate is a excitation-preserving-gate, it can be represented by an FSimGate
+    and the effect of rotations turns it into a PhasedFSimGate. Using XEB-data we find the
+    PhasedFSimGate parameters that minimize the infidelity of the gate.
+
+    References:
+        - https://arxiv.org/abs/2001.08343
+        - https://arxiv.org/abs/2010.07965
+        - https://arxiv.org/abs/1910.11333
+
+    Args:
+        sampler: The quantum engine or simulator to run the circuits.
+        qubits: Qubits to use. If none, use all qubits on the sampler's device.
+        two_qubit_gate: The entangling gate to use.
+        options: The XEB-fitting options. If None, calibrate all 5 PhasedFSimGate parameters,
+            using the representation of a two-qubit gate as an FSimGate for the initial guess.
+        n_repetitions: The number of repetitions to use.
+        n_combinations: The number of combinations to generate.
+        n_circuits: The number of circuits to generate.
+        cycle_depths: The cycle depths to use.
+        random_state: The random state to use.
+        atol: Absolute tolerance to be used by the minimizer.
+        pool_or_num_workers: An optional multi-processing pool or number of workers.
+            A zero value means no multiprocessing.
+            A positivie integer value will create a pool with the given number of workers.
+            A None value will create pool with maximum number of workers.
+    Returns:
+        - An `XEBCharacterizationResult` object that contains the calibration result.
+        - A `pd.DataFrame` comparing the before and after fidelities.
+    """
+
+    pool: Optional['multiprocessing.pool.Pool'] = None
+    local_pool = False
+    if isinstance(pool_or_num_workers, multiprocessing.pool.Pool):
+        pool = pool_or_num_workers  # pragma: no cover
+    elif pool_or_num_workers != 0:
+        pool = multiprocessing.Pool(pool_or_num_workers)
+        local_pool = True
+
+    fids_df_0, circuits, sampled_df = parallel_xeb_workflow(
+        sampler=sampler,
+        qubits=qubits,
+        entangling_gate=two_qubit_gate,
+        n_repetitions=n_repetitions,
+        cycle_depths=cycle_depths,
+        n_circuits=n_circuits,
+        n_combinations=n_combinations,
+        random_state=random_state,
+        pool=pool,
+    )
+
+    if options is None:
+        options = xeb_fitting.XEBPhasedFSimCharacterizationOptions(
+            characterize_chi=True,
+            characterize_gamma=True,
+            characterize_zeta=True,
+            characterize_theta=False,
+            characterize_phi=False,
+        ).with_defaults_from_gate(two_qubit_gate)
+
+    p_circuits = [
+        xeb_fitting.parameterize_circuit(circuit, options, ops.GateFamily(two_qubit_gate))
+        for circuit in circuits
+    ]
+
+    result = xeb_fitting.characterize_phased_fsim_parameters_with_xeb_by_pair(
+        sampled_df=sampled_df,
+        parameterized_circuits=p_circuits,
+        cycle_depths=cycle_depths,
+        options=options,
+        fatol=atol,
+        xatol=atol,
+        pool=pool,
+    )
+
+    before_after = xeb_fitting.before_and_after_characterization(
+        fids_df_0, characterization_result=result
+    )
+
+    if local_pool:
+        assert isinstance(pool, multiprocessing.pool.Pool)
+        pool.close()
+    return result, before_after
+
+
+def calibrate_z_phases(
+    sampler: 'cirq.Sampler',
+    qubits: Optional[Sequence['cirq.GridQubit']] = None,
+    two_qubit_gate: 'cirq.Gate' = ops.CZ,
+    options: Optional[xeb_fitting.XEBPhasedFSimCharacterizationOptions] = None,
+    n_repetitions: int = 10**4,
+    n_combinations: int = 10,
+    n_circuits: int = 20,
+    cycle_depths: Sequence[int] = tuple(np.arange(3, 100, 20)),
+    random_state: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None,
+    atol: float = 1e-3,
+    pool_or_num_workers: Optional[Union[int, 'multiprocessing.pool.Pool']] = None,
+) -> Dict[Tuple['cirq.Qid', 'cirq.Qid'], 'cirq.PhasedFSimGate']:
+    """Perform z-phase calibration for excitation-preserving gates.
+
+    For a given excitation-preserving two-qubit gate we assume an error model that can be described
+    using Z-rotations:
+                0: ───Rz(a)───two_qubit_gate───Rz(c)───
+                                │
+                1: ───Rz(b)───two_qubit_gate───Rz(d)───
+    for some angles a, b, c, and d.
+
+    Since the two-qubit gate is a excitation-preserving gate, it can be represented by an FSimGate
+    and the effect of rotations turns it into a PhasedFSimGate. Using XEB-data we find the
+    PhasedFSimGate parameters that minimize the infidelity of the gate.
+
+    References:
+        - https://arxiv.org/abs/2001.08343
+        - https://arxiv.org/abs/2010.07965
+        - https://arxiv.org/abs/1910.11333
+
+    Args:
+        sampler: The quantum engine or simulator to run the circuits.
+        qubits: Qubits to use. If none, use all qubits on the sampler's device.
+        two_qubit_gate: The entangling gate to use.
+        options: The XEB-fitting options. If None, calibrate all 5 PhasedFSimGate parameters,
+            using the representation of a two-qubit gate as an FSimGate for the initial guess.
+        n_repetitions: The number of repetitions to use.
+        n_combinations: The number of combinations to generate.
+        n_circuits: The number of circuits to generate.
+        cycle_depths: The cycle depths to use.
+        random_state: The random state to use.
+        atol: Absolute tolerance to be used by the minimizer.
+        pool_or_num_workers: An optional multi-processing pool or number of workers.
+            A zero value means no multiprocessing.
+            A positivie integer value will create a pool with the given number of workers.
+            A None value will create pool with maximum number of workers.
+
+    Returns:
+        - A dictionary mapping qubit pairs to the calibrated PhasedFSimGates.
+    """
+
+    if options is None:
+        options = xeb_fitting.XEBPhasedFSimCharacterizationOptions(
+            characterize_chi=True,
+            characterize_gamma=True,
+            characterize_zeta=True,
+            characterize_theta=False,
+            characterize_phi=False,
+        ).with_defaults_from_gate(two_qubit_gate)
+
+    result, _ = z_phase_calibration_workflow(
+        sampler=sampler,
+        qubits=qubits,
+        two_qubit_gate=two_qubit_gate,
+        options=options,
+        n_repetitions=n_repetitions,
+        n_combinations=n_combinations,
+        n_circuits=n_circuits,
+        cycle_depths=cycle_depths,
+        random_state=random_state,
+        atol=atol,
+        pool_or_num_workers=pool_or_num_workers,
+    )
+
+    gates = {}
+    for pair, params in result.final_params.items():
+        params['theta'] = params.get('theta', options.theta_default or 0)
+        params['phi'] = params.get('phi', options.phi_default or 0)
+        params['zeta'] = params.get('zeta', options.zeta_default or 0)
+        params['chi'] = params.get('chi', options.chi_default or 0)
+        params['gamma'] = params.get('gamma', options.gamma_default or 0)
+        gates[pair] = ops.PhasedFSimGate(**params)
+    return gates
+
+
+def plot_z_phase_calibration_result(
+    before_after_df: 'pd.DataFrame',
+    axes: Optional[np.ndarray[Sequence[Sequence['plt.Axes']], np.dtype[np.object_]]] = None,
+    pairs: Optional[Sequence[Tuple['cirq.Qid', 'cirq.Qid']]] = None,
+    *,
+    with_error_bars: bool = False,
+) -> np.ndarray[Sequence[Sequence['plt.Axes']], np.dtype[np.object_]]:
+    """A helper method to plot the result of running z-phase calibration.
+
+    Note that the plotted fidelity is a statistical estimate of the true fidelity and as a result
+    may be outside the [0, 1] range.
+
+    Args:
+        before_after_df: The second return object of running `z_phase_calibration_workflow`.
+        axes: And ndarray of the axes to plot on.
+            The number of axes is expected to be >= number of qubit pairs.
+        pairs: If provided, only the given pairs are plotted.
+        with_error_bars: Whether to add error bars or not.
+            The width of the bar is an upper bound on standard variation of the estimated fidelity.
+    """
+    if pairs is None:
+        pairs = before_after_df.index
+    if axes is None:
+        # Create a 16x9 rectangle.
+        ncols = int(np.ceil(np.sqrt(9 / 16 * len(pairs))))
+        nrows = (len(pairs) + ncols - 1) // ncols
+        _, axes = plt.subplots(nrows=nrows, ncols=ncols)
+    axes = axes if isinstance(axes, np.ndarray) else np.array(axes)
+    for pair, ax in zip(pairs, axes.flatten()):
+        row = before_after_df.loc[[pair]].iloc[0]
+        ax.errorbar(
+            row.cycle_depths_0,
+            row.fidelities_0,
+            yerr=row.layer_fid_std_0 * with_error_bars,
+            label='original',
+        )
+        ax.errorbar(
+            row.cycle_depths_0,
+            row.fidelities_c,
+            yerr=row.layer_fid_std_c * with_error_bars,
+            label='calibrated',
+        )
+        ax.axhline(1, linestyle='--')
+        ax.set_xlabel('cycle depth')
+        ax.set_ylabel('fidelity estimate')
+        ax.set_title('-'.join(str(q) for q in pair))
+        ax.legend()
+    return axes