diff --git a/doc/gates.md b/doc/gates.md index 620fd8df4..8984bb000 100644 --- a/doc/gates.md +++ b/doc/gates.md @@ -78,6 +78,7 @@ - [MYY](#MYY) - [MZZ](#MZZ) - Generalized Pauli Product Gates + - [CPP](#CPP) - [MPP](#MPP) - [SPP](#SPP) - [SPP_DAG](#SPP_DAG) @@ -2976,6 +2977,72 @@ Decomposition (into H, S, CX, M, R): ## Generalized Pauli Product Gates + +### The 'CPP' Instruction + +The generalized CNOT gate. Negates states in the intersection of Pauli product observables. + +Parens Arguments: + + This instruction takes no parens arguments. + +Targets: + + A series of pairs of Pauli products to intersect. + + Each Pauli product is a series of Pauli targets (`[XYZ]#`), record targets (`rec[-#]`), + or sweep targets (`sweep[#]`) separated by combiners (`*`). Each product can be negated + by prefixing a Pauli target in the product with an inverter (`!`). + + The number of products must be even. CPP X1 Y2 Z3 isn't allowed. + Within each pair of products, the pair must commute. CPP X1 Z1 isn't allowed. + +Examples: + + # Perform a CNOT gate with qubit 1 as the control and qubit 2 as the target. + CPP X1 Z2 + + # Perform a CZ gate between qubit 2 and qubit 5, then between qubit 3 and 4. + CPP Z2 Z5 Z3 Z4 + + # Perform many CX gates, all controlled by qubit 2, targeting qubits 5 through 10. + CPP Z2 X5*X6*X7*X8*X9*X10 + + # Swap qubit 1 and qubit 5 by negating their overlap with the singlet state. + CPP X1*X5 Z1*Z5 + + # Negate the amplitude of the |00> state. + CPP !Z0 !Z1 + + # Measure qubit 0 and do Pauli operations conditioned on the measurement returning TRUE. + M 0 + CPP rec[-1] X1*Y2*Z3 + +Stabilizer Generators (for `CPP X0*Y1 Z2*Z3`): + + X___ -> X___ + Z___ -> Z_ZZ + _X__ -> _XZZ + _Z__ -> _ZZZ + __X_ -> XYX_ + __Z_ -> __Z_ + ___X -> XY_X + ___Z -> ___Z + +Decomposition (into H, S, CX, M, R): + + # The following circuit is equivalent (up to global phase) to `CPP X0*Y1 Z2*Z3` + CX 3 2 + CX 1 0 + S 1 + S 1 + S 1 + CX 2 1 + S 1 + CX 1 0 + CX 3 2 + + ### The 'MPP' Instruction diff --git a/glue/crumble/test/generated_gate_name_list.test.js b/glue/crumble/test/generated_gate_name_list.test.js index b83143595..f8fd970c9 100644 --- a/glue/crumble/test/generated_gate_name_list.test.js +++ b/glue/crumble/test/generated_gate_name_list.test.js @@ -70,6 +70,7 @@ RZ MXX MYY MZZ +CPP MPP SPP SPP_DAG diff --git a/src/stim/circuit/circuit.test.cc b/src/stim/circuit/circuit.test.cc index ddfcd6174..38ec2de65 100644 --- a/src/stim/circuit/circuit.test.cc +++ b/src/stim/circuit/circuit.test.cc @@ -207,6 +207,33 @@ TEST(circuit, parse_mpp) { ASSERT_EQ(c.operations[0].targets.size(), 3); } +TEST(circuit, parse_cpp) { + ASSERT_THROW({ Circuit("CPP X1"); }, std::invalid_argument); + ASSERT_THROW({ Circuit("CPP 1 2"); }, std::invalid_argument); + ASSERT_THROW({ Circuit("CPP X1 X2 X3"); }, std::invalid_argument); + ASSERT_THROW({ Circuit("CPP X1*X2 X2 X3"); }, std::invalid_argument); + ASSERT_THROW({ Circuit("CPP X1*X4 X2*Z5*Z9*Z10 X3"); }, std::invalid_argument); + ASSERT_THROW({ Circuit("CPP rec[-1]"); }, std::invalid_argument); + + Circuit c; + + c = Circuit("CPP"); + ASSERT_EQ(c.operations.size(), 1); + + c = Circuit("CPP rec[-1] X1*Y2*Z3"); + ASSERT_EQ(c.operations.size(), 1); + + c = Circuit("CPP sweep[0] rec[-1]*X1*sweep[2]"); + ASSERT_EQ(c.operations.size(), 1); + + c = Circuit("CPP X1 Z2"); + ASSERT_EQ(c.operations.size(), 1); + ASSERT_EQ(c.operations[0].targets.size(), 2); + ASSERT_EQ( + c.operations[0].targets, + ((SpanRef)std::vector{GateTarget::x(1), GateTarget::z(2)})); +} + TEST(circuit, parse_spp) { ASSERT_THROW({ Circuit("SPP 1"); }, std::invalid_argument); ASSERT_THROW({ Circuit("SPP rec[-1]"); }, std::invalid_argument); @@ -1705,6 +1732,7 @@ Circuit stim::generate_test_circuit_with_all_operations() { # Pauli Product Gates MPP X0*Y1*Z2 Z0*Z1 + CPP X3*X4*X5 Z3*Z4*Y6 Y7 Y8 SPP X0*Y1*Z2 X3 SPP_DAG X0*Y1*Z2 X2 TICK diff --git a/src/stim/circuit/export_qasm.cc b/src/stim/circuit/export_qasm.cc index 20ee303c2..a1d485b63 100644 --- a/src/stim/circuit/export_qasm.cc +++ b/src/stim/circuit/export_qasm.cc @@ -138,6 +138,14 @@ struct QasmExporter { out << "// --- end decomposed MPP\n"; } + void output_decomposed_cpp_operation(const CircuitInstruction &inst) { + out << "// --- begin decomposed " << inst << "\n"; + decompose_cpp_operation_with_reverse_independence(inst, stats.num_qubits, [&](const CircuitInstruction &inst) { + output_instruction(inst); + }); + out << "// --- end decomposed CPP\n"; + } + void output_decomposed_spp_or_spp_dag_operation(const CircuitInstruction &inst) { out << "// --- begin decomposed " << inst << "\n"; decompose_spp_or_spp_dag_operation(inst, stats.num_qubits, false, [&](const CircuitInstruction &inst) { @@ -528,6 +536,9 @@ struct QasmExporter { case GateType::SPP_DAG: output_decomposed_spp_or_spp_dag_operation(instruction); return; + case GateType::CPP: + output_decomposed_cpp_operation(instruction); + return; default: break; diff --git a/src/stim/circuit/export_qasm.test.cc b/src/stim/circuit/export_qasm.test.cc index 7585fa5b5..25321b9fa 100644 --- a/src/stim/circuit/export_qasm.test.cc +++ b/src/stim/circuit/export_qasm.test.cc @@ -403,6 +403,30 @@ cx q[1], q[0]; measure q[0] -> rec[3]; cx q[1], q[0]; // --- end decomposed MPP +// --- begin decomposed CPP X3*X4*X5 Z3*Z4*Y6 Y7 Y8 +h q[3]; +h q[4]; +h q[5]; +cx q[4], q[3]; +cx q[5], q[3]; +h q[4]; +hyz q[6]; +cx q[6], q[4]; +cz q[3], q[4]; +cx q[6], q[4]; +hyz q[6]; +h q[4]; +cx q[5], q[3]; +cx q[4], q[3]; +h q[3]; +h q[4]; +h q[5]; +hyz q[7]; +hyz q[8]; +cz q[7], q[8]; +hyz q[7]; +hyz q[8]; +// --- end decomposed CPP // --- begin decomposed SPP X0*Y1*Z2 X3 h q[0]; hyz q[1]; @@ -611,6 +635,24 @@ cx q[1], q[0]; measure q[0] -> rec[3]; cx q[1], q[0]; // --- end decomposed MPP +hyz q[6]; +cx q[6], q[3]; +cx q[4], q[3]; +h q[4]; +h q[5]; +cx q[5], q[4]; +cz q[3], q[4]; +cx q[5], q[4]; +h q[4]; +h q[5]; +cx q[4], q[3]; +cx q[6], q[3]; +hyz q[8]; +hyz q[7]; +hyz q[6]; +cz q[8], q[7]; +hyz q[8]; +hyz q[7]; // --- begin decomposed SPP X0*Y1*Z2 X3 h q[0]; hyz q[1]; diff --git a/src/stim/circuit/gate_decomposition.cc b/src/stim/circuit/gate_decomposition.cc index 358c7f205..5a63b49be 100644 --- a/src/stim/circuit/gate_decomposition.cc +++ b/src/stim/circuit/gate_decomposition.cc @@ -269,6 +269,156 @@ void stim::decompose_spp_or_spp_dag_operation( } } +static void decompose_cpp_operation_with_reverse_independence_helper( + CircuitInstruction cpp_op, + PauliStringRef<64> obs1, + PauliStringRef<64> obs2, + std::span classical_bits1, + std::span classical_bits2, + const std::function &do_instruction_callback, + Circuit *workspace, + std::vector *buf) { + assert(obs1.num_qubits == obs2.num_qubits); + + if (!obs1.commutes(obs2)) { + std::stringstream ss; + ss << "Attempted to CPP two anticommuting observables.\n"; + ss << " obs1: " << obs1 << "\n"; + ss << " obs2: " << obs2 << "\n"; + ss << " instruction: " << cpp_op; + throw std::invalid_argument(ss.str()); + } + + workspace->clear(); + auto apply_fixup = [&](CircuitInstruction inst) { + workspace->safe_append(inst); + obs1.do_instruction(inst); + obs2.do_instruction(inst); + }; + + auto reduce = [&](PauliStringRef<64> target_obs) { + // Turn all non-identity terms into Z terms. + for_each_active_qubit_in(target_obs, [&](uint32_t q) { + GateTarget t = GateTarget::qubit(q); + apply_fixup({target_obs.zs[q] ? GateType::H_YZ : GateType::H, {}, &t}); + }); + + // Cancel any extra Z terms. + uint64_t pivot = UINT64_MAX; + for_each_active_qubit_in(target_obs, [&](uint32_t q) { + if (pivot == UINT64_MAX) { + pivot = q; + } else { + std::array ts{GateTarget::qubit(q), GateTarget::qubit(pivot)}; + apply_fixup({GateType::CX, {}, ts}); + } + }); + + return pivot; + }; + + uint64_t pivot1 = reduce(obs1); + uint64_t pivot2 = reduce(obs2); + + if (pivot1 == pivot2 && pivot1 != UINT64_MAX) { + // Both observables had identical quantum parts (up to sign). + // If their sign differed, we should do nothing. + // If their sign matched, we should apply Z to obs1. + assert(obs1.xs == obs2.xs); + assert(obs1.zs == obs2.zs); + obs2.zs[pivot2] = false; + obs2.sign ^= obs1.sign; + obs2.sign ^= true; + pivot2 = UINT64_MAX; + } + assert(obs1.weight() <= 1); + assert(obs2.weight() <= 1); + assert((pivot1 == UINT64_MAX) == (obs1.weight() == 0)); + assert((pivot2 == UINT64_MAX) == (obs2.weight() == 0)); + assert(pivot1 == UINT64_MAX || obs1.xs[pivot1] + 2 * obs1.zs[pivot1] == 2); + assert(pivot1 == UINT64_MAX || obs2.xs[pivot1] + 2 * obs2.zs[pivot1] == 0); + assert(pivot2 == UINT64_MAX || obs1.xs[pivot2] + 2 * obs1.zs[pivot2] == 0); + assert(pivot2 == UINT64_MAX || obs2.xs[pivot2] + 2 * obs2.zs[pivot2] == 2); + + // Apply rewrites. + workspace->for_each_operation(do_instruction_callback); + + // Handle the quantum-quantum interaction. + if (pivot1 != UINT64_MAX && pivot2 != UINT64_MAX) { + assert(pivot1 != pivot2); + std::array ts{GateTarget::qubit(pivot1), GateTarget::qubit(pivot2)}; + do_instruction_callback({GateType::CZ, {}, ts}); + } + + // Handle sign and classical feedback into obs1. + if (pivot1 != UINT64_MAX) { + for (const auto &t : classical_bits2) { + std::array ts{t, GateTarget::qubit(pivot1)}; + do_instruction_callback({GateType::CZ, {}, ts}); + } + if (obs2.sign) { + GateTarget t = GateTarget::qubit(pivot1); + do_instruction_callback({GateType::Z, {}, &t}); + } + } + + // Handle sign and classical feedback into obs2. + if (pivot2 != UINT64_MAX) { + for (const auto &t : classical_bits1) { + std::array ts{t, GateTarget::qubit(pivot2)}; + do_instruction_callback({GateType::CZ, {}, ts}); + } + if (obs1.sign) { + GateTarget t = GateTarget::qubit(pivot2); + do_instruction_callback({GateType::Z, {}, &t}); + } + } + + // Undo rewrites. + workspace->for_each_operation_reverse([&](CircuitInstruction inst) { + assert(inst.args.empty()); + if (inst.gate_type == GateType::CX) { + buf->clear(); + for (size_t k = inst.targets.size(); k;) { + k -= 2; + buf->push_back(inst.targets[k]); + buf->push_back(inst.targets[k + 1]); + } + do_instruction_callback({GateType::CX, {}, *buf}); + } else { + assert(inst.gate_type == GATE_DATA[inst.gate_type].inverse().id); + do_instruction_callback(inst); + } + }); +} + +void stim::decompose_cpp_operation_with_reverse_independence( + const CircuitInstruction &cpp_op, + size_t num_qubits, + const std::function &do_instruction_callback) { + PauliString<64> obs1(num_qubits); + PauliString<64> obs2(num_qubits); + std::vector bits1; + std::vector bits2; + Circuit circuit_workspace; + std::vector target_buf; + + size_t start = 0; + while (true) { + bool b1 = accumulate_next_obs_terms_to_pauli_string_helper(cpp_op, &start, &obs1, &bits1); + bool b2 = accumulate_next_obs_terms_to_pauli_string_helper(cpp_op, &start, &obs2, &bits2); + if (!b2) { + break; + } + if (!b1) { + throw std::invalid_argument("Odd number of products."); + } + + decompose_cpp_operation_with_reverse_independence_helper( + cpp_op, obs1, obs2, bits1, bits2, do_instruction_callback, &circuit_workspace, &target_buf); + } +} + void stim::decompose_pair_instruction_into_segments_with_single_use_controls( const CircuitInstruction &inst, size_t num_qubits, const std::function &callback) { simd_bits<64> used_as_control(std::max(num_qubits, size_t{1})); @@ -709,6 +859,11 @@ struct Simplifier { simplify_instruction(sub); }); break; + case GateType::CPP: + decompose_cpp_operation_with_reverse_independence(inst, num_qubits, [&](const CircuitInstruction sub) { + simplify_instruction(sub); + }); + break; case GateType::SPP: case GateType::SPP_DAG: decompose_spp_or_spp_dag_operation(inst, num_qubits, false, [&](const CircuitInstruction sub) { diff --git a/src/stim/circuit/gate_decomposition.h b/src/stim/circuit/gate_decomposition.h index d033e083c..f0b1bd41c 100644 --- a/src/stim/circuit/gate_decomposition.h +++ b/src/stim/circuit/gate_decomposition.h @@ -59,6 +59,15 @@ void decompose_mpp_operation( size_t num_qubits, const std::function &do_instruction_callback); +/// Decomposes CPP operations into sequences of simpler operations with the same effect. +/// +/// The output is guaranteed to only use self-inverse operations, and to have the same +/// effect if run in order or in reverse order. +void decompose_cpp_operation_with_reverse_independence( + const CircuitInstruction &cpp_op, + size_t num_qubits, + const std::function &do_instruction_callback); + /// Decomposes SPP operations into sequences of simpler operations with the same effect. void decompose_spp_or_spp_dag_operation( const CircuitInstruction &spp_op, diff --git a/src/stim/circuit/gate_decomposition.test.cc b/src/stim/circuit/gate_decomposition.test.cc index 36051a43f..c56bec973 100644 --- a/src/stim/circuit/gate_decomposition.test.cc +++ b/src/stim/circuit/gate_decomposition.test.cc @@ -156,6 +156,179 @@ TEST(gate_decomposition, decompose_pair_instruction_into_segments_with_single_us )CIRCUIT")); } +TEST(gate_decomposition, decompose_cpp_operation_with_reverse_independence_swap) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP X0*X1 Z0*Z1").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + H 0 1 + CX 1 0 + H 1 + CZ 0 1 + H 1 + CX 1 0 + H 0 1 + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_with_reverse_independence_swap_big_qubit) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP X0*X1001 Z0*Z1001").operations[0], 1002, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + H 0 1001 + CX 1001 0 + H 1001 + CZ 0 1001 + H 1001 + CX 1001 0 + H 0 1001 + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_classical_feedback) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP rec[-1] X0*Y1*Z2").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + H 0 + H_YZ 1 + CX 1 0 2 0 + CZ rec[-1] 0 + CX 2 0 1 0 + H_YZ 1 + H 0 + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_same_obs) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP Z0 Z0").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + Z 0 + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_opposite_obs) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP Z0 !Z0").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_same_obs_complex) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP X0*Y1*Z2 X0*Y1*Z2").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + H 0 + H_YZ 1 + CX 1 0 2 0 + Z 0 + CX 2 0 1 0 + H_YZ 1 + H 0 + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_opposite_obs_complex) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP X0*Y1*Z2 !X0*Y1*Z2").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + H 0 + H_YZ 1 + CX 1 0 2 0 2 0 1 0 + H_YZ 1 + H 0 + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_inversion_1) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP !Z0 Z1").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + CZ 0 1 + Z 1 + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_inversion_2) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP Z0 !Z1").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + CZ 0 1 + Z 0 + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_inversion_3) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP !Z0 !Z1").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + CZ 0 1 + Z 0 1 + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_only_classical) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP rec[-1] rec[-1]").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_only_classical_complex) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP X0*Y0*Z0*X1*Y1*Z1*rec[-1] rec[-1]").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + )CIRCUIT")); +} + +TEST(gate_decomposition, decompose_cpp_operation_mixed_quantum_classical) { + Circuit out; + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP rec[-1]*Z0 rec[-2]*X1").operations[0], 10, [&](const CircuitInstruction &inst) { + out.safe_append(inst); + }); + ASSERT_EQ(out, Circuit(R"CIRCUIT( + H 1 + CZ 0 1 rec[-2] 0 rec[-1] 1 + H 1 + )CIRCUIT")); +} + TEST(gate_decomposition, decompose_spp_or_spp_dag_operation_simple) { Circuit out; decompose_spp_or_spp_dag_operation(Circuit("SPP Z0").operations[0], 10, false, [&](const CircuitInstruction &inst) { @@ -281,6 +454,32 @@ TEST(gate_decomposition, decompose_spp_or_spp_dag_operation_bad) { std::invalid_argument); } +TEST(gate_decomposition, decompose_cpp_operation_bad) { + ASSERT_THROW( + { + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP X0*Z0 X0").operations[0], 10, [](const CircuitInstruction &inst) { + }); + }, + std::invalid_argument); + + ASSERT_THROW( + { + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP X0 Z0").operations[0], 10, [](const CircuitInstruction &inst) { + }); + }, + std::invalid_argument); + + ASSERT_THROW( + { + decompose_cpp_operation_with_reverse_independence( + Circuit("CPP X0*X1 Z0*X1").operations[0], 10, [](const CircuitInstruction &inst) { + }); + }, + std::invalid_argument); +} + static std::pair>, std::vector>> circuit_output_eq_val( const Circuit &circuit) { // CAUTION: this is not 100% reliable when measurement count is larger than 1. diff --git a/src/stim/cmd/command_help.cc b/src/stim/cmd/command_help.cc index 24026ad72..cb03cd090 100644 --- a/src/stim/cmd/command_help.cc +++ b/src/stim/cmd/command_help.cc @@ -229,6 +229,15 @@ std::vector stim::gate_decomposition_help_targets_for_gate_type(Gate GateTarget::combiner(), GateTarget::z(2), }; + } else if (g == GateType::CPP) { + return { + GateTarget::x(0), + GateTarget::combiner(), + GateTarget::y(1), + GateTarget::z(2), + GateTarget::combiner(), + GateTarget::z(3), + }; } else if (g == GateType::DETECTOR || g == GateType::OBSERVABLE_INCLUDE) { return {GateTarget::rec(-1)}; } else if (g == GateType::TICK || g == GateType::SHIFT_COORDS) { diff --git a/src/stim/diagram/gate_data_3d.cc b/src/stim/diagram/gate_data_3d.cc index 4d50bac35..513b7fb9f 100644 --- a/src/stim/diagram/gate_data_3d.cc +++ b/src/stim/diagram/gate_data_3d.cc @@ -459,6 +459,22 @@ std::map> stim_draw_internal::make_gate_p f("SPP_DAG:Y", 4, 10), f("SPP_DAG:Z", 5, 10), + f("CPP[I:X]", 0, 11), + f("CPP[I:Y]", 1, 11), + f("CPP[I:Z]", 2, 11), + f("CPP[X:I]", 3, 11), + f("CPP[X:X]", 4, 11), + f("CPP[X:Y]", 5, 11), + f("CPP[X:Z]", 6, 11), + f("CPP[Y:I]", 7, 11), + f("CPP[Y:X]", 8, 11), + f("CPP[Y:Y]", 9, 11), + f("CPP[Y:Z]", 10, 11), + f("CPP[Z:I]", 11, 11), + f("CPP[Z:X]", 12, 11), + f("CPP[Z:Y]", 13, 11), + f("CPP[Z:Z]", 14, 11), + make_x_control_mesh(), make_y_control_mesh(), make_z_control_mesh(), diff --git a/src/stim/diagram/gate_data_svg.cc b/src/stim/diagram/gate_data_svg.cc index d54a95693..6f342e6c4 100644 --- a/src/stim/diagram/gate_data_svg.cc +++ b/src/stim/diagram/gate_data_svg.cc @@ -57,6 +57,22 @@ std::map SvgGateData::make_gate_data_map() { result.insert({"SPP_DAG[Y]", {1, "SPP†", "Y", "", "black", "white", 0, 10, 0}}); result.insert({"SPP_DAG[Z]", {1, "SPP†", "Z", "", "black", "white", 0, 10, 0}}); + result.insert({"CPP[I:X]", {1, "CPP", "I", "X", "black", "white", 0, 10, 0}}); + result.insert({"CPP[I:Y]", {1, "CPP", "I", "Y", "black", "white", 0, 10, 0}}); + result.insert({"CPP[I:Z]", {1, "CPP", "I", "Z", "black", "white", 0, 10, 0}}); + result.insert({"CPP[X:I]", {1, "CPP", "X", "I", "black", "white", 0, 10, 0}}); + result.insert({"CPP[X:X]", {1, "CPP", "X", "X", "black", "white", 0, 10, 0}}); + result.insert({"CPP[X:Y]", {1, "CPP", "X", "Y", "black", "white", 0, 10, 0}}); + result.insert({"CPP[X:Z]", {1, "CPP", "X", "Z", "black", "white", 0, 10, 0}}); + result.insert({"CPP[Y:I]", {1, "CPP", "Y", "I", "black", "white", 0, 10, 0}}); + result.insert({"CPP[Y:X]", {1, "CPP", "Y", "X", "black", "white", 0, 10, 0}}); + result.insert({"CPP[Y:Y]", {1, "CPP", "Y", "Y", "black", "white", 0, 10, 0}}); + result.insert({"CPP[Y:Z]", {1, "CPP", "Y", "Z", "black", "white", 0, 10, 0}}); + result.insert({"CPP[Z:I]", {1, "CPP", "Z", "I", "black", "white", 0, 10, 0}}); + result.insert({"CPP[Z:X]", {1, "CPP", "Z", "X", "black", "white", 0, 10, 0}}); + result.insert({"CPP[Z:Y]", {1, "CPP", "Z", "Y", "black", "white", 0, 10, 0}}); + result.insert({"CPP[Z:Z]", {1, "CPP", "Z", "Z", "black", "white", 0, 10, 0}}); + result.insert({"SQRT_XX", {1, "√XX", "", "", "white", "black", 0, 10, 0}}); result.insert({"SQRT_YY", {1, "√YY", "", "", "white", "black", 0, 10, 0}}); result.insert({"SQRT_ZZ", {1, "√ZZ", "", "", "white", "black", 0, 10, 0}}); diff --git a/src/stim/diagram/timeline/timeline_3d_drawer.cc b/src/stim/diagram/timeline/timeline_3d_drawer.cc index a539d60b6..31fbf4852 100644 --- a/src/stim/diagram/timeline/timeline_3d_drawer.cc +++ b/src/stim/diagram/timeline/timeline_3d_drawer.cc @@ -273,10 +273,67 @@ void DiagramTimeline3DDrawer::do_mpp(const ResolvedTimelineOperation &op) { do_multi_qubit_gate_with_pauli_targets(op); } +void DiagramTimeline3DDrawer::do_multi_qubit_gate_with_paired_pauli_targets(const ResolvedTimelineOperation &op) { + reserve_drawing_room_for_targets(op.targets); + + PauliString<64> obs1(num_qubits); + PauliString<64> obs2(num_qubits); + std::vector bits1; + std::vector bits2; + + size_t start = 0; + accumulate_next_obs_terms_to_pauli_string_helper( + CircuitInstruction{op.gate_type, op.args, op.targets}, &start, &obs1, &bits1, true); + accumulate_next_obs_terms_to_pauli_string_helper( + CircuitInstruction{op.gate_type, op.args, op.targets}, &start, &obs2, &bits2, true); + + Coord<3> prev{}; + bool has_prev = false; + const auto &gate_data = GATE_DATA[op.gate_type]; + for (size_t q = 0; q < num_qubits; q++) { + uint8_t p1 = obs1.xs[q] + obs1.zs[q] * 2; + uint8_t p2 = obs2.xs[q] + obs2.zs[q] * 2; + if (p1 | p2) { + std::stringstream ss; + ss << gate_data.name; + ss << "["; + ss << "IXZY"[p1]; + // TODO: include bits. + // for (auto t : bits1) { + // ss << "*" << t; + // } + bits1.clear(); + ss << ":"; + ss << "IXZY"[p2]; + // TODO: include bits + // for (auto t : bits2) { + // ss << "*" << t; + // } + bits2.clear(); + ss << "]"; + // TODO: include args + // if (!op.args.empty()) { + // ss << "(" << comma_sep(op.args, ",") << ")"; + // } + auto center = mq2xyz(cur_moment, q); + diagram_out.elements.push_back({ss.str(), center}); + if (has_prev) { + draw_gate_connecting_line(center, prev); + } + prev = center; + has_prev = true; + } + } +} + void DiagramTimeline3DDrawer::do_spp(const ResolvedTimelineOperation &op) { do_multi_qubit_gate_with_pauli_targets(op); } +void DiagramTimeline3DDrawer::do_cpp(const ResolvedTimelineOperation &op) { + do_multi_qubit_gate_with_paired_pauli_targets(op); +} + void DiagramTimeline3DDrawer::do_correlated_error(const ResolvedTimelineOperation &op) { if (cur_moment_is_used) { start_next_moment(); @@ -305,6 +362,8 @@ void DiagramTimeline3DDrawer::do_resolved_operation(const ResolvedTimelineOperat do_mpp(op); } else if (op.gate_type == GateType::SPP || op.gate_type == GateType::SPP_DAG) { do_spp(op); + } else if (op.gate_type == GateType::CPP) { + do_cpp(op); } else if (op.gate_type == GateType::DETECTOR) { do_detector(op); } else if (op.gate_type == GateType::OBSERVABLE_INCLUDE) { diff --git a/src/stim/diagram/timeline/timeline_3d_drawer.h b/src/stim/diagram/timeline/timeline_3d_drawer.h index 82d16d67e..435cc3085 100644 --- a/src/stim/diagram/timeline/timeline_3d_drawer.h +++ b/src/stim/diagram/timeline/timeline_3d_drawer.h @@ -60,6 +60,7 @@ struct DiagramTimeline3DDrawer { void do_multi_qubit_gate_with_paired_pauli_targets(const ResolvedTimelineOperation &op); void do_mpp(const ResolvedTimelineOperation &op); void do_spp(const ResolvedTimelineOperation &op); + void do_cpp(const ResolvedTimelineOperation &op); void do_correlated_error(const ResolvedTimelineOperation &op); void do_qubit_coords(const ResolvedTimelineOperation &op); void do_else_correlated_error(const ResolvedTimelineOperation &op); diff --git a/src/stim/diagram/timeline/timeline_ascii_drawer.cc b/src/stim/diagram/timeline/timeline_ascii_drawer.cc index cdff8e75b..56d8e2a39 100644 --- a/src/stim/diagram/timeline/timeline_ascii_drawer.cc +++ b/src/stim/diagram/timeline/timeline_ascii_drawer.cc @@ -299,6 +299,56 @@ void DiagramTimelineAsciiDrawer::do_multi_qubit_gate_with_pauli_targets(const Re } } +void DiagramTimelineAsciiDrawer::do_multi_qubit_gate_with_paired_pauli_targets(const ResolvedTimelineOperation &op) { + reserve_drawing_room_for_targets(op.targets); + + PauliString<64> obs1(num_qubits); + PauliString<64> obs2(num_qubits); + std::vector bits1; + std::vector bits2; + + size_t start = 0; + accumulate_next_obs_terms_to_pauli_string_helper( + CircuitInstruction{op.gate_type, op.args, op.targets}, &start, &obs1, &bits1, true); + accumulate_next_obs_terms_to_pauli_string_helper( + CircuitInstruction{op.gate_type, op.args, op.targets}, &start, &obs2, &bits2, true); + + const auto &gate_data = GATE_DATA[op.gate_type]; + for (size_t q = 0; q < num_qubits; q++) { + uint8_t p1 = obs1.xs[q] + obs1.zs[q] * 2; + uint8_t p2 = obs2.xs[q] + obs2.zs[q] * 2; + if (p1 | p2) { + std::stringstream ss; + ss << gate_data.name; + ss << "["; + ss << "IXZY"[p1]; + for (auto t : bits1) { + ss << "*" << t; + } + bits1.clear(); + ss << ":"; + ss << "IXZY"[p2]; + for (auto t : bits2) { + ss << "*" << t; + } + bits2.clear(); + ss << "]"; + if (!op.args.empty()) { + ss << "(" << comma_sep(op.args, ",") << ")"; + } + diagram.add_entry(AsciiDiagramEntry{ + { + m2x(cur_moment), + q2y(q), + GATE_ALIGNMENT_X, + GATE_ALIGNMENT_Y, + }, + ss.str(), + }); + } + } +} + void DiagramTimelineAsciiDrawer::do_start_repeat(const CircuitTimelineLoopData &loop_data) { if (cur_moment_is_used) { do_tick(); @@ -438,6 +488,8 @@ void DiagramTimelineAsciiDrawer::do_observable_include(const ResolvedTimelineOpe void DiagramTimelineAsciiDrawer::do_resolved_operation(const ResolvedTimelineOperation &op) { if (op.gate_type == GateType::MPP || op.gate_type == GateType::SPP || op.gate_type == GateType::SPP_DAG) { do_multi_qubit_gate_with_pauli_targets(op); + } else if (op.gate_type == GateType::CPP) { + do_multi_qubit_gate_with_paired_pauli_targets(op); } else if (op.gate_type == GateType::DETECTOR) { do_detector(op); } else if (op.gate_type == GateType::OBSERVABLE_INCLUDE) { diff --git a/src/stim/diagram/timeline/timeline_ascii_drawer.test.cc b/src/stim/diagram/timeline/timeline_ascii_drawer.test.cc index f75dc4f91..a0f068aec 100644 --- a/src/stim/diagram/timeline/timeline_ascii_drawer.test.cc +++ b/src/stim/diagram/timeline/timeline_ascii_drawer.test.cc @@ -521,17 +521,17 @@ TEST(circuit_diagram_timeline_text, test_circuit_all_ops) { | | | | | | | q2: ---------------------Y-H_XY--SQRT_Y-----ISWAP-----SQRT_XX_DAG-X-E[Y](0.01)-DEPOLARIZE2(0.03)---------------Z_ERROR(0.03)------------------------------------------------------------------------------------------------MPP[Z]:rec[2]---------------SPP[Z]-SPP_DAG[Z]-SPP_DAG[X]-MR:rec[6]--MXX:rec[12]-|------------|--------------------------------------------------------------------------------------------------------------------------------MZZ:rec[22]----Z^rec[24]-- | | | | | | | | - q3: ---------------------Z-H-----SQRT_Y_DAG-ISWAP-----SQRT_XX_DAG-Y-E[Z](0.01)-PAULI_CHANNEL_1(0.01,0.02,0.03)-HERALDED_ERASE(0.04):rec[0]--------------------------------------------------------------------------------------------------------------SPP[X]-----------------------MX:rec[7]--MXX---------|------------|--------------------------------------------------------------------------------------------------------------------------------MZZ----------------------- - | | - q4: -----------------------H_YZ--S----------ISWAP_DAG-SQRT_YY-----X------------ELSE_CORRELATED_ERROR[X](0.02)--PAULI_CHANNEL_2[0](0.001,0.002,0.003,0.004,0.005,0.006,0.007,0.008,0.009,0.01,0.011,0.012,0.013,0.014,0.015)----------------------------------------------------------MY:rec[8]--MYY:rec[13]-|------------|--------------------------------------------------------------------------------------------------------------------------------MYY:rec[23]--------------- - | | | | | | | | | - q5: -----------------------------S_DAG------ISWAP_DAG-SQRT_YY-----@------------|-------------------------------PAULI_CHANNEL_2[1](0.001,0.002,0.003,0.004,0.005,0.006,0.007,0.008,0.009,0.01,0.011,0.012,0.013,0.014,0.015)----------------------------------------------------------M:rec[9]---MYY---------|------------|--------------------------------------------------------------------------------------------------------------------------------MYY----------------------- - | | | - q6: ----------------------------------------SWAP------SQRT_YY_DAG-Y------------ELSE_CORRELATED_ERROR[Z](0.02)--HERALDED_PAULI_CHANNEL_1(0.01,0.02,0.03,0.04):rec[1]------------------------------------------------------------------------------------------------------------------M:rec[10]--MZZ:rec[14]-|------------|--------------------------------------------------------------------------------------------------------------------------------MPP[X]:rec[24]------------ + q3: ---------------------Z-H-----SQRT_Y_DAG-ISWAP-----SQRT_XX_DAG-Y-E[Z](0.01)-PAULI_CHANNEL_1(0.01,0.02,0.03)-HERALDED_ERASE(0.04):rec[0]------------------------------------------------------------------------------------------------CPP[X:Z]------SPP[X]-----------------------MX:rec[7]--MXX---------|------------|--------------------------------------------------------------------------------------------------------------------------------MZZ----------------------- + | | | + q4: -----------------------H_YZ--S----------ISWAP_DAG-SQRT_YY-----X------------ELSE_CORRELATED_ERROR[X](0.02)--PAULI_CHANNEL_2[0](0.001,0.002,0.003,0.004,0.005,0.006,0.007,0.008,0.009,0.01,0.011,0.012,0.013,0.014,0.015)---------------CPP[X:Z]-----------------------------------MY:rec[8]--MYY:rec[13]-|------------|--------------------------------------------------------------------------------------------------------------------------------MYY:rec[23]--------------- + | | | | | | | | | | + q5: -----------------------------S_DAG------ISWAP_DAG-SQRT_YY-----@------------|-------------------------------PAULI_CHANNEL_2[1](0.001,0.002,0.003,0.004,0.005,0.006,0.007,0.008,0.009,0.01,0.011,0.012,0.013,0.014,0.015)---------------CPP[X:I]-----------------------------------M:rec[9]---MYY---------|------------|--------------------------------------------------------------------------------------------------------------------------------MYY----------------------- + | | | | + q6: ----------------------------------------SWAP------SQRT_YY_DAG-Y------------ELSE_CORRELATED_ERROR[Z](0.02)--HERALDED_PAULI_CHANNEL_1(0.01,0.02,0.03,0.04):rec[1]-----------------------------------------------------------------------CPP[I:Y]-----------------------------------M:rec[10]--MZZ:rec[14]-|------------|--------------------------------------------------------------------------------------------------------------------------------MPP[X]:rec[24]------------ | | | | | | | | - q7: ----------------------------------------SWAP------SQRT_YY_DAG-X------------ELSE_CORRELATED_ERROR[Y](0.02)------------------------------------------------------------------------------------------------------------------------------------------------------------------------RX---------MZZ---------|------------|--------------------------------------------------------------------------------------------------------------------------------MPP[Y]:rec[24]------------ - | | | - q8: ----------------------------------------XSWAP-----SQRT_ZZ-----Y------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------RY---------------------|------------|--------------------------------------------------------------------------------------------------------------------------------MPP[Z]:rec[24]------------ + q7: ----------------------------------------SWAP------SQRT_YY_DAG-X------------ELSE_CORRELATED_ERROR[Y](0.02)-----------------------------------------------------------------------------------------------------------------------------CPP[Y:I]-----------------------------------RX---------MZZ---------|------------|--------------------------------------------------------------------------------------------------------------------------------MPP[Y]:rec[24]------------ + | | | | + q8: ----------------------------------------XSWAP-----SQRT_ZZ-----Y-----------------------------------------------------------------------------------------------------------------------------------------------------------------------CPP[I:Y]-----------------------------------RY---------------------|------------|--------------------------------------------------------------------------------------------------------------------------------MPP[Z]:rec[24]------------ | | | | | q9: ----------------------------------------ZSWAP-----SQRT_ZZ-----Y------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------R----------------------|------------|---------------------------------------------------------------------------------------------------------------------------------------------------------- | | diff --git a/src/stim/diagram/timeline/timeline_svg_drawer.cc b/src/stim/diagram/timeline/timeline_svg_drawer.cc index 4bcf1f00c..e074bb6e7 100644 --- a/src/stim/diagram/timeline/timeline_svg_drawer.cc +++ b/src/stim/diagram/timeline/timeline_svg_drawer.cc @@ -638,6 +638,59 @@ void DiagramTimelineSvgDrawer::do_spp(const ResolvedTimelineOperation &op) { do_multi_qubit_gate_with_pauli_targets(op); } +void DiagramTimelineSvgDrawer::do_multi_qubit_gate_with_paired_pauli_targets(const ResolvedTimelineOperation &op) { + reserve_drawing_room_for_targets(op.targets); + auto minmax_q = compute_minmax_q(op.targets); + + PauliString<64> obs1(num_qubits); + PauliString<64> obs2(num_qubits); + std::vector bits1; + std::vector bits2; + + size_t start = 0; + accumulate_next_obs_terms_to_pauli_string_helper( + CircuitInstruction{op.gate_type, op.args, op.targets}, &start, &obs1, &bits1, true); + accumulate_next_obs_terms_to_pauli_string_helper( + CircuitInstruction{op.gate_type, op.args, op.targets}, &start, &obs2, &bits2, true); + + const auto &gate_data = GATE_DATA[op.gate_type]; + for (size_t q = 0; q < num_qubits; q++) { + uint8_t p1 = obs1.xs[q] + obs1.zs[q] * 2; + uint8_t p2 = obs2.xs[q] + obs2.zs[q] * 2; + if (p1 | p2) { + std::stringstream ss; + ss << gate_data.name; + ss << "["; + ss << "IXZY"[p1]; + // TODO: show bits + // for (auto t : bits1) { + // ss << "*" << t; + // } + bits1.clear(); + ss << ":"; + ss << "IXZY"[p2]; + // TODO: show bits + // for (auto t : bits2) { + // ss << "*" << t; + // } + bits2.clear(); + ss << "]"; + if (!op.args.empty()) { + ss << "(" << comma_sep(op.args, ",") << ")"; + } + auto c = q2xy(q); + draw_generic_box(c.xyz[0], c.xyz[1], ss.str(), q == minmax_q.second ? op.args : SpanRef{}); + if (gate_data.flags & GATE_PRODUCES_RESULTS && q == minmax_q.first) { + draw_rec(c.xyz[0], c.xyz[1]); + } + } + } +} + +void DiagramTimelineSvgDrawer::do_cpp(const ResolvedTimelineOperation &op) { + do_multi_qubit_gate_with_paired_pauli_targets(op); +} + void DiagramTimelineSvgDrawer::do_correlated_error(const ResolvedTimelineOperation &op) { if (cur_moment_is_used) { start_next_moment(); @@ -757,6 +810,8 @@ void DiagramTimelineSvgDrawer::do_resolved_operation(const ResolvedTimelineOpera do_mpp(op); } else if (op.gate_type == GateType::SPP || op.gate_type == GateType::SPP_DAG) { do_spp(op); + } else if (op.gate_type == GateType::CPP) { + do_cpp(op); } else if (op.gate_type == GateType::DETECTOR) { do_detector(op); } else if (op.gate_type == GateType::OBSERVABLE_INCLUDE) { diff --git a/src/stim/diagram/timeline/timeline_svg_drawer.h b/src/stim/diagram/timeline/timeline_svg_drawer.h index a9c9d1fbd..fae6862ac 100644 --- a/src/stim/diagram/timeline/timeline_svg_drawer.h +++ b/src/stim/diagram/timeline/timeline_svg_drawer.h @@ -99,6 +99,7 @@ struct DiagramTimelineSvgDrawer { void do_multi_qubit_gate_with_pauli_targets(const ResolvedTimelineOperation &op); void do_multi_qubit_gate_with_paired_pauli_targets(const ResolvedTimelineOperation &op); void do_mpp(const ResolvedTimelineOperation &op); + void do_cpp(const ResolvedTimelineOperation &op); void do_spp(const ResolvedTimelineOperation &op); void do_correlated_error(const ResolvedTimelineOperation &op); void do_qubit_coords(const ResolvedTimelineOperation &op); diff --git a/src/stim/gates/gate_data_pauli_product.cc b/src/stim/gates/gate_data_pauli_product.cc index 498f9b68b..1dbd4f8aa 100644 --- a/src/stim/gates/gate_data_pauli_product.cc +++ b/src/stim/gates/gate_data_pauli_product.cc @@ -89,6 +89,82 @@ M 0 3 CX 2 0 1 0 4 3 H 0 1 3 4 S 1 +)CIRCUIT", + }); + + add_gate( + failed, + Gate{ + .name = "CPP", + .id = GateType::CPP, + .best_candidate_inverse_id = GateType::CPP, + .arg_count = 0, + .flags = (GateFlags)(GATE_CAN_TARGET_BITS | GATE_TARGETS_PAULI_STRING | GATE_TARGETS_COMBINERS | + GATE_TARGETS_PAIRS), + .category = "P_Generalized Pauli Product Gates", + .help = R"MARKDOWN( +The generalized CNOT gate. Negates states in the intersection of Pauli product observables. + +Parens Arguments: + + This instruction takes no parens arguments. + +Targets: + + A series of pairs of Pauli products to intersect. + + Each Pauli product is a series of Pauli targets (`[XYZ]#`), record targets (`rec[-#]`), + or sweep targets (`sweep[#]`) separated by combiners (`*`). Each product can be negated + by prefixing a Pauli target in the product with an inverter (`!`). + + The number of products must be even. CPP X1 Y2 Z3 isn't allowed. + Within each pair of products, the pair must commute. CPP X1 Z1 isn't allowed. + +Examples: + + # Perform a CNOT gate with qubit 1 as the control and qubit 2 as the target. + CPP X1 Z2 + + # Perform a CZ gate between qubit 2 and qubit 5, then between qubit 3 and 4. + CPP Z2 Z5 Z3 Z4 + + # Perform many CX gates, all controlled by qubit 2, targeting qubits 5 through 10. + CPP Z2 X5*X6*X7*X8*X9*X10 + + # Swap qubit 1 and qubit 5 by negating their overlap with the singlet state. + CPP X1*X5 Z1*Z5 + + # Negate the amplitude of the |00> state. + CPP !Z0 !Z1 + + # Measure qubit 0 and do Pauli operations conditioned on the measurement returning TRUE. + M 0 + CPP rec[-1] X1*Y2*Z3 + +)MARKDOWN", + .unitary_data = {}, + .flow_data = + { + // For "CPP X0*Y1 Z2*Z3" + "X___ -> X___", + "Z___ -> Z_ZZ", + "_X__ -> _XZZ", + "_Z__ -> _ZZZ", + "__X_ -> XYX_", + "__Z_ -> __Z_", + "___X -> XY_X", + "___Z -> ___Z", + }, + .h_s_cx_m_r_decomposition = R"CIRCUIT( +CX 3 2 +CX 1 0 +S 1 +S 1 +S 1 +CX 2 1 +S 1 +CX 1 0 +CX 3 2 )CIRCUIT", }); diff --git a/src/stim/gates/gates.h b/src/stim/gates/gates.h index a352e6d1b..e4577f657 100644 --- a/src/stim/gates/gates.h +++ b/src/stim/gates/gates.h @@ -76,7 +76,7 @@ constexpr inline uint16_t gate_name_to_hash(const char *c) { return gate_name_to_hash(c, std::char_traits::length(c)); } -constexpr const size_t NUM_DEFINED_GATES = 70; +constexpr const size_t NUM_DEFINED_GATES = 71; enum class GateType : uint8_t { NOT_A_GATE = 0, @@ -150,6 +150,7 @@ enum class GateType : uint8_t { SQRT_ZZ_DAG, // Pauli product gates MPP, + CPP, SPP, SPP_DAG, // Swap gates diff --git a/src/stim/py/stim_pybind_test.py b/src/stim/py/stim_pybind_test.py index 866dbae55..da1c0dc85 100644 --- a/src/stim/py/stim_pybind_test.py +++ b/src/stim/py/stim_pybind_test.py @@ -61,7 +61,7 @@ def test_targets(): def test_gate_data(): data = stim.gate_data() - assert len(data) == 69 + assert len(data) == 70 assert data["CX"].name == "CX" assert data["CX"].aliases == ["CNOT", "CX", "ZCX"] assert data["X"].is_unitary diff --git a/src/stim/simulators/error_analyzer.cc b/src/stim/simulators/error_analyzer.cc index 1259169a8..6f744d6a5 100644 --- a/src/stim/simulators/error_analyzer.cc +++ b/src/stim/simulators/error_analyzer.cc @@ -80,6 +80,9 @@ void ErrorAnalyzer::undo_gate(const CircuitInstruction &inst) { case GateType::MPP: undo_MPP(inst); break; + case GateType::CPP: + undo_CPP(inst); + break; case GateType::SPP: case GateType::SPP_DAG: undo_SPP(inst); @@ -1608,6 +1611,21 @@ void ErrorAnalyzer::undo_MPP(const CircuitInstruction &target_data) { }); } +void ErrorAnalyzer::undo_CPP(const CircuitInstruction &target_data) { + size_t n = target_data.targets.size(); + std::vector reversed_targets(n); + std::vector reversed_measure_targets; + for (size_t k = 0; k < n; k++) { + reversed_targets[k] = target_data.targets[n - k - 1]; + } + decompose_cpp_operation_with_reverse_independence( + CircuitInstruction{GateType::CPP, target_data.args, reversed_targets}, + tracker.xs.size(), + [&](const CircuitInstruction &inst) { + undo_gate(inst); + }); +} + void ErrorAnalyzer::undo_SPP(const CircuitInstruction &target_data) { size_t n = target_data.targets.size(); std::vector reversed_targets(n); diff --git a/src/stim/simulators/error_analyzer.h b/src/stim/simulators/error_analyzer.h index f55178f1d..cf4c27920 100644 --- a/src/stim/simulators/error_analyzer.h +++ b/src/stim/simulators/error_analyzer.h @@ -172,6 +172,7 @@ struct ErrorAnalyzer { void undo_MY(const CircuitInstruction &inst); void undo_MZ(const CircuitInstruction &inst); void undo_MPP(const CircuitInstruction &inst); + void undo_CPP(const CircuitInstruction &inst); void undo_SPP(const CircuitInstruction &inst); void undo_MXX(const CircuitInstruction &inst); void undo_MYY(const CircuitInstruction &inst); diff --git a/src/stim/simulators/frame_simulator.h b/src/stim/simulators/frame_simulator.h index 3f076d3aa..0d154444b 100644 --- a/src/stim/simulators/frame_simulator.h +++ b/src/stim/simulators/frame_simulator.h @@ -123,6 +123,7 @@ struct FrameSimulator { void do_CZSWAP(const CircuitInstruction &inst); void do_SWAPCX(const CircuitInstruction &inst); void do_MPP(const CircuitInstruction &inst); + void do_CPP(const CircuitInstruction &inst); void do_SPP(const CircuitInstruction &inst); void do_SPP_DAG(const CircuitInstruction &inst); void do_MXX(const CircuitInstruction &inst); diff --git a/src/stim/simulators/frame_simulator.inl b/src/stim/simulators/frame_simulator.inl index 8eaef815d..c8b0a2ade 100644 --- a/src/stim/simulators/frame_simulator.inl +++ b/src/stim/simulators/frame_simulator.inl @@ -693,6 +693,16 @@ void FrameSimulator::do_MPP(const CircuitInstruction &target_data) { }); } +template +void FrameSimulator::do_CPP(const CircuitInstruction &target_data) { + decompose_cpp_operation_with_reverse_independence( + target_data, + num_qubits, + [&](const CircuitInstruction &inst) { + safe_do_instruction(inst); + }); +} + template void FrameSimulator::do_SPP(const CircuitInstruction &target_data) { decompose_spp_or_spp_dag_operation( @@ -953,6 +963,9 @@ void FrameSimulator::do_gate(const CircuitInstruction &inst) { case GateType::MPP: do_MPP(inst); break; + case GateType::CPP: + do_CPP(inst); + break; case GateType::SPP: do_SPP(inst); break; diff --git a/src/stim/simulators/sparse_rev_frame_tracker.cc b/src/stim/simulators/sparse_rev_frame_tracker.cc index 7521a0fee..3b9e0c704 100644 --- a/src/stim/simulators/sparse_rev_frame_tracker.cc +++ b/src/stim/simulators/sparse_rev_frame_tracker.cc @@ -56,6 +56,9 @@ void SparseUnsignedRevFrameTracker::undo_gate(const CircuitInstruction &inst) { case GateType::MPP: undo_MPP(inst); break; + case GateType::CPP: + undo_CPP(inst); + break; case GateType::SPP: case GateType::SPP_DAG: undo_SPP(inst); @@ -356,6 +359,21 @@ void SparseUnsignedRevFrameTracker::undo_SPP(const CircuitInstruction &target_da }); } +void SparseUnsignedRevFrameTracker::undo_CPP(const CircuitInstruction &target_data) { + size_t n = target_data.targets.size(); + std::vector reversed_targets(n); + std::vector reversed_measure_targets; + for (size_t k = 0; k < n; k++) { + reversed_targets[k] = target_data.targets[n - k - 1]; + } + decompose_cpp_operation_with_reverse_independence( + CircuitInstruction{target_data.gate_type, target_data.args, reversed_targets}, + xs.size(), + [&](const CircuitInstruction &inst) { + undo_gate(inst); + }); +} + void SparseUnsignedRevFrameTracker::clear_qubits(const CircuitInstruction &dat) { for (size_t k = dat.targets.size(); k-- > 0;) { auto q = dat.targets[k].qubit_value(); diff --git a/src/stim/simulators/sparse_rev_frame_tracker.h b/src/stim/simulators/sparse_rev_frame_tracker.h index e9f3e4dee..599418d57 100644 --- a/src/stim/simulators/sparse_rev_frame_tracker.h +++ b/src/stim/simulators/sparse_rev_frame_tracker.h @@ -85,6 +85,7 @@ struct SparseUnsignedRevFrameTracker { void undo_MY(const CircuitInstruction &inst); void undo_MZ(const CircuitInstruction &inst); void undo_MPP(const CircuitInstruction &inst); + void undo_CPP(const CircuitInstruction &inst); void undo_SPP(const CircuitInstruction &inst); void undo_MXX(const CircuitInstruction &inst); void undo_MYY(const CircuitInstruction &inst); diff --git a/src/stim/simulators/tableau_simulator.h b/src/stim/simulators/tableau_simulator.h index f9c974a92..9d6a96416 100644 --- a/src/stim/simulators/tableau_simulator.h +++ b/src/stim/simulators/tableau_simulator.h @@ -154,6 +154,7 @@ struct TableauSimulator { void do_CORRELATED_ERROR(const CircuitInstruction &inst); void do_ELSE_CORRELATED_ERROR(const CircuitInstruction &inst); void do_MPP(const CircuitInstruction &inst); + void do_CPP(const CircuitInstruction &inst); void do_SPP(const CircuitInstruction &inst); void do_SPP_DAG(const CircuitInstruction &inst); void do_MXX(const CircuitInstruction &inst); diff --git a/src/stim/simulators/tableau_simulator.inl b/src/stim/simulators/tableau_simulator.inl index 58f002532..4d73b99f6 100644 --- a/src/stim/simulators/tableau_simulator.inl +++ b/src/stim/simulators/tableau_simulator.inl @@ -65,6 +65,16 @@ void TableauSimulator::do_MPP(const CircuitInstruction &target_data) { }); } +template +void TableauSimulator::do_CPP(const CircuitInstruction &target_data) { + decompose_cpp_operation_with_reverse_independence( + target_data, + inv_state.num_qubits, + [&](const CircuitInstruction &inst) { + do_gate(inst); + }); +} + template void TableauSimulator::do_SPP(const CircuitInstruction &target_data) { decompose_spp_or_spp_dag_operation( @@ -1558,6 +1568,9 @@ void TableauSimulator::do_gate(const CircuitInstruction &inst) { case GateType::MPP: do_MPP(inst); break; + case GateType::CPP: + do_CPP(inst); + break; case GateType::SPP: do_SPP(inst); break; diff --git a/src/stim/simulators/transform_without_feedback.cc b/src/stim/simulators/transform_without_feedback.cc index 908b92816..1cc153a8b 100644 --- a/src/stim/simulators/transform_without_feedback.cc +++ b/src/stim/simulators/transform_without_feedback.cc @@ -59,6 +59,21 @@ struct WithoutFeedbackHelper { } } + void undo_cpp(const CircuitInstruction &inst) { + size_t n = inst.targets.size(); + std::vector reversed_targets(n); + std::vector reversed_measure_targets; + for (size_t k = 0; k < n; k++) { + reversed_targets[k] = inst.targets[n - k - 1]; + } + decompose_cpp_operation_with_reverse_independence( + CircuitInstruction{inst.gate_type, inst.args, reversed_targets}, + tracker.xs.size(), + [&](const CircuitInstruction &inst) { + undo_gate(inst); + }); + } + void undo_feedback_capable_pcp_operation(const CircuitInstruction &op) { for (size_t k = op.targets.size(); k > 0;) { k -= 2; @@ -125,7 +140,11 @@ struct WithoutFeedbackHelper { void undo_gate(const CircuitInstruction &op) { if (GATE_DATA[op.gate_type].flags & GATE_CAN_TARGET_BITS) { - undo_feedback_capable_pcp_operation(op); + if (op.gate_type == GateType::CPP) { + undo_cpp(op); + } else { + undo_feedback_capable_pcp_operation(op); + } } else { reversed_semi_flattened_output.safe_append(op); tracker.undo_gate(op); diff --git a/src/stim/simulators/transform_without_feedback.test.cc b/src/stim/simulators/transform_without_feedback.test.cc index 7743d4e9a..dd659645e 100644 --- a/src/stim/simulators/transform_without_feedback.test.cc +++ b/src/stim/simulators/transform_without_feedback.test.cc @@ -144,3 +144,28 @@ TEST(circuit_with_inlined_feedback, mpp) { DETECTOR rec[-3] rec[-1] )CIRCUIT")); } + +TEST(circuit_with_inlined_feedback, cpp) { + Circuit inp = Circuit(R"CIRCUIT( + R 0 1 + M 0 + CPP rec[-1] X1 + M 1 + DETECTOR rec[-1] + )CIRCUIT"); + auto actual = circuit_with_inlined_feedback(inp); + + auto dem1 = ErrorAnalyzer::circuit_to_detector_error_model(inp, true, true, false, 0, false, true); + auto dem2 = ErrorAnalyzer::circuit_to_detector_error_model(actual, true, true, false, 0, false, true); + dem1 = dem1.flattened(); + dem2 = dem2.flattened(); + ASSERT_TRUE(dem1.approx_equals(dem2, 1e-5)); + + ASSERT_EQ(actual, Circuit(R"CIRCUIT( + R 0 1 + M 0 + H 1 1 + M 1 + DETECTOR rec[-2] rec[-1] + )CIRCUIT")); +} diff --git a/testdata/circuit_all_ops_3d.gltf b/testdata/circuit_all_ops_3d.gltf index a53263e82..4b1459276 100644 --- a/testdata/circuit_all_ops_3d.gltf +++ b/testdata/circuit_all_ops_3d.gltf @@ -1 +1 @@ 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