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Change MRB Simulation (#24)
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* mirror_rb.py - changed simulation method and circuits

* updated CHANGELOG.rst

---------

Co-authored-by: Pedro Figueroa <pedro.romero@meetiqm.com>
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@pedrofigro
pedrofigro and Pedro Figueroa authored Jan 7, 2025
1 parent 2e10c51 commit 09e7cf7
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4 changes: 4 additions & 0 deletions CHANGELOG.rst
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Expand Up @@ -2,6 +2,10 @@
Changelog
=========

Version 2.5
===========
* Changed simulation method for MRB to 'stabilizer' and simulation circuits are compiled in circuit generation stage.

Version 2.4
===========
* Changed Qscore to operate under the new base class.
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50 changes: 25 additions & 25 deletions src/iqm/benchmarks/randomized_benchmarking/mirror_rb/mirror_rb.py
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Expand Up @@ -10,8 +10,8 @@

import numpy as np
from qiskit import transpile
from qiskit.quantum_info import random_clifford, random_pauli
from qiskit_aer import Aer
from qiskit.quantum_info import Clifford, random_clifford, random_pauli
from qiskit_aer import Aer, AerSimulator
from scipy.spatial.distance import hamming
import xarray as xr

Expand Down Expand Up @@ -262,9 +262,13 @@ def generate_pauli_dressed_mrb_circuits(
pauli_dressed_circuits_untranspiled: List[QuantumCircuit] = []
pauli_dressed_circuits_transpiled: List[QuantumCircuit] = []

sim_method = "stabilizer"
simulator = AerSimulator(method=sim_method)

for _ in range(pauli_samples_per_circ):
# Initialize the quantum circuit object
circ = QuantumCircuit(num_qubits)
circ_untransp = QuantumCircuit(num_qubits)
# Sample all the random Paulis
paulis = [random_pauli(num_qubits) for _ in range(depth + 1)]

Expand All @@ -282,6 +286,7 @@ def generate_pauli_dressed_mrb_circuits(
)
circ.barrier()
circ.compose(cycle_layers[k], inplace=True)
circ_untransp.compose(cycle_layers[k], inplace=True)
circ.barrier()

# Apply middle Pauli
Expand All @@ -307,16 +312,20 @@ def generate_pauli_dressed_mrb_circuits(
for i in range(num_qubits):
circ.compose(clifford_layer[i].to_instruction().inverse(), qubits=[i], inplace=True)

# Add measurements
circ.measure_all()

# Transpile to backend - no optimize SQG should be used!
if isinstance(backend_arg, str):
retrieved_backend = get_iqm_backend(backend_arg)
else:
assert isinstance(backend_arg, IQMBackendBase)
retrieved_backend = backend_arg

circ_untransp = circ.copy()
# Add measurements to untranspiled - after!
circ_untranspiled = transpile(Clifford(circ_untransp).to_circuit(), simulator)
circ_untranspiled.measure_all()

# Add measurements to transpiled - before!
circ.measure_all()
circ_transpiled = transpile(
circ,
backend=retrieved_backend,
Expand All @@ -325,7 +334,7 @@ def generate_pauli_dressed_mrb_circuits(
routing_method=routing_method,
)

pauli_dressed_circuits_untranspiled.append(circ)
pauli_dressed_circuits_untranspiled.append(circ_untranspiled)
pauli_dressed_circuits_transpiled.append(circ_transpiled)

# Store the circuit
Expand Down Expand Up @@ -418,14 +427,14 @@ def mrb_analysis(run: BenchmarkRunResult) -> BenchmarkAnalysisResult:
observations = {}
dataset = run.dataset.copy(deep=True)

shots = dataset.attrs["shots"]
# shots = dataset.attrs["shots"]
num_circuit_samples = dataset.attrs["num_circuit_samples"]
num_pauli_samples = dataset.attrs["num_pauli_samples"]

density_2q_gates = dataset.attrs["density_2q_gates"]
two_qubit_gate_ensemble = dataset.attrs["two_qubit_gate_ensemble"]

max_gates_per_batch = dataset.attrs["max_gates_per_batch"]
# max_gates_per_batch = dataset.attrs["max_gates_per_batch"]

# Analyze the results for each qubit layout of the experiment dataset
qubits_array = dataset.attrs["qubits_array"]
Expand All @@ -445,8 +454,9 @@ def mrb_analysis(run: BenchmarkRunResult) -> BenchmarkAnalysisResult:
else:
assigned_mrb_depths = {str(qubits_array[i]): [2 * m for m in depths_array[i]] for i in range(len(depths_array))}

transpiled_circuits = run.circuits["transpiled_circuits"]
simulator = Aer.get_backend("qasm_simulator")
mrb_sim_circuits = run.circuits["untranspiled_circuits"]
sim_method = "stabilizer"
simulator = AerSimulator(method=sim_method) # Aer.get_backend("stabilizer")

all_noisy_counts: Dict[str, Dict[int, List[Dict[str, int]]]] = {}
all_noiseless_counts: Dict[str, Dict[int, List[Dict[str, int]]]] = {}
Expand All @@ -465,22 +475,12 @@ def mrb_analysis(run: BenchmarkRunResult) -> BenchmarkAnalysisResult:
all_noisy_counts[str(qubits)][depth] = xrvariable_to_counts(
dataset, identifier, num_circuit_samples * num_pauli_samples
)

qcvv_logger.info(f"Depth {depth}")
# Execute the quantum circuits on the simulated, ideal backend
# pylint: disable=unbalanced-tuple-unpacking
all_noiseless_jobs, _ = submit_execute(
{tuple(qubits): transpiled_circuits[f"{str(qubits)}_depth_{str(depth)}"].circuits},
simulator,
shots,
calset_id=None,
max_gates_per_batch=max_gates_per_batch,
)

# Retrieve counts
all_noiseless_counts[str(qubits)][depth], time_retrieve_noiseless[str(qubits)][depth] = retrieve_all_counts(
all_noiseless_jobs
)
mrb_circs = mrb_sim_circuits[f"{str(qubits)}_depth_{str(depth)}"].circuits

qcvv_logger.info("Getting simulation counts")
all_noiseless_counts[str(qubits)][depth] = simulator.run(mrb_circs).result().get_counts()

# Compute polarizations for the current depth
polarizations[depth] = compute_polarizations(
Expand Down Expand Up @@ -695,7 +695,7 @@ def execute(self, backend: IQMBackendBase) -> xr.Dataset:
mrb_untranspiled_circuits_lists: Dict[int, List[QuantumCircuit]] = {}
time_circuit_generation[str(qubits)] = 0
for depth in assigned_mrb_depths[str(qubits)]:
qcvv_logger.info(f"Depth {depth}")
qcvv_logger.info(f"Depth {depth} - Generating all circuits")
mrb_circuits[depth], elapsed_time = generate_fixed_depth_mrb_circuits(
qubits,
self.num_circuit_samples,
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