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tanujkhattar
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Add cirq.TensoredConfusionMatrices for readout error mitigation. (quantumlib#4854)
Fixes quantumlib#4800. cc @mrwojtek @mpharrigan
1 parent 011f937 commit d02c9bf

8 files changed

+599
-6
lines changed

‎cirq-core/cirq/__init__.py

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Original file line numberDiff line numberDiff line change
@@ -102,6 +102,7 @@
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)
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from cirq.experiments import (
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TensoredConfusionMatrices,
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estimate_parallel_single_qubit_readout_errors,
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estimate_single_qubit_readout_errors,
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hog_score_xeb_fidelity_from_probabilities,
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generate_boixo_2018_supremacy_circuits_v2,
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generate_boixo_2018_supremacy_circuits_v2_bristlecone,
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generate_boixo_2018_supremacy_circuits_v2_grid,
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measure_confusion_matrix,
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xeb_fidelity,
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)
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‎cirq-core/cirq/experiments/__init__.py

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@@ -65,6 +65,11 @@
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random_rotations_between_grid_interaction_layers_circuit,
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)
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from cirq.experiments.readout_confusion_matrix import (
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TensoredConfusionMatrices,
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measure_confusion_matrix,
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)
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from cirq.experiments.n_qubit_tomography import (
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get_state_tomography_data,
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state_tomography,

‎cirq-core/cirq/experiments/readout_confusion_matrix.py

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Large diffs are not rendered by default.

‎cirq-core/cirq/experiments/readout_confusion_matrix_test.py

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# Copyright 2022 The Cirq Developers
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import cirq
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import pytest
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from cirq.experiments.single_qubit_readout_calibration_test import NoisySingleQubitReadoutSampler
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def get_expected_cm(num_qubits: int, p0: float, p1: float):
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expected_cm = np.zeros((2 ** num_qubits,) * 2)
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for i in range(2 ** num_qubits):
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for j in range(2 ** num_qubits):
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p = 1.0
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for k in range(num_qubits):
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b0 = (i >> k) & 1
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b1 = (j >> k) & 1
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if b0 == 0:
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p *= p0 * b1 + (1 - p0) * (1 - b1)
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else:
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p *= p1 * (1 - b1) + (1 - p1) * b1
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expected_cm[i][j] = p
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return expected_cm
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@pytest.mark.parametrize('p0, p1', [(0, 0), (0.2, 0.4), (0.5, 0.5), (0.6, 0.3), (1.0, 1.0)])
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def test_measure_confusion_matrix_with_noise(p0, p1):
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sampler = NoisySingleQubitReadoutSampler(p0, p1, seed=1234)
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num_qubits = 4
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qubits = cirq.LineQubit.range(num_qubits)
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expected_cm = get_expected_cm(num_qubits, p0, p1)
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qubits_small = qubits[:2]
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expected_cm_small = get_expected_cm(2, p0, p1)
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repetitions = 12_000
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# Build entire confusion matrix by running 2 ** 4 = 16 circuits.
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readout_cm = cirq.measure_confusion_matrix(sampler, qubits, repetitions=repetitions)
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assert readout_cm.repetitions == repetitions
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for q, expected in zip([None, qubits_small], [expected_cm, expected_cm_small]):
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np.testing.assert_allclose(readout_cm.confusion_matrix(q), expected, atol=1e-2)
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np.testing.assert_allclose(
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readout_cm.confusion_matrix(q) @ readout_cm.correction_matrix(q),
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np.eye(expected.shape[0]),
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atol=1e-2,
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)
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# Build a tensored confusion matrix using smaller single qubit confusion matrices.
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# This works because the error is uncorrelated and requires only 4 * 2 = 8 circuits.
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readout_cm = cirq.measure_confusion_matrix(
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sampler, [[q] for q in qubits], repetitions=repetitions
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)
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assert readout_cm.repetitions == repetitions
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for q, expected in zip([None, qubits_small], [expected_cm, expected_cm_small]):
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np.testing.assert_allclose(readout_cm.confusion_matrix(q), expected, atol=1e-2)
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np.testing.assert_allclose(
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readout_cm.confusion_matrix(q) @ readout_cm.correction_matrix(q),
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np.eye(expected.shape[0]),
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atol=1e-2,
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)
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# Apply corrections to sampled probabilities using readout_cm.
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qs = qubits_small
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circuit = cirq.Circuit(cirq.H.on_each(*qs), cirq.measure(*qs))
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reps = 100_000
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sampled_result = cirq.get_state_histogram(sampler.run(circuit, repetitions=reps)) / reps
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expected_result = [1 / 4] * 4
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def l2norm(result: np.ndarray):
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return np.sum((expected_result - result) ** 2)
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corrected_result = readout_cm.apply(sampled_result, qs)
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assert l2norm(corrected_result) <= l2norm(sampled_result)
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def test_readout_confusion_matrix_raises():
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num_qubits = 2
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confusion_matrix = get_expected_cm(num_qubits, 0.1, 0.2)
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qubits = cirq.LineQubit.range(4)
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with pytest.raises(ValueError, match=r"measure_qubits cannot be empty"):
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_ = cirq.TensoredConfusionMatrices([], [], repetitions=0, timestamp=0)
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with pytest.raises(ValueError, match=r"len\(confusion_matrices\)"):
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_ = cirq.TensoredConfusionMatrices(
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[confusion_matrix], [qubits[:2], qubits[2:]], repetitions=0, timestamp=0
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)
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with pytest.raises(ValueError, match="Shape mismatch for confusion matrix"):
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_ = cirq.TensoredConfusionMatrices(confusion_matrix, qubits, repetitions=0, timestamp=0)
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with pytest.raises(ValueError, match="Repeated qubits not allowed"):
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_ = cirq.TensoredConfusionMatrices(
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[confusion_matrix, confusion_matrix],
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[qubits[:2], qubits[1:3]],
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repetitions=0,
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timestamp=0,
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)
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readout_cm = cirq.TensoredConfusionMatrices(
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[confusion_matrix, confusion_matrix], [qubits[:2], qubits[2:]], repetitions=0, timestamp=0
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)
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with pytest.raises(ValueError, match="should be a subset of"):
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_ = readout_cm.confusion_matrix([cirq.NamedQubit("a")])
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with pytest.raises(ValueError, match="should be a subset of"):
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_ = readout_cm.correction_matrix([cirq.NamedQubit("a")])
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with pytest.raises(ValueError, match="result.shape .* should be"):
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_ = readout_cm.apply(np.asarray([100]), qubits[:2])
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with pytest.raises(ValueError, match="method.* should be"):
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_ = readout_cm.apply(np.asarray([1 / 16] * 16), method='l1norm')
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def test_readout_confusion_matrix_repr_and_equality():
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mat1 = cirq.testing.random_orthogonal(4, random_state=1234)
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mat2 = cirq.testing.random_orthogonal(2, random_state=1234)
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q = cirq.LineQubit.range(3)
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a = cirq.TensoredConfusionMatrices([mat1, mat2], [q[:2], q[2:]], repetitions=0, timestamp=0)
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b = cirq.TensoredConfusionMatrices(mat1, q[:2], repetitions=0, timestamp=0)
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c = cirq.TensoredConfusionMatrices(mat2, q[2:], repetitions=0, timestamp=0)
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for x in [a, b, c]:
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cirq.testing.assert_equivalent_repr(x)
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assert cirq.approx_eq(x, x)
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assert x._approx_eq_(mat1, 1e-6) is NotImplemented
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eq = cirq.testing.EqualsTester()
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eq.add_equality_group(a, a)
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eq.add_equality_group(b, b)
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eq.add_equality_group(c, c)

‎cirq-core/cirq/experiments/single_qubit_readout_calibration_test.py

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Original file line numberDiff line numberDiff line change
@@ -52,12 +52,12 @@ def run_sweep(
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results = self.simulator.run_sweep(program, params, repetitions)
5353
for result in results:
5454
for bits in result.measurements.values():
55-
with np.nditer(bits, op_flags=['readwrite']) as it:
56-
for x in it:
57-
if x == 0 and self.prng.uniform() < self.p0:
58-
x[...] = 1
59-
elif self.prng.uniform() < self.p1:
60-
x[...] = 0
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rand_num = self.prng.uniform(size=bits.shape)
56+
should_flip = np.logical_or(
57+
np.logical_and(bits == 0, rand_num < self.p0),
58+
np.logical_and(bits == 1, rand_num < self.p1),
59+
)
60+
bits[should_flip] ^= 1
6161
return results
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‎cirq-core/cirq/json_resolver_cache.py

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@@ -140,6 +140,7 @@ def _parallel_gate_op(gate, qubits):
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'_QubitAsQid': raw_types._QubitAsQid,
141141
'QuantumFourierTransformGate': cirq.QuantumFourierTransformGate,
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'RandomGateChannel': cirq.RandomGateChannel,
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'TensoredConfusionMatrices': cirq.TensoredConfusionMatrices,
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'RepetitionsStoppingCriteria': cirq.work.RepetitionsStoppingCriteria,
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'ResetChannel': cirq.ResetChannel,
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'Result': cirq.Result,

‎cirq-core/cirq/protocols/json_test_data/TensoredConfusionMatrices.json

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{
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"cirq_type": "TensoredConfusionMatrices",
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"confusion_matrices": [
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[
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[
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0.6395,
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0.1594,
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0.1592,
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0.0419
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],
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[
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0.5617,
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0.2368,
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0.1401,
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0.0614
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],
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[
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0.5655,
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0.1367,
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0.2403,
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0.0575
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],
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[
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0.4835,
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0.2185,
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0.2048,
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0.0932
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]
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],
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[
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[
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0.7999,
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0.2001
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],
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[
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0.7046,
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0.2954
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]
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]
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],
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"measure_qubits": [
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[
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{
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"cirq_type": "LineQubit",
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"x": 0
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},
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{
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"cirq_type": "LineQubit",
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"x": 1
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}
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],
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[
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{
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"cirq_type": "NamedQubit",
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"name": "a"
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}
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]
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],
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"repetitions": 10000,
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"timestamp": 1642630636.966274
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}

‎cirq-core/cirq/protocols/json_test_data/TensoredConfusionMatrices.repr

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cirq.TensoredConfusionMatrices(
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[
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np.array(
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[
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[0.6395, 0.1594, 0.1592, 0.0419],
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[0.5617, 0.2368, 0.1401, 0.0614],
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[0.5655, 0.1367, 0.2403, 0.0575],
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[0.4835, 0.2185, 0.2048, 0.0932],
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],
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dtype=np.float64,
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),
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np.array([[0.7999, 0.2001], [0.7046, 0.2954]], dtype=np.float64),
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],
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[[cirq.LineQubit(0), cirq.LineQubit(1)], [cirq.NamedQubit('a')]],
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repetitions=10000,
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timestamp=1642630636.966274,
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)

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