quri_parts.algo.mitigation.cdr package#

quri_parts.algo.mitigation.cdr.cdr(obs: Operator | PauliLabel, circuit: NonParametricQuantumCircuit, noisy_estimator: Callable[[Sequence[Operator | PauliLabel], Sequence[GeneralCircuitQuantumState]], Iterable[Estimate[complex]]], exact_estimator: Callable[[Sequence[Operator | PauliLabel], Sequence[GeneralCircuitQuantumState]], Iterable[Estimate[complex]]], regression_method: Callable[[float, Iterable[float], Iterable[float]], float], num_training_circuits: int = 10, fraction_of_replacement: float = 0.1, seed: int | None = None) → float#

Returns an error-mitigated expectation value of an observable by using clifford-data-regression (CDR).

Parameters:

  • obs – Observable for which the expected value is calculated.

  • circuit – Circuit that CDR is applied to.

  • noisy_estimator – A noisy estimator that computes the expectation value from obs and circuit.

  • exact_estimator – An exact (noiseless) estimator that computes the expectation value from obs and training circuits. This is assumed a simulator that can do a fast simulation of the (almost) Clifford circuit.

  • regression_method – Method used for regression.

  • num_training_circuits – A number of training circuits to be used for CDR.

  • fraction_of_replacement – Fraction of the number of non-Clifford gates that are to be replaced by Clifford gates.

  • seed – Seed to choose which gates to replace with Clifford ones.

quri_parts.algo.mitigation.cdr.create_cdr_estimator(noisy_estimator: Callable[[Sequence[Operator | PauliLabel], Sequence[GeneralCircuitQuantumState]], Iterable[Estimate[complex]]], exact_estimator: Callable[[Sequence[Operator | PauliLabel], Sequence[GeneralCircuitQuantumState]], Iterable[Estimate[complex]]], regression_method: Callable[[float, Iterable[float], Iterable[float]], float], num_training_circuits: int = 10, fraction_of_replacement: float = 0.1, seed: int | None = None) → Callable[[Operator | PauliLabel, GeneralCircuitQuantumState], Estimate[complex]]#

Wrap the given ConcurrentQuantumEstimator to create a QuantumEstimator where clifford-data-regression is automatically applied to the result.

Parameters:

  • noisy_estimator – A noisy estimator that computes the expectation value from obs and circuit. This is assumed a given ConcurrentQuantumEstimator which will be wrapped.

  • exact_estimator – An exact (noiseless) estimator that computes the expectation value from obs and training circuits. This is assumed a simulator that can do a fast simulation of the (almost) Clifford circuit.

  • regression_method – Method used for regression.

  • num_training_circuits – A number of training circuits to be used for CDR.

  • fraction_of_replacement – Fraction of the number of non-Clifford gates that are to be replaced by Clifford gates.

  • seed – Seed to choose which gates to replace with Clifford ones.

quri_parts.algo.mitigation.cdr.create_exp_regression(order: int) → Callable[[float, Iterable[float], Iterable[float]], float]#

Returns a RegressionMethod that gives a value which is evaluated by using the curve regression and exponential function f(x) = a + b exp(p(x)), where p(x) is a polynomial of a given order.

Parameters:

order – Order of the polynomial used for regression.

quri_parts.algo.mitigation.cdr.create_exp_regression_with_const(order: int, constant: float) → Callable[[float, Iterable[float], Iterable[float]], float]#

Returns a RegressionMethod that gives a value which is evaluated by using the curve regression and exponential function f(x) = constant + b exp(p(x)), where p(x) is a polynomial of a given order and constant is a known parameter (obtained as the infinite limit f(x->inf) when f(x) converges to a finite asymptotic value).

Parameters:

  • order – Order of the polynomial used for regression.

  • constant – A constant deduced from asymptotic behavior f(x->inf).

quri_parts.algo.mitigation.cdr.create_exp_regression_with_const_log(order: int, constant: float) → Callable[[float, Iterable[float], Iterable[float]], float]#

Returns a RegressionMethod that gives a value which is evaluated by using the log regression and exponential function f(x) = constant + b exp(p(x)), where p(x) is a polynomial of a given order and constant is a known parameter (obtained as the infinite limit f(x->inf) when f(x) converges to a finite asymptotic value).

Parameters:

  • order – Order of the polynomial used for regression.

  • constant – A constant deduced from asymptotic behavior f(x->inf).

quri_parts.algo.mitigation.cdr.create_polynomial_regression(order: int) → Callable[[float, Iterable[float], Iterable[float]], float]#

Returns a RegressionMethod that gives a value which is evaluated by using the polynomial regression.

Parameters:

order – Order of the polynomial used for regression.

quri_parts.algo.mitigation.cdr.make_training_circuits(circuit: NonParametricQuantumCircuit, num_non_clifford_untouched: int, num_training_circuits: int = 10, seed: int | None = None) → list[NonParametricQuantumCircuit]#

Returns a list of near Clifford circuits obtained by replacing some non- Clifford gates in the input circuit by the nearest Clifford gates. The gate to be replaced is chosen at random.

Parameters:

  • circuit – Original circuit from which the near Clifford circuit is constructed.

  • num_non_clifford_untouched – A number of non-Clifford gates that remain untouched in the replacing.

  • num_training_circuits – Number of circuits to be returned.

  • seed – Seed to choose which gates to replace with Clifford ones.

Submodules#