Template Class Measurements¶
Defined in File MeasurementsLQubit.hpp
Inheritance Relationships¶
Base Type¶
public MeasurementsBase< StateVectorT, Measurements< StateVectorT > >
Class Documentation¶
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template<class StateVectorT>
class Measurements : public MeasurementsBase<StateVectorT, Measurements<StateVectorT>>¶ Observable’s Measurement Class.
This class couples with a statevector to performs measurements. Observables are defined by its operator(matrix), the observable class, or through a string-based function dispatch.
- Template Parameters
StateVectorT – type of the statevector to be measured.
Public Functions
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inline explicit Measurements(const StateVectorT &statevector)¶
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inline auto probs() -> std::vector<PrecisionT>¶
Probabilities of each computational basis state.
- Returns
Floating point std::vector with probabilities in lexicographic order.
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inline auto probs(const std::vector<std::size_t> &wires, [[maybe_unused]] const std::vector<std::size_t> &device_wires = {}) -> std::vector<PrecisionT>¶
Probabilities for a subset of the full system.
- Parameters
wires – Wires will restrict probabilities to a subset of the full system.
- Returns
Floating point std::vector with probabilities. The basis columns are rearranged according to wires.
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inline auto probs(const Observable<StateVectorT> &obs, std::size_t num_shots = 0) -> std::vector<PrecisionT>¶
Probabilities to measure rotated basis states.
- Parameters
obs – An observable object.
num_shots – Number of shots (Optional). If specified with a non-zero number, shot-noise will be added to return probabilities
- Returns
Floating point std::vector with probabilities in lexicographic order.
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inline auto probs(std::size_t num_shots) -> std::vector<PrecisionT>¶
Probabilities with shot-noise.
- Parameters
num_shots – Number of shots.
- Returns
Floating point std::vector with probabilities.
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inline auto probs(const std::vector<std::size_t> &wires, std::size_t num_shots) -> std::vector<PrecisionT>¶
Probabilities with shot-noise for a subset of the full system.
- Parameters
wires – Wires will restrict probabilities to a subset
num_shots – Number of shots. of the full system.
- Returns
Floating point std::vector with probabilities.
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inline auto expval(const std::vector<ComplexT> &matrix, const std::vector<std::size_t> &wires) -> PrecisionT¶
Expected value of an observable.
- Parameters
matrix – Square matrix in row-major order.
wires – Wires where to apply the operator.
- Returns
Floating point expected value of the observable.
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inline auto expval(const std::string &operation, const std::vector<std::size_t> &wires) -> PrecisionT¶
Expected value of an observable.
- Parameters
operation – String with the operator name.
wires – Wires where to apply the operator.
- Returns
Floating point expected value of the observable.
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template<class index_type>
inline auto expval(const index_type *row_map_ptr, const index_type row_map_size, const index_type *entries_ptr, const ComplexT *values_ptr, const index_type numNNZ) -> PrecisionT¶ Expected value of a Sparse Hamiltonian.
- Template Parameters
index_type – integer type used as indices of the sparse matrix.
- Parameters
row_map_ptr – row_map array pointer. The j element encodes the number of non-zeros above row j.
row_map_size – row_map array size.
entries_ptr – pointer to an array with column indices of the non-zero elements.
values_ptr – pointer to an array with the non-zero elements.
numNNZ – number of non-zero elements.
- Returns
Floating point expected value of the observable.
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template<typename op_type>
inline auto expval(const std::vector<op_type> &operations_list, const std::vector<std::vector<std::size_t>> &wires_list) -> std::vector<PrecisionT>¶ Expected value for a list of observables.
- Template Parameters
op_type – Operation type.
- Parameters
operations_list – List of operations to measure.
wires_list – List of wires where to apply the operators.
- Returns
Floating point std::vector with expected values for the observables.
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inline auto expval(const Observable<StateVectorT> &obs) -> PrecisionT¶
Expectation value for a general Observable.
- Parameters
obs – An observable object.
- Returns
Floating point expected value of the observable.
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inline auto expval(const Observable<StateVectorT> &obs, const std::size_t &num_shots, const std::vector<std::size_t> &shot_range) -> PrecisionT¶
Expectation value for a Observable with shots.
- Parameters
obs – An observable object.
num_shots – Number of shots.
shot_range – Vector of shot number to measurement
- Returns
Floating point expected value of the observable.
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inline auto var(const Observable<StateVectorT> &obs, const std::size_t &num_shots) -> PrecisionT¶
Calculate the variance for an observable with the number of shots.
- Parameters
obs – An observable object.
num_shots – Number of shots.
- Returns
Variance of the given observable.
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inline auto var(const Observable<StateVectorT> &obs) -> PrecisionT¶
Variance value for a general Observable.
- Parameters
obs – An observable object.
- Returns
Floating point with the variance of the observable.
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inline auto var(const std::string &operation, const std::vector<std::size_t> &wires) -> PrecisionT¶
Variance of an observable.
- Parameters
operation – String with the operator name.
wires – Wires where to apply the operator.
- Returns
Floating point with the variance of the observable.
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inline auto var(const std::vector<ComplexT> &matrix, const std::vector<std::size_t> &wires) -> PrecisionT¶
Variance of a Hermitian matrix.
- Parameters
matrix – Square matrix in row-major order.
wires – Wires where to apply the operator.
- Returns
Floating point with the variance of the observable.
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template<typename op_type>
inline auto var(const std::vector<op_type> &operations_list, const std::vector<std::vector<std::size_t>> &wires_list) -> std::vector<PrecisionT>¶ Variance for a list of observables.
- Template Parameters
op_type – Operation type.
- Parameters
operations_list – List of operations to measure. Square matrix in row-major order or string with the operator name.
wires_list – List of wires where to apply the operators.
- Returns
Floating point std::vector with the variance of the observables.
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inline std::vector<std::size_t> generate_samples_metropolis(const std::string &kernelname, std::size_t num_burnin, std::size_t num_samples)¶
Generate samples using the Metropolis-Hastings method. Reference: Numerical Recipes, NetKet paper.
- Parameters
transition_kernel – User-defined functor for producing transitions between metropolis states.
num_burnin – Number of Metropolis burn-in steps.
num_samples – The number of samples to generate.
- Returns
1-D vector of samples in binary, each sample is separated by a stride equal to the number of qubits.
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template<class index_type>
inline PrecisionT var(const index_type *row_map_ptr, const index_type row_map_size, const index_type *entries_ptr, const ComplexT *values_ptr, const index_type numNNZ)¶ Variance of a sparse Hamiltonian.
- Template Parameters
index_type – integer type used as indices of the sparse matrix.
- Parameters
row_map_ptr – row_map array pointer. The j element encodes the number of non-zeros above row j.
row_map_size – row_map array size.
entries_ptr – pointer to an array with column indices of the non-zero elements.
values_ptr – pointer to an array with the non-zero elements.
numNNZ – number of non-zero elements.
- Returns
Floating point with the variance of the sparse Hamiltonian.
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inline std::vector<std::size_t> generate_samples(const std::size_t num_samples, const std::optional<std::size_t> &seed = std::nullopt)¶
Generate samples using the alias method. Reference: https://en.wikipedia.org/wiki/Alias_method.
- Parameters
num_samples – The number of samples to generate.
seed – Seed to generate the samples from
- Returns
1-D vector of samples in binary, each sample is separated by a stride equal to the number of qubits.
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inline std::vector<std::size_t> generate_samples(const std::vector<std::size_t> &wires, const std::size_t num_samples, const std::optional<std::size_t> &seed = std::nullopt)¶
Generate samples.
- Parameters
wires – Sample are generated for the specified wires.
num_samples – The number of samples to generate.
seed – Seed to generate the samples from
- Returns
1-D vector of samples in binary, each sample is separated by a stride equal to the number of qubits.