quri_parts.algo package

class TwoLocal(qubit_count, layer_pattern, rot_layer_maker, ent_layer_maker, rotation_indices, entangler_map_seq)

Bases: ImmutableLinearMappedParametricQuantumCircuit

The two-local circuit that consists of rotation layers and entanglement layers.

Examples

def add_rotation_gate(circuit, arg):
    layer_index, qubit_index = arg
    theta = circuit.add_parameter("theta")
    if layer_index % 2 == 0:
        circuit.add_ParametricRY_gate(qubit_index, theta)
    else:
        circuit.add_ParametricRZ_gate(qubit_index, theta)

def add_entanglement_gate(circuit, arg):
    layer_index, (i, j) = arg
    if layer_index % 2 == 0:
        circuit.add_CZ_gate(i, j)
    else:
        circuit.add_CZ_gate(i, j)

qubit_count = 4
reps = 2
entangler_map_seq = build_entangler_map(
    qubit_count, [EntanglementPatternType.LINEAR] * reps
)

two_local = TwoLocal(
    qubit_count=qubit_count,
    layer_pattern="rerer",
    rot_layer_maker=add_rotation_gate,
    ent_layer_maker=add_entanglement_gate,
    rotation_indices=range(4),
    entangler_map_seq=entangler_map_seq
)

Parameters:

• qubit_count (int) – Number of qubits.

• layer_pattern (str) – A string specifying layer pattern, which can contain “e” (referring an entanglement layer) or “r” (referring a rotation layer).

• rot_layer_maker (RotLayerMaker) – Function to rotate a qubit in a rotation layer.

• ent_layer_maker (EntLayerMaker) – Function to entangle a qubit pair in an entanglement layer.

• rotation_indices (Sequence[int]) – Qubit indices specifying on which qubits each rotation layer acts.

• entangler_map_seq (Sequence[Sequence[tuple[int, int]]]) – Qubit index pairs specifying on which qubit pairs each entanglement layer acts.

Raises:

ValueError – If layer_pattern contains characters other than “e” and “r”.

layer_pattern

A string specifying layer pattern, which can contain “e” (referring an entanglement layer) or “r” (referring a rotation layer).

rotation_indices

Qubit indices specifying on which qubits each rotation layer acts.

entangler_map_seq

Qubit index pairs specifying on which qubit pairs each entanglement layer acts.

class HardwareEfficient(qubit_count, reps, rotation_indices=None, entangler_map_seq=None, sub_width=0, sub_reps=0, sub_ent_map_seq=None)

Bases: ImmutableLinearMappedParametricQuantumCircuit

Hardware-efficient ansatz.

Ref: A. Kandala et. al., Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets, Nature 549, 242–246

For application of HWE ansatz to Subspace search VQE, see: Nakanishi et. al., Subspace-search variational quantum eigensolver for excited states, https://arxiv.org/abs/1810.09434

Parameters:

• qubit_count (int) – Number of qubits.

• reps (int) – Number of repetitions of a single entanglement layer.

• rotation_indices (Optional[Sequence[int]]) – Qubit indices specifying on which qubits each rotation layer acts.

• entangler_map_seq (Optional[Sequence[Sequence[tuple[int, int]]]]) – Qubit index pairs specifying on which qubit pairs each entanglement layer acts.

• sub_width (int) – Number of qubits in a subspace for SSVQE.

• sub_reps (int) – Number of repetitions of a single entanglement layer in a subspace for SSVQE.

• sub_ent_map_seq (Optional[Sequence[Sequence[tuple[int, int]]]]) – Qubit index pairs specifying on which qubit pairs each entanglement layer in a subspace for SSVQE acts.

class HardwareEfficientReal(qubit_count, reps, rotation_indices=None, entangler_map_seq=None, sub_width=0, sub_reps=0, sub_ent_map_seq=None)

Bases: HardwareEfficient

Real-valued hardware-efficient ansatz.

Ref: A. Kandala et. al., Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets, Nature 549, 242–246

This implementation explicitly removes Rz gates ensuring real states.

For application of HWE ansatz to Subspace search VQE, see: Nakanishi et. al., Subspace-search variational quantum eigensolver for excited states, https://arxiv.org/abs/1810.09434

Parameters:

• qubit_count (int) – Number of qubits.

• reps (int) – Number of repetitions of a single entanglement layer.

• rotation_indices (Optional[Sequence[int]]) – Qubit indices specifying on which qubits each rotation layer acts.

• entangler_map_seq (Optional[Sequence[Sequence[tuple[int, int]]]]) – Qubit index pairs specifying on which qubit pairs each entanglement layer acts.

• sub_width (int) – Number of qubits in a subspace for SSVQE.

• sub_reps (int) – Number of repetitions of a single entanglement layer in a subspace for SSVQE.

• sub_ent_map_seq (Optional[Sequence[Sequence[tuple[int, int]]]]) – Qubit index pairs specifying on which qubit pairs each entanglement layer in a subspace for SSVQE acts.

class SymmetryPreserving(qubit_count, reps, entangler_map_seq=None)

Bases: ImmutableLinearMappedParametricQuantumCircuit

Note

Simple Nakanishi-Fujii-Todo method of period 2pi does not work for this ansatz.

Ref:

Efficient Symmetry-Peserving State Preparation Circuits for the Variational Quantum Eigensolver Algorithm Bryan T. Gard, Linghua Zhu, George S. Barron, Nicholas J. Mayhall, Sophia E. Economou, Edwin Barnes, npj Quantum Inf 6, 10 (2020).

Parameters:

• qubit_count (int) – number of qubits.

• reps (int) – number of layers.

• entangler_map_seq (Optional[Sequence[Sequence[tuple[int, int]]]]) – indices specifying on which qubits each entanglement layer acts.

Raises:

ValueError – if number of qubits is less than 2.

class SymmetryPreservingReal(qubit_count, reps, entangler_map_seq=None)

Bases: SymmetryPreserving

This is an alternative of Symmetry Preserving Ansatz, whose RZ rotation is omitted.

Ref:

Yohei Ibe, Yuya O. Nakagawa, Nathan Earnest, Takahiro Yamamoto, Kosuke Mitarai, Qi Gao, and Takao Kobayashi, Calculating transition amplitudes by variational quantum deflation, Phys. Rev. Research 4, 013173 (2022).

Parameters:

• qubit_count (int) – Number of qubits.

• reps (int) – Number of layers.

• entangler_map_seq (Optional[Sequence[Sequence[tuple[int, int]]]]) – Indices specifying on which qubits each entanglement layer acts.

Raises:

ValueError – if number of qubits is less than 2.

class LBFGS(c1=0.0001, c2=0.4, amin=1e-100, amax=1e+100, maxiter_linesearch=20, rho_const=1000.0, m=5, gtol=1e-06)

Bases: Optimizer

L-BFGS (Limited memory Bryden-Fletcher-Goldfarb-Shanno) optimizer. Partially inspired by SciPy implementation of BFGS optimizer [1]. For the details of algorithm, see [2].

Parameters:

• c1 (float) – coefficient in strong Wolfe condition. It determines the range of the cost function.

• c2 (float) – coefficient in strong Wolfe condition. It determines the range of the derivative of the cost function.

• amin (float) – lower bound of the value of the step size that is computed in line search.

• amax (float) – upper bound of the value of the step size that is computed in line search.

• maxiter_linesearch (int) – the maximum number of the iteration used in line search.

• rho_const (float) – when computing \(1/x\), where \(x\) is a scaler, sometimes it returns zero division error. In that case \(1/x\) is replaced by rho_cost.

• m (int) – In parameters update of each step, it uses the info of the last m steps.

• gtol (Optional[float]) – If not None, it is used for determining if the opotimization has terminated successfully. If gtol is less than the infinity norm of the gradient of the cost function, the optimization is regarded to have terminated successfully. The infinity norm of the gradient \(g\) is defined as \(||g||_{\infty} = \max\{|g_1|, |g_2|, \ldots, |g_n|\}\)

Refs:

[1]: https://github.com/scipy/scipy/blob/master/scipy/optimize/optimize.py [2]: Jorge Nocedal and Stephen J. Wright.

Numerical Optimization (Springer, New York, 2006).

get_init_state(init_params)

Returns an initial state for optimization.

Parameters:

init_params (algo.optimizer.interface.Params)

Return type:

OptimizerStateLBFGS

step(state, cost_function, grad_function=None)

Run a single optimization step and returns a new state.

Parameters:

• state (OptimizerState)

• cost_function (algo.optimizer.interface.CostFunction)

• grad_function (algo.optimizer.interface.GradientFunction | None)

Return type:

OptimizerStateLBFGS

Subpackages

• quri_parts.algo.ansatz package
  • EntanglementPatternType
    • EntanglementPatternType.FULL
    • EntanglementPatternType.LINEAR
    • EntanglementPatternType.CIRCULAR
  • RotLayerMaker
  • EntLayerMaker
  • RotLayerMakerArg
    • RotLayerMakerArg.layer_index
    • RotLayerMakerArg.qubit_index
  • EntLayerMakerArg
    • EntLayerMakerArg.layer_index
    • EntLayerMakerArg.qubit_indices
  • TwoLocal
    • TwoLocal.layer_pattern
    • TwoLocal.rotation_indices
    • TwoLocal.entangler_map_seq
  • build_entangler_map()
  • HardwareEfficient
  • HardwareEfficientReal
  • SymmetryPreserving
  • SymmetryPreservingReal
  • Z2SymmetryPreservingReal
  • Submodules
    • quri_parts.algo.ansatz.hardware_efficient module
      • HardwareEfficient
      • HardwareEfficientReal
    • quri_parts.algo.ansatz.symmetry_preserving module
      • SymmetryPreserving
      • SymmetryPreservingReal
    • quri_parts.algo.ansatz.two_local module
      • EntanglementPatternType
      • RotLayerMakerArg
      • EntLayerMakerArg
      • RotLayerMaker
      • EntLayerMaker
      • TwoLocal
      • build_entangler_map()
    • quri_parts.algo.ansatz.z2_symmetry_preserving module
      • PauliRotation()
      • Rxx_gate()
      • Z2SymmetryPreservingReal
• quri_parts.algo.mitigation package
  • Subpackages
    • quri_parts.algo.mitigation.cdr package
      • cdr()
      • create_cdr_estimator()
      • create_exp_regression()
      • create_exp_regression_with_const()
      • create_exp_regression_with_const_log()
      • create_polynomial_regression()
      • make_training_circuits()
      • Submodules
    • quri_parts.algo.mitigation.post_selection package
      • create_general_post_selection_sampler()
      • Submodules
    • quri_parts.algo.mitigation.readout_mitigation package
      • create_filter_matrix()
      • readout_mitigation()
      • create_readout_mitigation_sampler()
      • create_readout_mitigation_concurrent_sampler()
      • Submodules
    • quri_parts.algo.mitigation.zne package
      • create_folding_left()
      • create_folding_right()
      • create_folding_random()
      • create_polynomial_extrapolate()
      • create_exp_extrapolate()
      • create_exp_extrapolate_with_const()
      • create_exp_extrapolate_with_const_log()
      • create_zne_estimator()
      • zne()
      • richardson_extrapolation()
      • scaling_circuit_folding()
      • Submodules
• quri_parts.algo.optimizer package
  • Params
  • CostFunction
  • GradientFunction
  • OptimizerStatus
    • OptimizerStatus.SUCCESS
    • OptimizerStatus.FAILED
    • OptimizerStatus.CONVERGED
  • OptimizerState
    • OptimizerState.params
    • OptimizerState.cost
    • OptimizerState.status
    • OptimizerState.niter
    • OptimizerState.funcalls
    • OptimizerState.gradcalls
    • OptimizerState.n_params
  • Optimizer
    • Optimizer.get_init_state()
    • Optimizer.step()
  • Adam
    • Adam.get_init_state()
    • Adam.step()
  • AdaBelief
  • LBFGS
    • LBFGS.get_init_state()
    • LBFGS.step()
  • NFT
  • NFTfit
  • SPSA
    • SPSA.get_init_state()
    • SPSA.step()
  • Submodules
    • quri_parts.algo.optimizer.adam module
      • OptimizerStateAdam
      • Adam
      • AdaBelief
    • quri_parts.algo.optimizer.interface module
      • Params
      • CostFunction
      • GradientFunction
      • OptimizerStatus
      • OptimizerState
      • Optimizer
    • quri_parts.algo.optimizer.lbfgs module
      • OptimizerStateLBFGS
      • LBFGS
    • quri_parts.algo.optimizer.nft module
      • OptimizerStateNFT
      • NFTBase
      • NFT
      • NFTfit
    • quri_parts.algo.optimizer.spsa module
      • OptimizerStateSPSA
      • SPSA
    • quri_parts.algo.optimizer.tolerance module
      • ftol()
      • gtol()
• quri_parts.algo.utils package
  • exp_fitting()
  • exp_fitting_with_const()
  • exp_fitting_with_const_log()
  • polynomial_fitting()
  • Submodules
    • quri_parts.algo.utils.fitting module
      • FittedResult
      • polynomial_fitting()
      • exp_fitting()
      • exp_fitting_with_const()
      • exp_fitting_with_const_log()