matchcake.devices.sampling_strategies package¶

Submodules¶

matchcake.devices.sampling_strategies.k_qubits_by_k_qubits_sampling module¶

class matchcake.devices.sampling_strategies.k_qubits_by_k_qubits_sampling.KQubitsByKQubitsSampling¶

Bases: SamplingStrategy

K: int = None¶

NAME: str = 'kQubitBykQubitSampling'¶

batch_generate_samples(device: QubitDevice, states_prob_func: Callable[[TensorLike, Wires], TensorLike], **kwargs) → TensorLike¶

Generate qubit-by-qubit samples.

1. Sample x_0 from the probability distribution pi_0(x_0)

2. for j = 1 to n-1 do

3. Sample x_j from the probability distribution pi_j(x_0, …, x_{j-1}, x_j) / pi_{j-1}(x_0, …, x_{j-1})

4. end for

5. return x_0 … x_{n-1}

Returns:

Samples with shape (shots, num_wires) of probabilities |<x|psi>|^2

batch_generate_samples_by_subsets_of_k(device: QubitDevice, states_prob_func: Callable[[TensorLike, Wires], TensorLike], k: int = 1, **kwargs) → TensorLike¶

Generate qubit-by-qubit samples.

1. Sample x_0 from the probability distribution pi_0(x_0)

2. for j = 1 to n-1 do

3. Sample x_j from the probability distribution pi_j(x_0, …, x_{j-1}, x_j) / pi_{j-1}(x_0, …, x_{j-1})

4. end for

5. return x_0 … x_{n-1}

Returns:

Samples with shape (shots, num_wires) of probabilities |<x|psi>|^2

classmethod compute_extend_probs_to_all(all_states: TensorLike, extended_states: TensorLike, extended_states_probs: TensorLike)¶

classmethod extend_states(states: TensorLike, added_states: TensorLike, unique: bool = True) → TensorLike¶

generate_samples(device: QubitDevice, state_prob_func: Callable[[TensorLike, Wires], TensorLike], **kwargs) → TensorLike¶

Generate qubit-by-qubit samples.

1. Sample x_0 from the probability distribution pi_0(x_0)

2. for j = 1 to n-1 do

3. Sample x_j from the probability distribution pi_j(x_0, …, x_{j-1}, x_j) / pi_{j-1}(x_0, …, x_{j-1})

4. end for

5. return x_0 … x_{n-1}

Returns:

Samples with shape (shots, num_wires) of probabilities |<x|psi>|^2

matchcake.devices.sampling_strategies.qubit_by_qubit_sampling module¶

class matchcake.devices.sampling_strategies.qubit_by_qubit_sampling.QubitByQubitSampling¶

Bases: KQubitsByKQubitsSampling

K: int = 1¶

NAME: str = 'QubitByQubitSampling'¶

generate_samples(device: QubitDevice, state_prob_func: Callable[[TensorLike, Wires], TensorLike], **kwargs) → TensorLike¶

Generate qubit-by-qubit samples.

1. Sample x_0 from the probability distribution pi_0(x_0)

2. for j = 1 to n-1 do

3. Sample x_j from the probability distribution pi_j(x_0, …, x_{j-1}, x_j) / pi_{j-1}(x_0, …, x_{j-1})

4. end for

5. return x_0 … x_{n-1}

Returns:

Samples with shape (shots, num_wires) of probabilities |<x|psi>|^2

matchcake.devices.sampling_strategies.sampling_strategy module¶

class matchcake.devices.sampling_strategies.sampling_strategy.SamplingStrategy¶

Bases: ABC

NAME: str = 'SamplingStrategy'¶

batch_generate_samples(device: QubitDevice, states_prob_func: Callable[[TensorLike, Wires], TensorLike], **kwargs) → TensorLike¶

abstract generate_samples(device: QubitDevice, state_prob_func: Callable[[TensorLike, Wires], TensorLike], **kwargs) → TensorLike¶

matchcake.devices.sampling_strategies.two_qubits_by_two_qubits_sampling module¶

class matchcake.devices.sampling_strategies.two_qubits_by_two_qubits_sampling.TwoQubitsByTwoQubitsSampling¶

Bases: KQubitsByKQubitsSampling

K: int = 2¶

NAME: str = '2QubitBy2QubitSampling'¶

Module contents¶

matchcake.devices.sampling_strategies.get_sampling_strategy(name: str) → SamplingStrategy¶