cirq.ActOnStateVectorArgs

State and context for an operation acting on a state vector.

Inherits From: ActOnArgs, OperationTarget

cirq.ActOnStateVectorArgs(
    target_tensor: Optional[np.ndarray] = None,
    available_buffer: Optional[np.ndarray] = None,
    prng: Optional[np.random.RandomState] = None,
    log_of_measurement_results: Optional[Dict[str, List[int]]] = None,
    qubits: Optional[Sequence['cirq.Qid']] = None,
    initial_state: Union[np.ndarray, 'cirq.STATE_VECTOR_LIKE'] = 0,
    dtype: Type[np.number] = np.complex64,
    classical_data: Optional['cirq.ClassicalDataStore'] = None
)

There are two common ways to act on this object:

Directly edit the target_tensor property, which is storing the state vector of the quantum system as a numpy array with one axis per qudit.
Overwrite the available_buffer property with the new state vector, and then pass available_buffer into swap_target_tensor_for.

Args
`target_tensor`	The state vector to act on, stored as a numpy array with one dimension for each qubit in the system. Operations are expected to perform inplace edits of this object.
`available_buffer`	A workspace with the same shape and dtype as `target_tensor`. Used by operations that cannot be applied to `target_tensor` inline, in order to avoid unnecessary allocations. Passing `available_buffer` into `swap_target_tensor_for` will swap it for `target_tensor`.
`qubits`	Determines the canonical ordering of the qubits. This is often used in specifying the initial state, i.e. the ordering of the computational basis states.
`prng`	The pseudo random number generator to use for probabilistic effects.
`log_of_measurement_results`	A mutable object that measurements are being recorded into.
`initial_state`	The initial state for the simulation in the computational basis.
`dtype`	The `numpy.dtype` of the inferred state vector. One of `numpy.complex64` or `numpy.complex128`. Only used when `target_tenson` is None.
`classical_data`	The shared classical data container for this simulation.

Attributes
`allows_factoring`	Subclasses that allow factorization should override this.
`available_buffer`
`can_represent_mixed_states`
`classical_data`	The shared classical data container for this simulation..
`ignore_measurement_results`
`log_of_measurement_results`	Gets the log of measurement results.
`prng`
`qubit_map`
`qubits`	Gets the qubit order maintained by this target.
`target_tensor`

Methods

`apply_operation`

View source

apply_operation(
    op: 'cirq.Operation'
)

`copy`

View source

copy(
    deep_copy_buffers: bool = True
) -> TSelf

Creates a copy of the object.

Args
`deep_copy_buffers`	If True, buffers will also be deep-copied. Otherwise the copy will share a reference to the original object's buffers.

Returns
A copied instance.

`create_merged_state`

View source

create_merged_state() -> TSelf

Creates a final merged state.

`factor`

View source

factor(
    qubits: Sequence['cirq.Qid'], *, validate=True, atol=1e-07, inplace=False
) -> Tuple[TSelf, TSelf]

Splits two state spaces after a measurement or reset.

`get_axes`

View source

get_axes(
    qubits: Sequence['cirq.Qid']
) -> List[int]

`kronecker_product`

View source

kronecker_product(
    other: TSelf, *, inplace=False
) -> TSelf

Joins two state spaces together.

`measure`

View source

measure(
    qubits: Sequence['cirq.Qid'], key: str, invert_mask: Sequence[bool]
)

Measures the qubits and records to log_of_measurement_results.

Any bitmasks will be applied to the measurement record. If self._ignore_measurement_results is set, it dephases instead of measuring, and no measurement result will be logged.

Args
`qubits`	The qubits to measure.
`key`	The key the measurement result should be logged under. Note that operations should only store results under keys they have declared in a `_measurement_key_names_` method.
`invert_mask`	The invert mask for the measurement.

Raises
`ValueError`	If a measurement key has already been logged to a key.

`rename`

View source

rename(
    q1: 'cirq.Qid', q2: 'cirq.Qid', *, inplace=False
)

Renames q1 to q2.

Args
`q1`	The qubit to rename.
`q2`	The new name.
`inplace`	True to rename the qubit in the current object, False to create a copy with the qubit renamed.

Returns
The original object with the qubits renamed if inplace is requested, or a copy of the original object with the qubits renamed otherwise.

Raises
`ValueError`	If the qubits are of different dimensionality.

`sample`

View source

sample(
    qubits: Sequence['cirq.Qid'],
    repetitions: int = 1,
    seed: 'cirq.RANDOM_STATE_OR_SEED_LIKE' = None
) -> np.ndarray

Samples the state value.

`subspace_index`

View source

subspace_index(
    axes: Sequence[int],
    little_endian_bits_int: int = 0,
    *,
    big_endian_bits_int: int = 0
) -> Tuple[Union[slice, int, 'ellipsis'], ...]

THIS FUNCTION IS DEPRECATED.

IT WILL BE REMOVED IN cirq v0.16.

None, this function was unintentionally made public.

An index for the subspace where the target axes equal a value.

    Args:
        axes: The qubits that are specified by the index bits.
        little_endian_bits_int: The desired value of the qubits at the
            targeted `axes`, packed into an integer. The least significant
            bit of the integer is the desired bit for the first axis, and
            so forth in increasing order. Can't be specified at the same
            time as `big_endian_bits_int`.

            When operating on qudits instead of qubits, the same basic logic
            applies but in a different basis. For example, if the target
            axes have dimension [a:2, b:3, c:2] then the integer 10
            decomposes into [a=0, b=2, c=1] via 7 = 1*(3*2) +  2*(2) + 0.
        big_endian_bits_int: The desired value of the qubits at the
            targeted `axes`, packed into an integer. The most significant
            bit of the integer is the desired bit for the first axis, and
            so forth in decreasing order. Can't be specified at the same
            time as `little_endian_bits_int`.

            When operating on qudits instead of qubits, the same basic logic
            applies but in a different basis. For example, if the target
            axes have dimension [a:2, b:3, c:2] then the integer 10
            decomposes into [a=1, b=2, c=0] via 7 = 1*(3*2) +  2*(2) + 0.

    Returns:
        A value that can be used to index into `target_tensor` and
        `available_buffer`, and manipulate only the part of Hilbert space
        corresponding to a given bit assignment.

    Example:
        If `target_tensor` is a 4 qubit tensor and `axes` is `[1, 3]` and
        then this method will return the following when given
        `little_endian_bits=0b01`:

            `(slice(None), 0, slice(None), 1, Ellipsis)`

        Therefore the following two lines would be equivalent:

            args.target_tensor[args.subspace_index(0b01)] += 1

            args.target_tensor[:, 0, :, 1] += 1

`swap`

View source

swap(
    q1: 'cirq.Qid', q2: 'cirq.Qid', *, inplace=False
)

Swaps two qubits.

This only affects the index, and does not modify the underlying state.

Args
`q1`	The first qubit to swap.
`q2`	The second qubit to swap.
`inplace`	True to swap the qubits in the current object, False to create a copy with the qubits swapped.

Returns
The original object with the qubits swapped if inplace is requested, or a copy of the original object with the qubits swapped otherwise.

Raises
`ValueError`	If the qubits are of different dimensionality.

`swap_target_tensor_for`

View source

swap_target_tensor_for(
    new_target_tensor: np.ndarray
)

THIS FUNCTION IS DEPRECATED.

IT WILL BE REMOVED IN cirq v0.16.

None, this function was unintentionally made public.

Gives a new state vector for the system.

    Typically, the new state vector should be `args.available_buffer` where
    `args` is this <a href="../cirq/ActOnStateVectorArgs"><code>cirq.ActOnStateVectorArgs</code></a> instance.

    Args:
        new_target_tensor: The new system state. Must have the same shape
            and dtype as the old system state.

`transpose_to_qubit_order`

View source

transpose_to_qubit_order(
    qubits: Sequence['cirq.Qid'], *, inplace=False
) -> TSelf

Physically reindexes the state by the new basis.

Args
`qubits`	The desired qubit order.
`inplace`	True to perform this operation inplace.

Returns
The state with qubit order transposed and underlying representation updated.

Raises
`ValueError`	If the provided qubits do not match the existing ones.

`with_qubits`

View source

with_qubits(
    qubits
) -> TSelf

Extend current state space with added qubits.

The state of the added qubits is the default value set in the subclasses. A new state space is created as the Kronecker product of the original one and the added one.

Args
`qubits`	The qubits to be added to the state space.

Regurns:

A new subclass object containing the extended state space.

`getitem`

View source

__getitem__(
    item: Optional['cirq.Qid']
) -> TSelf

Gets the item associated with the qubit.

`iter`

View source

__iter__() -> Iterator[Optional['cirq.Qid']]

Iterates the keys of the mapping.

`len`

View source

__len__() -> int

Gets the number of items in the mapping.

cirq.ActOnStateVectorArgs

Args

Attributes

Methods

apply_operation

copy

create_merged_state

factor

get_axes

kronecker_product

measure

rename

sample

subspace_index

swap

swap_target_tensor_for

transpose_to_qubit_order

with_qubits

Regurns:

__getitem__

__iter__

__len__

`apply_operation`

`copy`

`create_merged_state`

`factor`

`get_axes`

`kronecker_product`

`measure`

`rename`

`sample`

`subspace_index`

`swap`

`swap_target_tensor_for`

`transpose_to_qubit_order`

`with_qubits`

`getitem`

`iter`

`len`