Circuit¶
Circuit
¶
Bases: Module
The Circuit
object is a symbolic representation of a quantum circuit for qubits, qudits, or for an infinite-dimensional Fock space.
The circuit is composed of a sequence of quantum operators on wires
which define the evolution of the quantum
Attributes:
Name | Type | Description |
---|---|---|
ops |
dict[Union[str, int], AbstractOp]
|
A dictionary of ops (dictionary value) with an assigned label (dictionary key). |
Example
Source code in src/squint/circuit.py
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|
wires: set[int]
property
¶
Initializes a quantum circuit with the specified backend type.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
backend
|
Literal['pure', 'mixed']
|
The type of backend to use for the circuit. |
required |
subscripts: str
property
¶
Returns the einsum subscript expression as a string.
__init__(backend: Optional[Literal['pure', 'mixed']] = None)
¶
Initializes a quantum circuit with the specified backend type.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
backend
|
Literal['pure', 'mixed']
|
The type of backend to use for the circuit. |
None
|
Source code in src/squint/circuit.py
add(op: Union[AbstractOp, Block], key: str = None) -> None
¶
Add an operator to the circuit.
Operators are added sequential along the wires. The first operator on each wire must be a state (a subtype of AbstractPureState or AbstractMixedState).
Parameters:
Name | Type | Description | Default |
---|---|---|---|
op
|
AbstractOp
|
The operator instance to add to the circuit. |
required |
key
|
Optional[str]
|
A string key for indexing into the circuit PyTree instance. Defaults to |
None
|
Source code in src/squint/circuit.py
unwrap() -> tuple[AbstractOp]
¶
Unwrap all operators in the circuit by recursively calling the op.unwrap()
method.
path(dim: int, optimize: str = 'greedy')
¶
Computes the einsum contraction path using the opt_einsum
algorithm.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dim
|
int
|
The dimension of the local Hilbert space (the same dimension across all wires). |
required |
optimize
|
str
|
The argument to pass to |
'greedy'
|
Source code in src/squint/circuit.py
evaluate(dim: int)
¶
Evaluates the corresponding numerical tensor for each operator in the circuit, based on the provided dimension.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dim
|
int
|
The dimension of the local Hilbert space (the same dimension across all wires). |
required |
Source code in src/squint/circuit.py
verify()
¶
Performs a verification check on the circuit object to ensure it is valid prior to being compiled.
Source code in src/squint/circuit.py
compile(static: PyTree, dim: int, *params, **kwargs) -> Simulator
staticmethod
¶
Compiles the circuit into a tensor contraction function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
static
|
PyTree
|
The static PyTree, following the |
required |
dim
|
int
|
The dimension of the local Hilbert space (the same dimension across all wires). |
required |
params
|
Sequence[PyTree]
|
The parameterized PyTree, following the |
()
|
Returns:
Name | Type | Description |
---|---|---|
sim |
Simulator
|
A class which contains methods for computing the parameterized forward, grad, and Fisher information functions. |
Source code in src/squint/circuit.py
compile(static: PyTree, dim: int, *params, **kwargs)
¶
Compiles the circuit into a tensor contraction function.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
static
|
PyTree
|
The static PyTree, following the |
required |
dim
|
int
|
The dimension of the local Hilbert space (the same dimension across all wires). |
required |
params
|
Sequence[PyTree]
|
The parameterized PyTree, following the |
()
|
Returns:
Name | Type | Description |
---|---|---|
sim |
Simulator
|
A class which contains methods for computing the parameterized forward, grad, and Fisher information functions. |
Source code in src/squint/circuit.py
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|
subscripts_pure(circuit: Circuit)
¶
Source code in src/squint/circuit.py
subscripts_mixed(circuit: Circuit)
¶
Assigns the indices for all tensor legs when the circuit is includes mixed states, channels, and non-unitary evolution.
The canonical ordering of indices is (input_indices, output_indices)
Source code in src/squint/circuit.py
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|