tensorcircuit.mpscircuit#

Quantum circuit: MPS state simulator

class tensorcircuit.mpscircuit.MPSCircuit(nqubits: int, center_position: Optional[int] = None, tensors: Optional[Sequence[Any]] = None, wavefunction: Optional[Union[tensorcircuit.quantum.QuVector, Any]] = None, split: Optional[Dict[str, Any]] = None)[source]#

Bases: tensorcircuit.abstractcircuit.AbstractCircuit

MPSCircuit class. Simple usage demo below.

mps = tc.MPSCircuit(3)
mps.H(1)
mps.CNOT(0, 1)
mps.rx(2, theta=tc.num_to_tensor(1.))
mps.expectation((tc.gates.z(), 2))

ANY(*index: int, **vars: Any) → None#

Apply ANY gate with parameters on the circuit. See tensorcircuit.gates.any_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

CNOT(*index: int, **kws: Any) → None#

Apply CNOT gate on the circuit. See tensorcircuit.gates.cnot_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j \end{bmatrix}\end{split}\]

CPHASE(*index: int, **vars: Any) → None#

Apply CPHASE gate with parameters on the circuit. See tensorcircuit.gates.cphase_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

CR(*index: int, **vars: Any) → None#

Apply CR gate with parameters on the circuit. See tensorcircuit.gates.cr_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

CRX(*index: int, **vars: Any) → None#

Apply CRX gate with parameters on the circuit. See tensorcircuit.gates.crx_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

CRY(*index: int, **vars: Any) → None#

Apply CRY gate with parameters on the circuit. See tensorcircuit.gates.cry_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

CRZ(*index: int, **vars: Any) → None#

Apply CRZ gate with parameters on the circuit. See tensorcircuit.gates.crz_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

CU(*index: int, **vars: Any) → None#

Apply CU gate with parameters on the circuit. See tensorcircuit.gates.cu_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

CY(*index: int, **kws: Any) → None#

Apply CY gate on the circuit. See tensorcircuit.gates.cy_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.-1.j\\ 0.+0.j & 0.+0.j & 0.+1.j & 0.+0.j \end{bmatrix}\end{split}\]

CZ(*index: int, **kws: Any) → None#

Apply CZ gate on the circuit. See tensorcircuit.gates.cz_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & -1.+0.j \end{bmatrix}\end{split}\]

EXP(*index: int, **vars: Any) → None#

Apply EXP gate with parameters on the circuit. See tensorcircuit.gates.exp_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

EXP1(*index: int, **vars: Any) → None#

Apply EXP1 gate with parameters on the circuit. See tensorcircuit.gates.exp1_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

FREDKIN(*index: int, **kws: Any) → None#

Apply FREDKIN gate on the circuit. See tensorcircuit.gates.fredkin_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \end{bmatrix}\end{split}\]

H(*index: int, **kws: Any) → None#

Apply H gate on the circuit. See tensorcircuit.gates.h_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.70710677+0.j & 0.70710677+0.j\\ 0.70710677+0.j & -0.70710677+0.j \end{bmatrix}\end{split}\]

I(*index: int, **kws: Any) → None#

Apply I gate on the circuit. See tensorcircuit.gates.i_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j \end{bmatrix}\end{split}\]

ISWAP(*index: int, **vars: Any) → None#

Apply ISWAP gate with parameters on the circuit. See tensorcircuit.gates.iswap_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

MPO(*index: int, **vars: Any) → None#

Apply mpo gate in MPO format on the circuit. See tensorcircuit.gates.mpo_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

classmethod MPO_to_gate(tensors: Sequence[Any]) → tensorcircuit.gates.Gate[source]#: Convert MPO to gate

MULTICONTROL(*index: int, **vars: Any) → None#

Apply multicontrol gate in MPO format on the circuit. See tensorcircuit.gates.multicontrol_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

ORX(*index: int, **vars: Any) → None#

Apply ORX gate with parameters on the circuit. See tensorcircuit.gates.orx_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

ORY(*index: int, **vars: Any) → None#

Apply ORY gate with parameters on the circuit. See tensorcircuit.gates.ory_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

ORZ(*index: int, **vars: Any) → None#

Apply ORZ gate with parameters on the circuit. See tensorcircuit.gates.orz_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

OX(*index: int, **kws: Any) → None#

Apply OX gate on the circuit. See tensorcircuit.gates.ox_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \end{bmatrix}\end{split}\]

OY(*index: int, **kws: Any) → None#

Apply OY gate on the circuit. See tensorcircuit.gates.oy_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.+0.j & 0.-1.j & 0.+0.j & 0.+0.j\\ 0.+1.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \end{bmatrix}\end{split}\]

OZ(*index: int, **kws: Any) → None#

Apply OZ gate on the circuit. See tensorcircuit.gates.oz_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & -1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \end{bmatrix}\end{split}\]

PHASE(*index: int, **vars: Any) → None#

Apply PHASE gate with parameters on the circuit. See tensorcircuit.gates.phase_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

R(*index: int, **vars: Any) → None#

Apply R gate with parameters on the circuit. See tensorcircuit.gates.r_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

RX(*index: int, **vars: Any) → None#

Apply RX gate with parameters on the circuit. See tensorcircuit.gates.rx_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

RXX(*index: int, **vars: Any) → None#

Apply RXX gate with parameters on the circuit. See tensorcircuit.gates.rxx_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

RY(*index: int, **vars: Any) → None#

Apply RY gate with parameters on the circuit. See tensorcircuit.gates.ry_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

RYY(*index: int, **vars: Any) → None#

Apply RYY gate with parameters on the circuit. See tensorcircuit.gates.ryy_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

RZ(*index: int, **vars: Any) → None#

Apply RZ gate with parameters on the circuit. See tensorcircuit.gates.rz_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

RZZ(*index: int, **vars: Any) → None#

Apply RZZ gate with parameters on the circuit. See tensorcircuit.gates.rzz_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

S(*index: int, **kws: Any) → None#

Apply S gate on the circuit. See tensorcircuit.gates.s_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j\\ 0.+0.j & 0.+1.j \end{bmatrix}\end{split}\]

SD(*index: int, **kws: Any) → None#

Apply SD gate on the circuit. See tensorcircuit.gates.sd_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j\\ 0.+0.j & 0.-1.j \end{bmatrix}\end{split}\]

SWAP(*index: int, **kws: Any) → None#

Apply SWAP gate on the circuit. See tensorcircuit.gates.swap_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \end{bmatrix}\end{split}\]

T(*index: int, **kws: Any) → None#

Apply T gate on the circuit. See tensorcircuit.gates.t_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1. & +0.j & 0. & +0.j\\ 0. & +0.j & 0.70710677+0.70710677j \end{bmatrix}\end{split}\]

TD(*index: int, **kws: Any) → None#

Apply TD gate on the circuit. See tensorcircuit.gates.td_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1. & +0.j & 0. & +0.j\\ 0. & +0.j & 0.70710677-0.70710677j \end{bmatrix}\end{split}\]

TOFFOLI(*index: int, **kws: Any) → None#

Apply TOFFOLI gate on the circuit. See tensorcircuit.gates.toffoli_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j \end{bmatrix}\end{split}\]

U(*index: int, **vars: Any) → None#

Apply U gate with parameters on the circuit. See tensorcircuit.gates.u_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

WROOT(*index: int, **kws: Any) → None#

Apply WROOT gate on the circuit. See tensorcircuit.gates.wroot_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.70710677+0.j & -0.5 & -0.5j\\ 0.5 & -0.5j & 0.70710677+0.j \end{bmatrix}\end{split}\]

X(*index: int, **kws: Any) → None#

Apply X gate on the circuit. See tensorcircuit.gates.x_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.+0.j & 1.+0.j\\ 1.+0.j & 0.+0.j \end{bmatrix}\end{split}\]

Y(*index: int, **kws: Any) → None#

Apply Y gate on the circuit. See tensorcircuit.gates.y_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.+0.j & 0.-1.j\\ 0.+1.j & 0.+0.j \end{bmatrix}\end{split}\]

Z(*index: int, **kws: Any) → None#

Apply Z gate on the circuit. See tensorcircuit.gates.z_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j\\ 0.+0.j & -1.+0.j \end{bmatrix}\end{split}\]

__init__(nqubits: int, center_position: Optional[int] = None, tensors: Optional[Sequence[Any]] = None, wavefunction: Optional[Union[tensorcircuit.quantum.QuVector, Any]] = None, split: Optional[Dict[str, Any]] = None) → None[source]#

MPSCircuit object based on state simulator.

Parameters

nqubits (int) – The number of qubits in the circuit.
center_position (int, optional) – The center position of MPS, default to 0
tensors (Sequence[Tensor], optional) – If not None, the initial state of the circuit is taken as tensors instead of \(\vert 0\rangle^n\) qubits, defaults to None. When tensors are specified, if center_position is None, then the tensors are canonicalized, otherwise it is assumed the tensors are already canonicalized at the center_position
wavefunction (Tensor) – If not None, it is transformed to the MPS form according to the split rules
split (Any) – Split rules

amplitude(l: str) → Any[source]#

any(*index: int, **vars: Any) → None#

Apply ANY gate with parameters on the circuit. See tensorcircuit.gates.any_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

append(c: tensorcircuit.abstractcircuit.AbstractCircuit, indices: Optional[List[int]] = None) → tensorcircuit.abstractcircuit.AbstractCircuit#

append circuit c before

Example

>>> c1 = tc.Circuit(2)
>>> c1.H(0)
>>> c1.H(1)
>>> c2 = tc.Circuit(2)
>>> c2.cnot(0, 1)
>>> c1.append(c2)
<tensorcircuit.circuit.Circuit object at 0x7f8402968970>
>>> c1.draw()
    ┌───┐
q_0:┤ H ├──■──
    ├───┤┌─┴─┐
q_1:┤ H ├┤ X ├
    └───┘└───┘

Parameters

c (BaseCircuit) – The other circuit to be appended
indices (Optional[List[int]], optional) – the qubit indices to which c is appended on. Defaults to None, which means plain concatenation.

Returns

The composed circuit

Return type

BaseCircuit

append_from_qir(qir: List[Dict[str, Any]]) → None#

Apply the ciurict in form of quantum intermediate representation after the current cirucit.

Example

>>> c = tc.Circuit(3)
>>> c.H(0)
>>> c.to_qir()
[{'gatef': h, 'gate': Gate(...), 'index': (0,), 'name': 'h', 'split': None, 'mpo': False}]
>>> c2 = tc.Circuit(3)
>>> c2.CNOT(0, 1)
>>> c2.to_qir()
[{'gatef': cnot, 'gate': Gate(...), 'index': (0, 1), 'name': 'cnot', 'split': None, 'mpo': False}]
>>> c.append_from_qir(c2.to_qir())
>>> c.to_qir()
[{'gatef': h, 'gate': Gate(...), 'index': (0,), 'name': 'h', 'split': None, 'mpo': False},
 {'gatef': cnot, 'gate': Gate(...), 'index': (0, 1), 'name': 'cnot', 'split': None, 'mpo': False}]

Parameters: qir (List[Dict[str, Any]]) – The quantum intermediate representation.

apply(gate: Union[tensorcircuit.gates.Gate, tensorcircuit.quantum.QuOperator], *index: int, name: Optional[str] = None, split: Optional[Dict[str, Any]] = None, mpo: bool = False, ir_dict: Optional[Dict[str, Any]] = None) → None#

Apply a general qubit gate on MPS.

Parameters

gate (Gate) – The Gate to be applied
index (int) – Qubit indices of the gate

Raises

ValueError – “MPS does not support application of gate on > 2 qubits.”

apply_MPO(tensors: Sequence[Any], index_left: int, center_left: bool = True, split: Optional[Dict[str, Any]] = None) → None[source]#: Apply a MPO to the MPS

apply_adjacent_double_gate(gate: tensorcircuit.gates.Gate, index1: int, index2: int, center_position: Optional[int] = None, split: Optional[Dict[str, Any]] = None) → None[source]#

Apply a double qubit gate on adjacent qubits of Matrix Product States (MPS).

Parameters

gate (Gate) – The Gate to be applied
index1 (int) – The first qubit index of the gate
index2 (int) – The second qubit index of the gate
center_position (Optional[int]) – Center position of MPS, default is None

apply_double_gate(gate: tensorcircuit.gates.Gate, index1: int, index2: int, split: Optional[Dict[str, Any]] = None) → None[source]#

Apply a double qubit gate on MPS.

Parameters

gate (Gate) – The Gate to be applied
index1 (int) – The first qubit index of the gate
index2 (int) – The second qubit index of the gate

apply_general_gate(gate: Union[tensorcircuit.gates.Gate, tensorcircuit.quantum.QuOperator], *index: int, name: Optional[str] = None, split: Optional[Dict[str, Any]] = None, mpo: bool = False, ir_dict: Optional[Dict[str, Any]] = None) → None[source]#

Apply a general qubit gate on MPS.

Parameters

gate (Gate) – The Gate to be applied
index (int) – Qubit indices of the gate

Raises

ValueError – “MPS does not support application of gate on > 2 qubits.”

static apply_general_gate_delayed(gatef: Callable[[], tensorcircuit.gates.Gate], name: Optional[str] = None, mpo: bool = False) → Callable[[...], None]#

static apply_general_variable_gate_delayed(gatef: Callable[[...], tensorcircuit.gates.Gate], name: Optional[str] = None, mpo: bool = False) → Callable[[...], None]#

apply_nqubit_gate(gate: tensorcircuit.gates.Gate, *index: int, split: Optional[Dict[str, Any]] = None) → None[source]#: Apply a n-qubit gate by transforming the gate to MPO

apply_single_gate(gate: tensorcircuit.gates.Gate, index: int) → None[source]#

Apply a single qubit gate on MPS; no truncation is needed.

Parameters

gate (Gate) – gate to be applied
index (int) – Qubit index of the gate

barrier_instruction(*index: List[int]) → None#

add a barrier instruction flag, no effect on numerical simulation

Parameters: index (List[int]) – the corresponding qubits

ccnot(*index: int, **kws: Any) → None#

Apply TOFFOLI gate on the circuit. See tensorcircuit.gates.toffoli_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j \end{bmatrix}\end{split}\]

ccx(*index: int, **kws: Any) → None#

Apply TOFFOLI gate on the circuit. See tensorcircuit.gates.toffoli_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j \end{bmatrix}\end{split}\]

circuit_param: Dict[str, Any]#

cnot(*index: int, **kws: Any) → None#

Apply CNOT gate on the circuit. See tensorcircuit.gates.cnot_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j \end{bmatrix}\end{split}\]

cond_measure(index: int) → Any#

Measurement on z basis at index qubit based on quantum amplitude (not post-selection). The highlight is that this method can return the measured result as a int Tensor and thus maintained a jittable pipeline.

Example

>>> c = tc.Circuit(2)
>>> c.H(0)
>>> r = c.cond_measurement(0)
>>> c.conditional_gate(r, [tc.gates.i(), tc.gates.x()], 1)
>>> c.expectation([tc.gates.z(), [0]]), c.expectation([tc.gates.z(), [1]])
# two possible outputs: (1, 1) or (-1, -1)

Note

In terms of DMCircuit, this method returns nothing and the density matrix after this method is kept in mixed state without knowing the measuremet resuslts

Parameters: index (int) – the qubit for the z-basis measurement
Returns: 0 or 1 for z measurement on up and down freedom
Return type: Tensor

cond_measurement(index: int) → Any#

Example

>>> c = tc.Circuit(2)
>>> c.H(0)
>>> r = c.cond_measurement(0)
>>> c.conditional_gate(r, [tc.gates.i(), tc.gates.x()], 1)
>>> c.expectation([tc.gates.z(), [0]]), c.expectation([tc.gates.z(), [1]])
# two possible outputs: (1, 1) or (-1, -1)

Note

In terms of DMCircuit, this method returns nothing and the density matrix after this method is kept in mixed state without knowing the measuremet resuslts

Parameters: index (int) – the qubit for the z-basis measurement
Returns: 0 or 1 for z measurement on up and down freedom
Return type: Tensor

conditional_gate(which: Any, kraus: Sequence[tensorcircuit.gates.Gate], *index: int) → None#

Apply which-th gate from kraus list, i.e. apply kraus[which]

Parameters

which (Tensor) – Tensor of shape [] and dtype int
kraus (Sequence[Gate]) – A list of gate in the form of tc.gate or Tensor
index (int) – the qubit lines the gate applied on

conj() → tensorcircuit.mpscircuit.MPSCircuit[source]#

Compute the conjugate of the current MPS.

Returns: The constructed MPS
Return type: MPSCircuit

consecutive_swap(index_from: int, index_to: int, split: Optional[Dict[str, Any]] = None) → None[source]#

copy() → tensorcircuit.mpscircuit.MPSCircuit[source]#

Copy the current MPS.

Returns: The constructed MPS
Return type: MPSCircuit

copy_without_tensor() → tensorcircuit.mpscircuit.MPSCircuit[source]#

Copy the current MPS without the tensors.

Returns: The constructed MPS
Return type: MPSCircuit

cphase(*index: int, **vars: Any) → None#

Apply CPHASE gate with parameters on the circuit. See tensorcircuit.gates.cphase_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

cr(*index: int, **vars: Any) → None#

Apply CR gate with parameters on the circuit. See tensorcircuit.gates.cr_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

crx(*index: int, **vars: Any) → None#

Apply CRX gate with parameters on the circuit. See tensorcircuit.gates.crx_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

cry(*index: int, **vars: Any) → None#

Apply CRY gate with parameters on the circuit. See tensorcircuit.gates.cry_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

crz(*index: int, **vars: Any) → None#

Apply CRZ gate with parameters on the circuit. See tensorcircuit.gates.crz_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

cswap(*index: int, **kws: Any) → None#

Apply FREDKIN gate on the circuit. See tensorcircuit.gates.fredkin_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

cu(*index: int, **vars: Any) → None#

Apply CU gate with parameters on the circuit. See tensorcircuit.gates.cu_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

cx(*index: int, **kws: Any) → None#

Apply CNOT gate on the circuit. See tensorcircuit.gates.cnot_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j \end{bmatrix}\end{split}\]

cy(*index: int, **kws: Any) → None#

Apply CY gate on the circuit. See tensorcircuit.gates.cy_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.-1.j\\ 0.+0.j & 0.+0.j & 0.+1.j & 0.+0.j \end{bmatrix}\end{split}\]

cz(*index: int, **kws: Any) → None#

Apply CZ gate on the circuit. See tensorcircuit.gates.cz_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

draw(**kws: Any) → Any#

Visualise the circuit. This method recevies the keywords as same as qiskit.circuit.QuantumCircuit.draw. More details can be found here: https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.draw.html. Interesting kws options include: ``idle_wires``(bool)

Example

>>> c = tc.Circuit(3)
>>> c.H(1)
>>> c.X(2)
>>> c.CNOT(0, 1)
>>> c.draw(output='text')
q_0: ───────■──
     ┌───┐┌─┴─┐
q_1: ┤ H ├┤ X ├
     ├───┤└───┘
q_2: ┤ X ├─────
     └───┘

exp(*index: int, **vars: Any) → None#

Apply EXP gate with parameters on the circuit. See tensorcircuit.gates.exp_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

exp1(*index: int, **vars: Any) → None#

Apply EXP1 gate with parameters on the circuit. See tensorcircuit.gates.exp1_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

expectation(*ops: Tuple[tensorcircuit.gates.Gate, List[int]], reuse: bool = True, other: Optional[tensorcircuit.mpscircuit.MPSCircuit] = None, conj: bool = True, normalize: bool = False, split: Optional[Dict[str, Any]] = None, **kws: Any) → Any[source]#

Compute the expectation of corresponding operators in the form of tensor.

Parameters

ops (Tuple[tn.Node, List[int]]) – Operator and its position on the circuit, eg. (gates.Z(), [1]), (gates.X(), [2]) is for operator \(Z_1X_2\)
reuse (bool, optional) – If True, then the wavefunction tensor is cached for further expectation evaluation, defaults to be true.
other (MPSCircuit, optional) – If not None, will be used as bra
conj (bool, defaults to be True) – Whether to conjugate the bra state
normalize (bool, defaults to be True) – Whether to normalize the MPS
split (Any) – Truncation split

Returns

The expectation of corresponding operators

Return type

Tensor

expectation_ps(x: Optional[Sequence[int]] = None, y: Optional[Sequence[int]] = None, z: Optional[Sequence[int]] = None, ps: Optional[Sequence[int]] = None, reuse: bool = True, noise_conf: Optional[Any] = None, nmc: int = 1000, status: Optional[Any] = None, **kws: Any) → Any#

Shortcut for Pauli string expectation. x, y, z list are for X, Y, Z positions

Example

>>> c = tc.Circuit(2)
>>> c.X(0)
>>> c.H(1)
>>> c.expectation_ps(x=[1], z=[0])
array(-0.99999994+0.j, dtype=complex64)

>>> c = tc.Circuit(2)
>>> c.cnot(0, 1)
>>> c.rx(0, theta=0.4)
>>> c.rx(1, theta=0.8)
>>> c.h(0)
>>> c.h(1)
>>> error1 = tc.channels.generaldepolarizingchannel(0.1, 1)
>>> error2 = tc.channels.generaldepolarizingchannel(0.06, 2)
>>> noise_conf = NoiseConf()
>>> noise_conf.add_noise("rx", error1)
>>> noise_conf.add_noise("cnot", [error2], [[0, 1]])
>>> c.expectation_ps(x=[0], noise_conf=noise_conf, nmc=10000)
(0.46274087-3.764033e-09j)

Parameters

x (Optional[Sequence[int]], optional) – sites to apply X gate, defaults to None
y (Optional[Sequence[int]], optional) – sites to apply Y gate, defaults to None
z (Optional[Sequence[int]], optional) – sites to apply Z gate, defaults to None
ps (Optional[Sequence[int]], optional) – or one can apply a ps structures instead of x, y, z, e.g. [0, 1, 3, 0, 2, 2] for X_1Z_2Y_4Y_5 defaults to None, ps can overwrite x, y and z
reuse (bool, optional) – whether to cache and reuse the wavefunction, defaults to True
noise_conf (Optional[NoiseConf], optional) – Noise Configuration, defaults to None
nmc (int, optional) – repetition time for Monte Carlo sampling for noisfy calculation, defaults to 1000
status (Optional[Tensor], optional) – external randomness given by tensor uniformly from [0, 1], defaults to None, used for noisfy circuit sampling

Returns

Expectation value

Return type

Tensor

fredkin(*index: int, **kws: Any) → None#

Apply FREDKIN gate on the circuit. See tensorcircuit.gates.fredkin_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

classmethod from_json(jsonstr: str, circuit_params: Optional[Dict[str, Any]] = None) → tensorcircuit.abstractcircuit.AbstractCircuit#

load json str as a Circuit

Parameters

jsonstr (str) – _description_
circuit_params (Optional[Dict[str, Any]], optional) – Extra circuit parameters in the format of __init__, defaults to None

Returns

_description_

Return type

AbstractCircuit

classmethod from_json_file(file: str, circuit_params: Optional[Dict[str, Any]] = None) → tensorcircuit.abstractcircuit.AbstractCircuit#

load json file and convert it to a circuit

Parameters

file (str) – filename
circuit_params (Optional[Dict[str, Any]], optional) – _description_, defaults to None

Returns

_description_

Return type

AbstractCircuit

classmethod from_openqasm(qasmstr: str, circuit_params: Optional[Dict[str, Any]] = None, keep_measure_order: bool = False) → tensorcircuit.abstractcircuit.AbstractCircuit#

classmethod from_openqasm_file(file: str, circuit_params: Optional[Dict[str, Any]] = None, keep_measure_order: bool = False) → tensorcircuit.abstractcircuit.AbstractCircuit#

classmethod from_qir(qir: List[Dict[str, Any]], circuit_params: Optional[Dict[str, Any]] = None) → tensorcircuit.abstractcircuit.AbstractCircuit#

Restore the circuit from the quantum intermediate representation.

Example

>>> c = tc.Circuit(3)
>>> c.H(0)
>>> c.rx(1, theta=tc.array_to_tensor(0.7))
>>> c.exp1(0, 1, unitary=tc.gates._zz_matrix, theta=tc.array_to_tensor(-0.2), split=split)
>>> len(c)
7
>>> c.expectation((tc.gates.z(), [1]))
array(0.764842+0.j, dtype=complex64)
>>> qirs = c.to_qir()
>>>
>>> c = tc.Circuit.from_qir(qirs, circuit_params={"nqubits": 3})
>>> len(c._nodes)
7
>>> c.expectation((tc.gates.z(), [1]))
array(0.764842+0.j, dtype=complex64)

Parameters

qir (List[Dict[str, Any]]) – The quantum intermediate representation of a circuit.
circuit_params (Optional[Dict[str, Any]]) – Extra circuit parameters.

Returns

The circuit have same gates in the qir.

Return type

Circuit

classmethod from_qiskit(qc: Any, n: Optional[int] = None, inputs: Optional[List[float]] = None, circuit_params: Optional[Dict[str, Any]] = None, binding_params: Optional[Union[Sequence[float], Dict[Any, float]]] = None) → tensorcircuit.abstractcircuit.AbstractCircuit#

Import Qiskit QuantumCircuit object as a tc.Circuit object.

Example

>>> from qiskit import QuantumCircuit
>>> qisc = QuantumCircuit(3)
>>> qisc.h(2)
>>> qisc.cswap(1, 2, 0)
>>> qisc.swap(0, 1)
>>> c = tc.Circuit.from_qiskit(qisc)

Parameters

qc (QuantumCircuit in Qiskit) – Qiskit Circuit object
n (int) – The number of qubits for the circuit
inputs (Optional[List[float]], optional) – possible input wavefunction for tc.Circuit, defaults to None
circuit_params (Optional[Dict[str, Any]]) – kwargs given in Circuit.__init__ construction function, default to None.
binding_params (Optional[Union[Sequence[float], Dict[Any, float]]]) – (variational) parameters for the circuit. Could be either a sequence or dictionary depending on the type of parameters in the Qiskit circuit. For ParameterVectorElement use sequence. For Parameter use dictionary

Returns

The same circuit but as tensorcircuit object

Return type

Circuit

classmethod from_qsim_file(file: str, circuit_params: Optional[Dict[str, Any]] = None) → tensorcircuit.abstractcircuit.AbstractCircuit#

gate_aliases = [['cnot', 'cx'], ['fredkin', 'cswap'], ['toffoli', 'ccnot'], ['toffoli', 'ccx'], ['any', 'unitary'], ['sd', 'sdg'], ['td', 'tdg']]#

gate_count(gate_list: Optional[Union[str, Sequence[str]]] = None) → int#

count the gate number of the circuit

Example

>>> c = tc.Circuit(3)
>>> c.h(0)
>>> c.multicontrol(0, 1, 2, ctrl=[0, 1], unitary=tc.gates._x_matrix)
>>> c.toffolli(1, 2, 0)
>>> c.gate_count()
3
>>> c.gate_count(["multicontrol", "toffoli"])
2

Parameters: gate_list (Optional[Sequence[str]], optional) – gate name or gate name list to be counted, defaults to None (counting all gates)
Returns: the total number of all gates or gates in the gate_list
Return type: int

gate_count_by_condition(cond_func: Callable[[Dict[str, Any]], bool]) → int#

count the number of gates that satisfy certain condition

Example

>>> c = tc.Circuit(3)
>>> c.x(0)
>>> c.h(0)
>>> c.multicontrol(0, 1, 2, ctrl=[0, 1], unitary=tc.gates._x_matrix)
>>> c.gate_count_by_condition(lambda qir: qir["index"] == (0, ))
2
>>> c.gate_count_by_condition(lambda qir: qir["mpo"])
1

Parameters: cond_func (Callable[[Dict[str, Any]], bool]) – the condition for counting the gate
Returns: the total number of all gates which satisfy the condition
Return type: int

gate_summary() → Dict[str, int]#

return the summary dictionary on gate type - gate count pair

Returns: the gate count dict by gate type
Return type: Dict[str, int]

classmethod gate_to_MPO(gate: Union[tensorcircuit.gates.Gate, Any], *index: int) → Tuple[Sequence[Any], int][source]#: Convert gate to MPO form with identities at empty sites

get_bond_dimensions() → Any[source]#

Get the MPS bond dimensions

Returns: MPS tensors
Return type: Tensor

get_center_position() → Optional[int][source]#

Get the center position of the MPS

Returns: center position
Return type: Optional[int]

get_norm() → Any[source]#

Get the normalized Center Position.

Returns: Normalized Center Position.
Return type: Tensor

get_positional_logical_mapping() → Dict[int, int]#

Get positional logical mapping dict based on measure instruction. This function is useful when we only measure part of the qubits in the circuit, to process the count result from partial measurement, we must be aware of the mapping, i.e. for each position in the count bitstring, what is the corresponding qubits (logical) defined on the circuit

Returns: positional_logical_mapping
Return type: Dict[int, int]

get_quvector() → tensorcircuit.quantum.QuVector[source]#

Get the representation of the output state in the form of QuVector: has to be full contracted in MPS

Returns: QuVector representation of the output state from the circuit
Return type: QuVector

get_tensors() → List[Any][source]#

Get the MPS tensors

Returns: MPS tensors
Return type: List[Tensor]

h(*index: int, **kws: Any) → None#

Apply H gate on the circuit. See tensorcircuit.gates.h_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.70710677+0.j & 0.70710677+0.j\\ 0.70710677+0.j & -0.70710677+0.j \end{bmatrix}\end{split}\]

i(*index: int, **kws: Any) → None#

Apply I gate on the circuit. See tensorcircuit.gates.i_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j \end{bmatrix}\end{split}\]

initial_mapping(logical_physical_mapping: Dict[int, int], n: Optional[int] = None, circuit_params: Optional[Dict[str, Any]] = None) → tensorcircuit.abstractcircuit.AbstractCircuit#

generate a new circuit with the qubit mapping given by logical_physical_mapping

Parameters

logical_physical_mapping (Dict[int, int]) – how to map logical qubits to the physical qubits on the new circuit
n (Optional[int], optional) – number of qubit of the new circuit, can be different from the original one, defaults to None
circuit_params (Optional[Dict[str, Any]], optional) – _description_, defaults to None

Returns

_description_

Return type

AbstractCircuit

inputs: Any#

inverse(circuit_params: Optional[Dict[str, Any]] = None) → tensorcircuit.abstractcircuit.AbstractCircuit#

inverse the circuit, return a new inversed circuit

EXAMPLE

>>> c = tc.Circuit(2)
>>> c.H(0)
>>> c.rzz(1, 2, theta=0.8)
>>> c1 = c.inverse()

Parameters: circuit_params (Optional[Dict[str, Any]], optional) – keywords dict for initialization the new circuit, defaults to None
Returns: the inversed circuit
Return type: Circuit

is_mps: bool = True#

is_valid() → bool[source]#

Check whether the circuit is legal.

Returns: Whether the circuit is legal.
Return type: bool

iswap(*index: int, **vars: Any) → None#

Apply ISWAP gate with parameters on the circuit. See tensorcircuit.gates.iswap_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

measure(*index: int, with_prob: bool = False, status: Optional[Any] = None) → Tuple[Any, Any][source]#

Take measurement to the given quantum lines.

Parameters

index (int) – Measure on which quantum line.
with_prob (bool, optional) – If true, theoretical probability is also returned.
status (Optional[Tensor]) – external randomness, with shape [index], defaults to None

Returns

The sample output and probability (optional) of the quantum line.

Return type

Tuple[Tensor, Tensor]

measure_instruction(*index: int) → None#

add a measurement instruction flag, no effect on numerical simulation

Parameters: index (int) – the corresponding qubits

mid_measurement(index: int, keep: int = 0) → None[source]#

Middle measurement in the z-basis on the circuit, note the wavefunction output is not normalized with mid_measurement involved, one should normalized the state manually if needed.

Parameters

index (int) – The index of qubit that the Z direction postselection applied on
keep (int, optional) – 0 for spin up, 1 for spin down, defaults to 0

mpo(*index: int, **vars: Any) → None#

Apply mpo gate in MPO format on the circuit. See tensorcircuit.gates.mpo_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

mpogates = ['multicontrol', 'mpo']#

multicontrol(*index: int, **vars: Any) → None#

Apply multicontrol gate in MPO format on the circuit. See tensorcircuit.gates.multicontrol_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

normalize() → None[source]#: Normalize MPS Circuit according to the center position.

orx(*index: int, **vars: Any) → None#

Apply ORX gate with parameters on the circuit. See tensorcircuit.gates.orx_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

ory(*index: int, **vars: Any) → None#

Apply ORY gate with parameters on the circuit. See tensorcircuit.gates.ory_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

orz(*index: int, **vars: Any) → None#

Apply ORZ gate with parameters on the circuit. See tensorcircuit.gates.orz_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

ox(*index: int, **kws: Any) → None#

Apply OX gate on the circuit. See tensorcircuit.gates.ox_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \end{bmatrix}\end{split}\]

oy(*index: int, **kws: Any) → None#

Apply OY gate on the circuit. See tensorcircuit.gates.oy_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.+0.j & 0.-1.j & 0.+0.j & 0.+0.j\\ 0.+1.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \end{bmatrix}\end{split}\]

oz(*index: int, **kws: Any) → None#

Apply OZ gate on the circuit. See tensorcircuit.gates.oz_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

phase(*index: int, **vars: Any) → None#

Apply PHASE gate with parameters on the circuit. See tensorcircuit.gates.phase_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

position(site: int) → None[source]#

Wrapper of tn.FiniteMPS.position. Set orthogonality center.

Parameters: site (int) – The orthogonality center

prepend(c: tensorcircuit.abstractcircuit.AbstractCircuit) → tensorcircuit.abstractcircuit.AbstractCircuit#

prepend circuit c before

Parameters: c (BaseCircuit) – The other circuit to be prepended
Returns: The composed circuit
Return type: BaseCircuit

proj_with_mps(other: tensorcircuit.mpscircuit.MPSCircuit, conj: bool = True) → Any[source]#

Compute the projection between other as bra and self as ket.

Parameters: other (MPSCircuit) – ket of the other MPS, which will be converted to bra automatically
Returns: The projection in form of tensor
Return type: Tensor

r(*index: int, **vars: Any) → None#

Apply R gate with parameters on the circuit. See tensorcircuit.gates.r_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

reduce_dimension(index_left: int, center_left: bool = True, split: Optional[Dict[str, Any]] = None) → None[source]#: Reduce the bond dimension between two adjacent sites by SVD

classmethod reduce_tensor_dimension(tensor_left: Any, tensor_right: Any, center_left: bool = True, split: Optional[Dict[str, Any]] = None) → Tuple[Any, Any][source]#: Reduce the bond dimension between two general tensors by SVD

reset_instruction(*index: int) → None#

add a reset instruction flag, no effect on numerical simulation

Parameters: index (int) – the corresponding qubits

rx(*index: int, **vars: Any) → None#

Apply RX gate with parameters on the circuit. See tensorcircuit.gates.rx_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

rxx(*index: int, **vars: Any) → None#

Apply RXX gate with parameters on the circuit. See tensorcircuit.gates.rxx_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

ry(*index: int, **vars: Any) → None#

Apply RY gate with parameters on the circuit. See tensorcircuit.gates.ry_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

ryy(*index: int, **vars: Any) → None#

Apply RYY gate with parameters on the circuit. See tensorcircuit.gates.ryy_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

rz(*index: int, **vars: Any) → None#

Apply RZ gate with parameters on the circuit. See tensorcircuit.gates.rz_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

rzz(*index: int, **vars: Any) → None#

Apply RZZ gate with parameters on the circuit. See tensorcircuit.gates.rzz_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

s(*index: int, **kws: Any) → None#

Apply S gate on the circuit. See tensorcircuit.gates.s_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j\\ 0.+0.j & 0.+1.j \end{bmatrix}\end{split}\]

sd(*index: int, **kws: Any) → None#

Apply SD gate on the circuit. See tensorcircuit.gates.sd_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j\\ 0.+0.j & 0.-1.j \end{bmatrix}\end{split}\]

sdg(*index: int, **kws: Any) → None#

Apply SD gate on the circuit. See tensorcircuit.gates.sd_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j\\ 0.+0.j & 0.-1.j \end{bmatrix}\end{split}\]

select_gate(which: Any, kraus: Sequence[tensorcircuit.gates.Gate], *index: int) → None#

Apply which-th gate from kraus list, i.e. apply kraus[which]

Parameters

which (Tensor) – Tensor of shape [] and dtype int
kraus (Sequence[Gate]) – A list of gate in the form of tc.gate or Tensor
index (int) – the qubit lines the gate applied on

set_split_rules(split: Dict[str, Any]) → None[source]#

Set truncation split when double qubit gates are applied. If nothing is specified, no truncation will take place and the bond dimension will keep growing. For more details, refer to split_tensor.

Parameters: split (Any) – Truncation split

sgates = ['i', 'x', 'y', 'z', 'h', 't', 's', 'td', 'sd', 'wroot', 'cnot', 'cz', 'swap', 'cy', 'ox', 'oy', 'oz', 'toffoli', 'fredkin']#

slice(begin: int, end: int) → tensorcircuit.mpscircuit.MPSCircuit[source]#: Get a slice of the MPS (only for internal use)

static standardize_gate(name: str) → str#

standardize the gate name to tc common gate sets

Parameters: name (str) – non-standard gate name
Returns: the standard gate name
Return type: str

state(form: str = 'default') → Any#

Compute the output wavefunction from the circuit.

Parameters: form (str, optional) – the str indicating the form of the output wavefunction
Returns: Tensor with shape [1, -1]
Return type: Tensor a b ab | | ||

i–A–B–j -> i–XX–j

swap(*index: int, **kws: Any) → None#

Apply SWAP gate on the circuit. See tensorcircuit.gates.swap_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j \end{bmatrix}\end{split}\]

t(*index: int, **kws: Any) → None#

Apply T gate on the circuit. See tensorcircuit.gates.t_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1. & +0.j & 0. & +0.j\\ 0. & +0.j & 0.70710677+0.70710677j \end{bmatrix}\end{split}\]

td(*index: int, **kws: Any) → None#

Apply TD gate on the circuit. See tensorcircuit.gates.td_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1. & +0.j & 0. & +0.j\\ 0. & +0.j & 0.70710677-0.70710677j \end{bmatrix}\end{split}\]

tdg(*index: int, **kws: Any) → None#

Apply TD gate on the circuit. See tensorcircuit.gates.td_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1. & +0.j & 0. & +0.j\\ 0. & +0.j & 0.70710677-0.70710677j \end{bmatrix}\end{split}\]

tex(**kws: Any) → str#

Generate latex string based on quantikz latex package

Returns: Latex string that can be directly compiled via, e.g. latexit
Return type: str

to_cirq(enable_instruction: bool = False) → Any#

Translate tc.Circuit to a cirq circuit object.

Parameters: enable_instruction (bool, defaults to False) – whether also export measurement and reset instructions
Returns: A cirq circuit of this circuit.

to_json(file: Optional[str] = None, simplified: bool = False) → Any#

circuit dumps to json

Parameters

file (Optional[str], optional) – file str to dump the json to, defaults to None, return the json str
simplified (bool) – If False, keep all info for each gate, defaults to be False. If True, suitable for IO since less information is required

Returns

None if dumps to file otherwise the json str

Return type

Any

to_openqasm(**kws: Any) → str#

transform circuit to openqasm via qiskit circuit, see https://qiskit.org/documentation/stubs/qiskit.circuit.QuantumCircuit.qasm.html for usage on possible options for kws

Returns: circuit representation in openqasm format
Return type: str

to_qir() → List[Dict[str, Any]]#

Return the quantum intermediate representation of the circuit.

Example

>>> c = tc.Circuit(2)
>>> c.CNOT(0, 1)
>>> c.to_qir()
[{'gatef': cnot, 'gate': Gate(
    name: 'cnot',
    tensor:
        array([[[[1.+0.j, 0.+0.j],
                [0.+0.j, 0.+0.j]],

                [[0.+0.j, 1.+0.j],
                [0.+0.j, 0.+0.j]]],


            [[[0.+0.j, 0.+0.j],
                [0.+0.j, 1.+0.j]],

                [[0.+0.j, 0.+0.j],
                [1.+0.j, 0.+0.j]]]], dtype=complex64),
    edges: [
        Edge(Dangling Edge)[0],
        Edge(Dangling Edge)[1],
        Edge('cnot'[2] -> 'qb-1'[0] ),
        Edge('cnot'[3] -> 'qb-2'[0] )
    ]), 'index': (0, 1), 'name': 'cnot', 'split': None, 'mpo': False}]

Returns: The quantum intermediate representation of the circuit.
Return type: List[Dict[str, Any]]

to_qiskit(enable_instruction: bool = False, enable_inputs: bool = False) → Any#

Translate tc.Circuit to a qiskit QuantumCircuit object.

Parameters

enable_instruction (bool, defaults to False) – whether also export measurement and reset instructions
enable_inputs (bool, defaults to False) – whether also export the inputs

Returns

A qiskit object of this circuit.

toffoli(*index: int, **kws: Any) → None#

Apply TOFFOLI gate on the circuit. See tensorcircuit.gates.toffoli_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j & 0.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j\\ 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 0.+0.j & 1.+0.j & 0.+0.j \end{bmatrix}\end{split}\]

u(*index: int, **vars: Any) → None#

Apply U gate with parameters on the circuit. See tensorcircuit.gates.u_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

unitary(*index: int, **vars: Any) → None#

Apply ANY gate with parameters on the circuit. See tensorcircuit.gates.any_gate().

Parameters

index (int.) – Qubit number that the gate applies on.
vars (float.) – Parameters for the gate.

vgates = ['r', 'cr', 'u', 'cu', 'rx', 'ry', 'rz', 'phase', 'rxx', 'ryy', 'rzz', 'cphase', 'crx', 'cry', 'crz', 'orx', 'ory', 'orz', 'iswap', 'any', 'exp', 'exp1']#

vis_tex(**kws: Any) → str#

Generate latex string based on quantikz latex package

Returns: Latex string that can be directly compiled via, e.g. latexit
Return type: str

wavefunction(form: str = 'default') → Any[source]#

Compute the output wavefunction from the circuit.

Parameters: form (str, optional) – the str indicating the form of the output wavefunction
Returns: Tensor with shape [1, -1]
Return type: Tensor a b ab | | ||

i–A–B–j -> i–XX–j

classmethod wavefunction_to_tensors(wavefunction: Any, dim_phys: int = 2, norm: bool = True, split: Optional[Dict[str, Any]] = None) → List[Any][source]#

Construct the MPS tensors from a given wavefunction.

Parameters

wavefunction (Tensor) – The given wavefunction (any shape is OK)
split (Dict) – Truncation split
dim_phys (int) – Physical dimension, 2 for MPS and 4 for MPO
norm (bool) – Whether to normalize the wavefunction

Returns

The tensors

Return type

List[Tensor]

wroot(*index: int, **kws: Any) → None#

Apply WROOT gate on the circuit. See tensorcircuit.gates.wroot_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.70710677+0.j & -0.5 & -0.5j\\ 0.5 & -0.5j & 0.70710677+0.j \end{bmatrix}\end{split}\]

x(*index: int, **kws: Any) → None#

Apply X gate on the circuit. See tensorcircuit.gates.x_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.+0.j & 1.+0.j\\ 1.+0.j & 0.+0.j \end{bmatrix}\end{split}\]

y(*index: int, **kws: Any) → None#

Apply Y gate on the circuit. See tensorcircuit.gates.y_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 0.+0.j & 0.-1.j\\ 0.+1.j & 0.+0.j \end{bmatrix}\end{split}\]

z(*index: int, **kws: Any) → None#

Apply Z gate on the circuit. See tensorcircuit.gates.z_gate().

Parameters

index (int.) –

Qubit number that the gate applies on. The matrix for the gate is

\[\begin{split}\begin{bmatrix} 1.+0.j & 0.+0.j\\ 0.+0.j & -1.+0.j \end{bmatrix}\end{split}\]

tensorcircuit.mpscircuit.split_tensor(tensor: Any, center_left: bool = True, split: Optional[Dict[str, Any]] = None) → Tuple[Any, Any][source]#

Split the tensor by SVD or QR depends on whether a truncation is required.

Parameters

tensor (Tensor) – The input tensor to split.
center_left (bool, optional) – Determine the orthogonal center is on the left tensor or the right tensor.

Returns

Two tensors after splitting

Return type

Tuple[Tensor, Tensor]