tensorcircuit.basecircuit#

Quantum circuit: common methods for all circuit classes as MixIn

class tensorcircuit.basecircuit.BaseCircuit[source]#

Bases: tensorcircuit.abstractcircuit.AbstractCircuit

static all_zero_nodes(n: int, d: int = 2, prefix: str = 'qb-') → List[tensornetwork.network_components.Node][source]#
amplitude(l: Union[str, Any]) → Any[source]#

Returns the amplitude of the circuit given the bitstring l. For state simulator, it computes \(\langle l\vert \psi\rangle\), for density matrix simulator, it computes \(Tr(\rho \vert l\rangle \langle 1\vert)\) Note how these two are different up to a square operation.

Example

>>> c = tc.Circuit(2)
>>> c.X(0)
>>> c.amplitude("10")
array(1.+0.j, dtype=complex64)
>>> c.CNOT(0, 1)
>>> c.amplitude("11")
array(1.+0.j, dtype=complex64)
Parameters

l (Union[str, Tensor]) – The bitstring of 0 and 1s.

Returns

The amplitude of the circuit.

Return type

tn.Node.tensor

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#

An implementation of this method should also append gate directionary to self._qir

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]#

An implementation of this method should also append gate directionary to self._qir

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

add a barrier instruction flag, no effect on numerical simulation

Parameters

index (List[int]) – the corresponding qubits

circuit_param: Dict[str, Any]#
static coloring_copied_nodes(nodes: Sequence[tensornetwork.network_components.Node], nodes0: Sequence[tensornetwork.network_components.Node], is_dagger: bool = True, flag: str = 'inputs') → None[source]#
static coloring_nodes(nodes: Sequence[tensornetwork.network_components.Node], is_dagger: bool = False, flag: str = 'inputs') → None[source]#
cond_measure(index: int, status: Optional[float] = None) → 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, status: Optional[float] = None) → Any[source]#

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

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

copy() → tensorcircuit.abstractcircuit.AbstractCircuit#
static copy_nodes(nodes: Sequence[tensornetwork.network_components.Node], dangling: Optional[Sequence[tensornetwork.network_components.Edge]] = None, conj: Optional[bool] = False) → Tuple[List[tensornetwork.network_components.Node], List[tensornetwork.network_components.Edge]][source]#

copy all nodes and dangling edges correspondingly

Returns

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 ├─────
     └───┘
expectation(*ops: Tuple[tensornetwork.network_components.Node, List[int]], reuse: bool = True, noise_conf: Optional[Any] = None, nmc: int = 1000, status: Optional[Any] = None, **kws: Any) → Any#
expectation_before(*ops: Tuple[tensornetwork.network_components.Node, List[int]], reuse: bool = True, **kws: Any) → List[tensornetwork.network_components.Node][source]#

Get the tensor network in the form of a list of nodes for the expectation calculation before the real contraction

Parameters

reuse (bool, optional) – _description_, defaults to True

Raises

ValueError – _description_

Returns

_description_

Return type

List[tn.Node]

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

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#
static front_from_nodes(nodes: List[tensornetwork.network_components.Node]) → List[tensornetwork.network_components.Edge][source]#
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]

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 while maintaining the circuit uncomputed

Returns

QuVector representation of the output state from the circuit

Return type

QuVector

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

Take measurement to the given quantum lines. This method is jittable is and about 100 times faster than unjit version!

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

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

Take measurement to the given quantum lines. This method is jittable is and about 100 times faster than unjit version!

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]

mpogates = ['multicontrol', 'mpo']#
perfect_sampling(status: Optional[Any] = None) → Tuple[str, float][source]#

Sampling bistrings from the circuit output based on quantum amplitudes. Reference: arXiv:1201.3974.

Parameters

status (Optional[Tensor]) – external randomness, with shape [nqubits], defaults to None

Returns

Sampled bit string and the corresponding theoretical probability.

Return type

Tuple[str, float]

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

probability() → Any[source]#

get the 2^n length probability vector over computational basis

Returns

probability vector

Return type

Tensor

quvector() → tensorcircuit.quantum.QuVector#

Get the representation of the output state in the form of QuVector while maintaining the circuit uncomputed

Returns

QuVector representation of the output state from the circuit

Return type

QuVector

readouterror_bs(readout_error: Optional[Sequence[Any]] = None, p: Optional[Any] = None) → Any[source]#

Apply readout error to original probabilities of bit string and return the noisy probabilities.

Example

>>> readout_error = []
>>> readout_error.append([0.9,0.75])  # readout error for qubit 0, [p0|0,p1|1]
>>> readout_error.append([0.4,0.7])   # readout error for qubit 1, [p0|0,p1|1]
Parameters

  • readout_error (Optional[Sequence[Any]] Tensor, List, Tuple) – list of readout error for each qubits.

  • p (Optional[Any]) – probabilities of bit string

Return type

Tensor

replace_inputs(inputs: Any) → None[source]#

Replace the input state with the circuit structure unchanged.

Parameters

inputs (Tensor) – Input wavefunction.

reset_instruction(*index: int) → None#

add a reset instruction flag, no effect on numerical simulation

Parameters

index (int) – the corresponding qubits

sample(batch: Optional[int] = None, allow_state: bool = False, readout_error: Optional[Sequence[Any]] = None, format: Optional[str] = None, random_generator: Optional[Any] = None, status: Optional[Any] = None) → Any[source]#

batched sampling from state or circuit tensor network directly

Parameters

  • batch (Optional[int], optional) – number of samples, defaults to None

  • allow_state (bool, optional) – if true, we sample from the final state if memory allows, True is preferred, defaults to False

  • readout_error (Optional[Sequence[Any]] Tensor, List, Tuple) – readout_error, defaults to None

  • format (Optional[str]) – sample format, defaults to None as backward compatibility check the doc in tensorcircuit.quantum.measurement_results()

  • format – alias for the argument format

  • random_generator (Optional[Any], optional) – random generator, defaults to None

  • status (Optional[Tensor]) – external randomness given by tensor uniformly from [0, 1], if set, can overwrite random_generator

Returns

List (if batch) of tuple (binary configuration tensor and corresponding probability) if the format is None, and consistent with format when given

Return type

Any

sample_expectation_ps(x: Optional[Sequence[int]] = None, y: Optional[Sequence[int]] = None, z: Optional[Sequence[int]] = None, shots: Optional[int] = None, random_generator: Optional[Any] = None, status: Optional[Any] = None, readout_error: Optional[Sequence[Any]] = None, noise_conf: Optional[Any] = None, nmc: int = 1000, statusc: Optional[Any] = None, **kws: Any) → Any[source]#

Compute the expectation with given Pauli string with measurement shots numbers

Example

>>> c = tc.Circuit(2)
>>> c.H(0)
>>> c.rx(1, theta=np.pi/2)
>>> c.sample_expectation_ps(x=[0], y=[1])
-0.99999976
>>> readout_error = []
>>> readout_error.append([0.9,0.75])
>>> readout_error.append([0.4,0.7])
>>> c.sample_expectation_ps(x=[0], y=[1],readout_error = readout_error)

>>> 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)
>>> readout_error = [[0.9, 0.75],[0.4, 0.7]]
>>> noise_conf = NoiseConf()
>>> noise_conf.add_noise("rx", error1)
>>> noise_conf.add_noise("cnot", [error2], [[0, 1]])
>>> noise_conf.add_noise("readout", readout_error)
>>> c.sample_expectation_ps(x=[0], noise_conf=noise_conf, nmc=10000)
0.44766843
Parameters

  • x (Optional[Sequence[int]], optional) – index for Pauli X, defaults to None

  • y (Optional[Sequence[int]], optional) – index for Pauli Y, defaults to None

  • z (Optional[Sequence[int]], optional) – index for Pauli Z, defaults to None

  • shots (Optional[int], optional) – number of measurement shots, defaults to None, indicating analytical result

  • random_generator (Optional[Any]) – random_generator, defaults to None

  • status (Optional[Tensor]) – external randomness given by tensor uniformly from [0, 1], if set, can overwrite random_generator

  • readout_error (Optional[Sequence[Any]] Tensor, List, Tuple) – readout_error, defaults to None. Overrided if noise_conf is provided.

  • 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

  • statusc (Optional[Tensor], optional) – external randomness given by tensor uniformly from [0, 1], defaults to None, used for noisfy circuit sampling

Returns

[description]

Return type

Tensor

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

sexpps(x: Optional[Sequence[int]] = None, y: Optional[Sequence[int]] = None, z: Optional[Sequence[int]] = None, shots: Optional[int] = None, random_generator: Optional[Any] = None, status: Optional[Any] = None, readout_error: Optional[Sequence[Any]] = None, noise_conf: Optional[Any] = None, nmc: int = 1000, statusc: Optional[Any] = None, **kws: Any) → Any#

Compute the expectation with given Pauli string with measurement shots numbers

Example

>>> c = tc.Circuit(2)
>>> c.H(0)
>>> c.rx(1, theta=np.pi/2)
>>> c.sample_expectation_ps(x=[0], y=[1])
-0.99999976
>>> readout_error = []
>>> readout_error.append([0.9,0.75])
>>> readout_error.append([0.4,0.7])
>>> c.sample_expectation_ps(x=[0], y=[1],readout_error = readout_error)

>>> 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)
>>> readout_error = [[0.9, 0.75],[0.4, 0.7]]
>>> noise_conf = NoiseConf()
>>> noise_conf.add_noise("rx", error1)
>>> noise_conf.add_noise("cnot", [error2], [[0, 1]])
>>> noise_conf.add_noise("readout", readout_error)
>>> c.sample_expectation_ps(x=[0], noise_conf=noise_conf, nmc=10000)
0.44766843
Parameters

  • x (Optional[Sequence[int]], optional) – index for Pauli X, defaults to None

  • y (Optional[Sequence[int]], optional) – index for Pauli Y, defaults to None

  • z (Optional[Sequence[int]], optional) – index for Pauli Z, defaults to None

  • shots (Optional[int], optional) – number of measurement shots, defaults to None, indicating analytical result

  • random_generator (Optional[Any]) – random_generator, defaults to None

  • status (Optional[Tensor]) – external randomness given by tensor uniformly from [0, 1], if set, can overwrite random_generator

  • readout_error (Optional[Sequence[Any]] Tensor, List, Tuple) – readout_error, defaults to None. Overrided if noise_conf is provided.

  • 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

  • statusc (Optional[Tensor], optional) – external randomness given by tensor uniformly from [0, 1], defaults to None, used for noisfy circuit sampling

Returns

[description]

Return type

Tensor

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

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_graphviz(graph: Optional[graphviz.graphs.Graph] = None, include_all_names: bool = False, engine: str = 'neato') → graphviz.graphs.Graph[source]#

Not an ideal visualization for quantum circuit, but reserve here as a general approach to show the tensornetwork [Deprecated, use Circuit.vis_tex or Circuit.draw instead]

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]][source]#

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.

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