tensorcircuit.basecircuit#

Quantum circuit: common methods for all circuit classes as MixIn

class tensorcircuit.basecircuit.BaseCircuit[源代码]#

基类：tensorcircuit.abstractcircuit.AbstractCircuit

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

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)
参数

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

返回

The amplitude of the circuit.

返回类型

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 ├
    └───┘└───┘
参数

  • 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.

返回

The composed circuit

返回类型

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}]
参数

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[源代码]#

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

参数

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[源代码]#
static coloring_nodes(nodes: Sequence[tensornetwork.network_components.Node], is_dagger: bool = False, flag: str = 'inputs') → None[源代码]#
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)

注解

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

参数

index (int) -- the qubit for the z-basis measurement

返回

0 or 1 for z measurement on up and down freedom

返回类型

Tensor

cond_measurement(index: int, status: Optional[float] = None) → Any[源代码]#

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)

注解

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

参数

index (int) -- the qubit for the z-basis measurement

返回

0 or 1 for z measurement on up and down freedom

返回类型

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]

参数

  • 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]][源代码]#

copy all nodes and dangling edges correspondingly

返回

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][源代码]#

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

参数

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

引发

ValueError -- _description_

返回

_description_

返回类型

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)
参数

  • 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

返回

Expectation value

返回类型

Tensor

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

load json str as a Circuit

参数

  • jsonstr (str) -- _description_

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

返回

_description_

返回类型

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

参数

  • file (str) -- filename

  • circuit_params (Optional[Dict[str, Any]], optional) -- _description_, defaults to None

返回

_description_

返回类型

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)
参数

  • qir (List[Dict[str, Any]]) -- The quantum intermediate representation of a circuit.

  • circuit_params (Optional[Dict[str, Any]]) -- Extra circuit parameters.

返回

The circuit have same gates in the qir.

返回类型

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)
参数

  • 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

返回

The same circuit but as tensorcircuit object

返回类型

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][源代码]#
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
参数

gate_list (Optional[Sequence[str]], optional) -- gate name or gate name list to be counted, defaults to None (counting all gates)

返回

the total number of all gates or gates in the gate_list

返回类型

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
参数

cond_func (Callable[[Dict[str, Any]], bool]) -- the condition for counting the gate

返回

the total number of all gates which satisfy the condition

返回类型

int

gate_summary() → Dict[str, int]#

return the summary dictionary on gate type - gate count pair

返回

the gate count dict by gate type

返回类型

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

返回

positional_logical_mapping

返回类型

Dict[int, int]

get_quvector() → tensorcircuit.quantum.QuVector[源代码]#

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

返回

QuVector representation of the output state from the circuit

返回类型

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

参数

  • 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

返回

_description_

返回类型

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()
参数

circuit_params (Optional[Dict[str, Any]], optional) -- keywords dict for initialization the new circuit, defaults to None

返回

the inversed circuit

返回类型

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!

参数

  • 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

返回

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

返回类型

Tuple[Tensor, Tensor]

measure_instruction(*index: int) → None#

add a measurement instruction flag, no effect on numerical simulation

参数

index (int) -- the corresponding qubits

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

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

参数

  • 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

返回

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

返回类型

Tuple[Tensor, Tensor]

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

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

参数

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

返回

Sampled bit string and the corresponding theoretical probability.

返回类型

Tuple[str, float]

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

prepend circuit c before

参数

c (BaseCircuit) -- The other circuit to be prepended

返回

The composed circuit

返回类型

BaseCircuit

probability() → Any[源代码]#

get the 2^n length probability vector over computational basis

返回

probability vector

返回类型

Tensor

quvector() → tensorcircuit.quantum.QuVector#

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

返回

QuVector representation of the output state from the circuit

返回类型

QuVector

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

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]
参数

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

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

返回类型

Tensor

replace_inputs(inputs: Any) → None[源代码]#

Replace the input state with the circuit structure unchanged.

参数

inputs (Tensor) -- Input wavefunction.

reset_instruction(*index: int) → None#

add a reset instruction flag, no effect on numerical simulation

参数

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[源代码]#

batched sampling from state or circuit tensor network directly

参数

  • 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

返回

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

返回类型

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[源代码]#

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
参数

  • 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

返回

[description]

返回类型

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]

参数

  • 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
参数

  • 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

返回

[description]

返回类型

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

参数

name (str) -- non-standard gate name

返回

the standard gate name

返回类型

str

tex(**kws: Any) → str#

Generate latex string based on quantikz latex package

返回

Latex string that can be directly compiled via, e.g. latexit

返回类型

str

to_cirq(enable_instruction: bool = False) → Any#

Translate tc.Circuit to a cirq circuit object.

参数

enable_instruction (bool, defaults to False) -- whether also export measurement and reset instructions

返回

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[源代码]#

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

参数

  • 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

返回

None if dumps to file otherwise the json str

返回类型

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

返回

circuit representation in openqasm format

返回类型

str

to_qir() → List[Dict[str, Any]][源代码]#

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}]
返回

The quantum intermediate representation of the circuit.

返回类型

List[Dict[str, Any]]

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

Translate tc.Circuit to a qiskit QuantumCircuit object.

参数

  • enable_instruction (bool, defaults to False) -- whether also export measurement and reset instructions

  • enable_inputs (bool, defaults to False) -- whether also export the inputs

返回

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

返回

Latex string that can be directly compiled via, e.g. latexit

返回类型

str