deepquantum.photonic package#

Submodules#

deepquantum.photonic.ansatz module#

Ansatze: various photonic quantum circuits

class deepquantum.photonic.ansatz.Clements(nmode: int, init_state: Any, cutoff: int | None = None, basis: bool = True, phi_first: bool = True, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：QumodeCircuit

Clements circuit.

dict2data(angle_dict: Dict, dtype=torch.float32) → Tensor#: Convert the dictionary of angles to the input data for the circuit.

class deepquantum.photonic.ansatz.GaussianBosonSampling(nmode: int, squeezing: Any, unitary: Any, cutoff: int | None = None, backend: str = 'gaussian', basis: bool = True, detector: str = 'pnrd', noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：QumodeCircuit

Gaussian Boson Sampling circuit.

class deepquantum.photonic.ansatz.GBS_Graph(adj_mat: Any, cutoff: int | None = None, mean_photon_num: int | None = None, detector: str = 'pnrd', noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：GaussianBosonSampling

Simulate Gaussian Boson Sampling for graph problems.

static norm_factor_c(n_num, lambd, trials=20)#: Get the normalization factor c of squeezing parameters for given total mean photon numbers.

static postselect(samples: Dict, nodes_list: List) → List#: Postselect the results with the fixed node subgraph.

static graph_density(graph: Graph, samples: Dict) → Dict#: Get all subgraph densities.

deepquantum.photonic.channel module#

Photonic quantum channels

class deepquantum.photonic.channel.PhotonLoss(inputs: Any | None = None, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, requires_grad: bool = False)#

基类：Channel

Photon loss channel on single mode.

This channel couples the target mode $hat{a}$ to the vacuum mode $hat{b}$ using following transformation:

\[\hat{a}_{\text{out}} = \sqrt{T}\hat{a}_{\text{in}} + \sqrt{1-T}\hat{b}_{\text{vac}}\]

参数:

inputs (Any, optional) – The parameter of the channel. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
requires_grad (bool, optional) – Whether the parameter is nn.Parameter or buffer. Default: False (which means buffer)

property theta#

property t#: Transmittance.

update_matrix_state() → Tensor#: Update the local Kraus matrices acting on Fock state density matrices.

get_matrix_state(theta: Any) → Tensor#

Get the local Kraus matrices acting on Fock state density matrices.

See https://arxiv.org/pdf/1012.4266 Eq.(2.4)

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

update_transform_xy() → Tuple[Tensor, Tensor]#

Update the local transformation matrices X and Y acting on Gaussian states.

See https://arxiv.org/pdf/quant-ph/0503237 Eq.(4.19), Eq.(4.20)

deepquantum.photonic.circuit module#

Photonic quantum circuit

class deepquantum.photonic.circuit.QumodeCircuit(nmode: int, init_state: Any, cutoff: int | None = None, backend: str = 'fock', basis: bool = True, den_mat: bool = False, detector: str = 'pnrd', name: str | None = None, mps: bool = False, chi: int | None = None, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Operation

Photonic quantum circuit.

参数:

nmode (int) – The number of modes in the circuit.
init_state (Any) – The initial state of the circuit. It can be a vacuum state with 'vac'. For Fock backend, it can be a Fock basis state, e.g., [1,0,0], or a Fock state tensor, e.g., [(1/2**0.5, [1,0]), (1/2**0.5, [0,1])]. Alternatively, it can be a tensor representation. For Gaussian backend, it can be arbitrary Gaussian states with [cov, mean]. Use xxpp convention and $\hbar=2$ by default.
cutoff (int or None, optional) – The Fock space truncation. Default: None
backend (str, optional) – Use 'fock' for Fock backend or 'gaussian' for Gaussian backend. Default: 'fock'
basis (bool, optional) – Whether to use the representation of Fock basis state for the initial state. Default: True
den_mat (bool, optional) – Whether to use density matrix representation. Only valid for Fock state tensor. Default: False
detector (str, optional) – For Gaussian backend, use 'pnrd' for the photon-number-resolving detector or 'threshold' for the threshold detector. Default: 'pnrd'
name (str or None, optional) – The name of the circuit. Default: None
mps (bool, optional) – Whether to use matrix product state representation. Default: False
chi (int or None, optional) – The bond dimension for matrix product state representation. Default: None
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

set_init_state(init_state: Any) → None#: Set the initial state of the circuit.

to(arg: Any) → QumodeCircuit#: Set dtype or device of the QumodeCircuit.

Perform a forward pass of the photonic quantum circuit and return the final-state-related result.

参数:

data (torch.Tensor or None, optional) – The input data for the encoders. Default: None
state (Any, optional) – The initial state for the photonic quantum circuit. Default: None
is_prob (bool or None, optional) – For Fock backend, whether to return probabilities or amplitudes. For Gaussian backend, whether to return probabilities or the final Gaussian state. For Fock backend with basis=True, set None to return the unitary matrix. Default: None
detector (str or None, optional) – For Gaussian backend, use 'pnrd' for the photon-number-resolving detector or 'threshold' for the threshold detector. Default: None
stepwise (bool, optional) – Whether to use the forward function of each operator for Gaussian backend. Default: False

返回:

The result of the photonic quantum circuit after applying the operators.

返回类型:

Union[torch.Tensor, Dict, List[torch.Tensor]]

global_circuit(nstep: int, use_deepcopy: bool = False) → QumodeCircuit#: Get the global circuit given the number of time steps.

备注

The initial state of the global circuit is always the vacuum state.

encode(data: Tensor | None) → None#

Encode the input data into the photonic quantum circuit parameters.

This method iterates over the encoders of the circuit and initializes their parameters with the input data.

参数:: data (torch.Tensor or None) – The input data for the encoders, must be a 1D tensor.

get_unitary() → Tensor#: Get the unitary matrix of the photonic quantum circuit.

get_symplectic() → Tensor#: Get the symplectic matrix of the photonic quantum circuit.

get_displacement(init_mean: Any) → Tensor#: Get the final mean value of the Gaussian state in xxpp order.

get_amplitude(final_state: Any, init_state: Any | None = None, unitary: Tensor | None = None) → Tensor#

Get the transfer amplitude between the final state and the initial state.

参数:

final_state (Any) – The final Fock basis state.
init_state (Any, optional) – The initial Fock basis state. Default: None
unitary (torch.Tensor or None, optional) – The unitary matrix. Default: None

get_prob(final_state: Any, refer_state: Any | None = None, unitary: Tensor | None = None) → Tensor#

Get the probability of the final state related to the reference state.

参数:

final_state (Any) – The final Fock basis state.
refer_state (Any, optional) – The initial Fock basis state or the final Gaussian state. Default: None
unitary (torch.Tensor or None, optional) – The unitary matrix. Default: None

Measure the final state.

参数:

shots (int, optional) – The number of times to sample from the quantum state. Default: 1024
with_prob (bool, optional) – A flag that indicates whether to return the probabilities along with the number of occurrences. Default: False
wires (int, List[int] or None, optional) – The wires to measure. It can be an integer or a list of integers specifying the indices of the wires. NOT valid for MCMC. Default: None (which means all wires are measured)
detector (str or None, optional) – For Gaussian backend, use 'pnrd' for the photon-number-resolving detector or 'threshold' for the threshold detector. Default: None
mcmc (bool, optional) – Whether to use MCMC sampling method. Default: False

See https://arxiv.org/pdf/2108.01622 for MCMC.

photon_number_mean_var(wires: int | List[int] | None = None) → Tuple[Tensor, Tensor] | None#

Get the expectation value and variance of the photon number operator.

参数:: wires (int, List[int] or None, optional) – The wires to measure. It can be an integer or a list of integers specifying the indices of the wires. Default: None (which means all wires are measured)

measure_homodyne(shots: int = 1024, wires: int | List[int] | None = None) → Tensor | None#

Get the homodyne measurement results.

If self.measurements is specified via self.homodyne, return the results of the conditional homodyne measurement. Otherwise, return the results of the ideal homodyne measurement. The Gaussian states after measurements are stored in self.state_measured.

参数:

shots (int, optional) – The number of times to sample from the quantum state. Default: 1024
wires (int, List[int] or None, optional) – The wires to measure for the ideal homodyne. It can be an integer or a list of integers specifying the indices of the wires. Default: None (which means all wires are measured)

property max_depth: int#: Get the max number of gates on the wires.

draw(filename: str | None = None, unroll: bool = False)#

Visualize the photonic quantum circuit.

参数:

filename (str or None, optional) – The path for saving the figure.
unroll (bool, optional) – Whether to draw the unrolled circuit.

add(op: Operation, encode: bool = False, wires: int | List[int] | None = None) → None#

A method that adds an operation to the photonic quantum circuit.

The operation can be a gate or another photonic quantum circuit. The method also updates the attributes of the photonic quantum circuit. If wires is specified, the parameters of gates are shared.

参数:

op (Operation) – The operation to add. It is an instance of Operation class or its subclasses, such as Gate, or QumodeCircuit.
encode (bool, optional) – Whether the gate is to encode data. Default: False
wires (int, List[int] or None, optional) – The wires to apply the gate on. It can be an integer or a list of integers specifying the indices of the wires. Default: None (which means the gate has its own wires)

抛出:

AssertionError – If the input arguments are invalid or incompatible with the quantum circuit.

ps(wires: int, inputs: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a phase shifter.

bs(wires: List[int], inputs: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a beam splitter.

mzi(wires: List[int], inputs: Any | None = None, phi_first: bool = True, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a Mach-Zehnder interferometer.

bs_theta(wires: List[int], inputs: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a beam splitter with fixed $\phi$ at $\pi/2$.

bs_phi(wires: List[int], inputs: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a beam splitter with fixed $\theta$ at $\pi/4$.

bs_rx(wires: List[int], inputs: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add an Rx-type beam splitter.

bs_ry(wires: List[int], inputs: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add an Ry-type beam splitter.

bs_h(wires: List[int], inputs: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add an H-type beam splitter.

dc(wires: List[int], mu: float | None = None, sigma: float | None = None) → None#: Add a directional coupler.

h(wires: List[int], mu: float | None = None, sigma: float | None = None) → None#: Add a photonic Hadamard gate.

any(unitary: Any, wires: int | List[int] | None = None, minmax: List[int] | None = None, name: str = 'uany') → None#: Add an arbitrary unitary gate.

Add the Clements architecture of the unitary matrix.

This is equivalent to any, using ‘cssr’-type Clements decomposition. When basis is False, this implementation is much faster.

s(wires: int, r: Any | None = None, theta: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a squeezing gate.

s2(wires: List[int], r: Any | None = None, theta: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a two-mode squeezing gate.

d(wires: int, r: Any | None = None, theta: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a displacement gate.

x(wires: int, inputs: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a position displacement gate.

z(wires: int, inputs: Any | None = None, encode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a momentum displacement gate.

r(wires: int, inputs: Any | None = None, encode: bool = False, inv_mode: bool = False, mu: float | None = None, sigma: float | None = None) → None#: Add a rotation gate.

f(wires: int, mu: float | None = None, sigma: float | None = None) → None#: Add a Fourier gate.

delay(wires: int, ntau: int = 1, inputs: Any | None = None, convention: str = 'bs', encode: bool = False, loop_gates: List | None = None, mu: float | None = None, sigma: float | None = None) → None#: Add a delay loop.

homodyne(wires: int, phi: Any | None = None, eps: float = 0.0002, mu: float | None = None, sigma: float | None = None) → None#: Add a homodyne measurement.

homodyne_x(wires: int, eps: float = 0.0002, mu: float | None = None, sigma: float | None = None) → None#: Add a homodyne measurement for quadrature x.

homodyne_p(wires: int, eps: float = 0.0002, mu: float | None = None, sigma: float | None = None) → None#: Add a homodyne measurement for quadrature p.

loss(wires: int, inputs: Any | None = None, encode: bool = False) → None#

Add a photon loss channel.

The inputs corresponds to theta of the loss channel.

loss_t(wires: int, inputs: Any | None = None, encode: bool = False) → None#

Add a photon loss channel.

The inputs corresponds to the transmittance of the loss channel.

loss_db(wires: int, inputs: Any | None = None, encode: bool = False) → None#

Add a photon loss channel.

The inputs corresponds to the probability of loss with the unit of dB and is positive.

deepquantum.photonic.decompose module#

Decompose the unitary matrix

class deepquantum.photonic.decompose.UnitaryDecomposer(unitary: ndarray | Tensor, method: str = 'cssr')#

基类：object

This class is to decompose a unitary matrix into the Clements/Reck architecture.

参数:

unitary (np.ndarray or torch.Tensor) – The unitary matrix to be decomposed.
method (str, optional) – The decomposition method, only 16 values ('rssr', 'rsdr', 'rdsr', 'rddr', 'rssl', 'rsdl', 'rdsl', 'rddl', 'cssr', 'csdr', 'cdsr', 'cddr', 'cssl', 'csdl', 'cdsl', 'cddl') are valid. The first char denotes the Clements or Reck architecture. The second char denotes single or double arms of outer phase shifters. The third char denotes single or double arms of inner phase shifters. The last char denotes the position of a column of phase shifters, i.e., 'l' for left and 'r' for right. Default: 'cssr'

decomp() → Tuple[Dict, Dict, Dict]#: Decompose the unitary matrix.The third dictionary is the representation of the positions and the angles of all phase shifters.

sort_mzi(mzi_info)#: Sort mzi parameters in the same array for plotting.

ps_pos(dic_mzi, phase_angle)#: Label the position of each phaseshifter for 'cssr' case.

deepquantum.photonic.draw module#

Draw quantum circuit.

class deepquantum.photonic.draw.DrawCircuit(circuit_name: str, circuit_nmode: int, circuit_operators: Sequential, measurements: ModuleList, nstep: int | None = None)#

基类：object

Draw the photonic quantum circuit.

参数:

circuit_name (str) – The name of the circuit.
circuit_nmode (int) – The number of modes in the circuit.
circuit_operators (nn.Sequential) – The operators of the circuit.
measurements (nn.ModuleList) – The measurements of the circuit.
nstep (int or None, optional) – The number of time steps for the TDM circuit. Default: None

draw()#: Draw circuit

draw_nstep()#: Draw unroll circuit with given step.

draw_normal(depth=None, ops=None, measurements=None)#: Draw normal circuit.

save(filename)#: Save the circuit as svg.

draw_mode_num()#

draw_bs(name, order, wires, theta, phi=None)#: Draw beamsplitter.

draw_ps(order, wires, theta=0, name=None)#: Draw phaseshift (rotation) gate.

draw_homodyne(order, wire, phi, name=None)#: Draw homodyne measurement.

draw_sq(order, wires, r=None, theta=None, name=None)#: Draw squeezing gate, displacement gate.

draw_delay(order, wires, inputs=None)#: Draw delay loop.

draw_loss(order, wires, name, t)#: Draw loss gate.

draw_any(order, wires, name, para_dict=None)#: Draw arbitrary unitary gate.

draw_lines(order, wires)#: Act nothing.

barrier(order)#

class deepquantum.photonic.draw.DrawClements(nmode: int, mzi_info: Dict, cl: str = 'dodgerblue', fs: int = 30, method: str = 'cssr')#

基类：object

Draw the n-mode Clements architecture.

参数:

nmode (int) – The number of modes of the Clements architecture.
mzi_info (Dict) – The dictionary for mzi parameters, resulting from the decompose function.
cl (str, optional) – The color for plotting. Default: 'dodgerblue'
fs (int, optional) – The fontsize. Default: 30
method (str, optional) – The way for Clements decomposition, 'cssr' or 'cssl'. Default: 'cssr'

plotting_clements()#: Plot clements structure with cssr or cssl type.

plotting_clements_1()#: Plot cssr with left to right order.

plotting_clements_2()#: Plot cssl with right to left order.

sort_mzi()#: Sort mzi parameters in the same array for plotting.

ps_pos()#: Label the position of each phaseshifter for cssr case.

static connect1(coordinate, ax, cl, wid=0.1, height=0.08, a=-0.05, b=-0.05, c=0.7, d=-0.05)#: Connect odd column.

static connect2(coordinate, cl)#: Connect even column.

static plot_paras(sort_mzi_dic, nmode, fs=20)#: Plot mzi parameters for cssl case.

static plot_paras_1(sort_mzi_dic, fs=20)#: Plot mzi parameters for cssr case.

deepquantum.photonic.gate module#

Photonic quantum gates

class deepquantum.photonic.gate.SingleGate(name: str | None = None, inputs: Any | None = None, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Gate

Single-mode photonic quantum gate.

参数:

name (str or None, optional) – The name of the gate. Default: None
inputs (Any, optional) – The parameters of the gate. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameter is nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

class deepquantum.photonic.gate.DoubleGate(name: str | None = None, inputs: Any | None = None, nmode: int = 2, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Gate

Two-mode photonic quantum gate.

参数:

name (str or None, optional) – The name of the gate. Default: None
inputs (Any, optional) – The parameters of the gate. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 2
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameter is nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

class deepquantum.photonic.gate.PhaseShift(inputs: Any | None = None, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1, inv_mode: bool = False)#

基类：SingleGate

Phase Shifter.

Matrix Representation:

\[U_{\text{PS}}(\theta) = e^{i\theta}\]

Symplectic Transformation:

\[\begin{split}PS^{\dagger}(\theta) \begin{pmatrix} \hat{x} \\ \hat{p} \\ \end{pmatrix} PS(\theta) = \begin{pmatrix} \cos\theta & -\sin\theta \\ \sin\theta & \cos\theta \\ \end{pmatrix} \begin{pmatrix} \hat{x} \\ \hat{p} \\ \end{pmatrix}\end{split}\]

参数:

inputs (Any, optional) – The parameter of the gate. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameter is nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1
inv_mode (bool, optional) – Whether the rotation in the phase space is clockwise. Default: False

inputs_to_tensor(inputs: Any | None = None) → Tensor#: Convert inputs to torch.Tensor.

get_matrix(theta: Any) → Tensor#: Get the local unitary matrix acting on creation operators.

update_matrix() → Tensor#: Update the local unitary matrix acting on creation operators.

get_matrix_state(matrix: Tensor) → Tensor#: Get the local transformation matrix acting on Fock state tensors.

get_transform_xp(theta: Any) → Tuple[Tensor, Tensor]#: Get the local affine symplectic transformation acting on quadrature operators in xxpp order.

update_transform_xp() → Tuple[Tensor, Tensor]#: Update the local affine symplectic transformation acting on quadrature operators in xxpp order.

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

class deepquantum.photonic.gate.BeamSplitter(inputs: Any | None = None, nmode: int = 2, wires: List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：DoubleGate

Beam Splitter.

See https://arxiv.org/abs/2004.11002 Eq.(42b)

Matrix Representation:

\[\begin{split}U_{\text{BS}}(\theta, \phi) = \begin{pmatrix} \cos\left(\theta\right) & -e^{-i\phi} \sin\left(\theta\right) \\ e^{i\phi} \sin\left(\theta\right) & \cos\left(\theta\right) \\ \end{pmatrix}\end{split}\]

Symplectic Transformation:

\[\begin{split}BS^\dagger(\theta, \phi) \begin{pmatrix} \hat{x}_1 \\ \hat{x}_2 \\ \hat{p}_1 \\ \hat{p}_2 \\ \end{pmatrix} BS(\theta, \phi) = \begin{pmatrix} \mathrm{Re}(U_{\text{BS}}) & -\mathrm{Im}(U_{\text{BS}}) \\ \mathrm{Im}(U_{\text{BS}}) & \mathrm{Re}(U_{\text{BS}}) \\ \end{pmatrix} \begin{pmatrix} \hat{x}_1 \\ \hat{x}_2 \\ \hat{p}_1 \\ \hat{p}_2 \\ \end{pmatrix}\end{split}\]

参数:

inputs (Any, optional) – The parameters of the gate ($\theta$ and $\phi$). Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 2
wires (List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameters are nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

inputs_to_tensor(inputs: Any | None = None) → Tuple[Tensor, Tensor]#: Convert inputs to torch.Tensor.

get_matrix(theta: Any, phi: Any) → Tensor#: Get the local unitary matrix acting on creation operators.

update_matrix() → Tensor#: Update the local unitary matrix acting on creation operators.

get_matrix_state(matrix: Tensor) → Tensor#

Get the local transformation matrix acting on Fock state tensors.

See https://arxiv.org/pdf/2004.11002.pdf Eq.(74) and Eq.(75)

get_transform_xp(theta: Any, phi: Any) → Tuple[Tensor, Tensor]#: Get the local affine symplectic transformation acting on quadrature operators in xxpp order.

update_transform_xp() → Tuple[Tensor, Tensor]#: Update the local affine symplectic transformation acting on quadrature operators in xxpp order.

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

class deepquantum.photonic.gate.MZI(inputs: Any | None = None, nmode: int = 2, wires: List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, phi_first: bool = True, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：BeamSplitter

Mach-Zehnder Interferometer.

Matrix Representation:

\[ \begin{align}\begin{aligned}\newcommand{\th}{\frac{\theta}{2}}\\\begin{split}U_{\text{MZI}}(\theta, \phi) = ie^{i\theta/2} \begin{pmatrix} e^{i\phi} \sin\left(\th\right) & \cos\left(\th\right) \\ e^{i\phi} \cos\left(\th\right) & -\sin\left(\th\right) \\ \end{pmatrix}\end{split}\end{aligned}\end{align} \]

or when phi_first is False:

\[ \begin{align}\begin{aligned}\newcommand{\th}{\frac{\theta}{2}}\\\begin{split}U_{\text{MZI}}(\theta, \phi) = ie^{i\theta/2} \begin{pmatrix} e^{i\phi} \sin\left(\th\right) & e^{i\phi} \cos\left(\th\right) \\ \cos\left(\th\right) & -\sin\left(\th\right) \\ \end{pmatrix}\end{split}\end{aligned}\end{align} \]

Symplectic Transformation:

\[\begin{split}MZI^\dagger(\theta, \phi) \begin{pmatrix} \hat{x}_1 \\ \hat{x}_2 \\ \hat{p}_1 \\ \hat{p}_2 \\ \end{pmatrix} MZI(\theta, \phi) = \begin{pmatrix} \mathrm{Re}(U_{\text{MZI}}) & -\mathrm{Im}(U_{\text{MZI}}) \\ \mathrm{Im}(U_{\text{MZI}}) & \mathrm{Re}(U_{\text{MZI}}) \\ \end{pmatrix} \begin{pmatrix} \hat{x}_1 \\ \hat{x}_2 \\ \hat{p}_1 \\ \hat{p}_2 \\ \end{pmatrix}\end{split}\]

参数:

inputs (Any, optional) – The parameters of the gate ($\theta$ and $\phi$). Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 2
wires (List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
phi_first (bool, optional) – Whether $\phi$ is the first phase shifter. Default: True
requires_grad (bool, optional) – Whether the parameters are nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

get_matrix(theta: Any, phi: Any) → Tensor#: Get the local unitary matrix acting on operators.

class deepquantum.photonic.gate.BeamSplitterTheta(inputs: Any | None = None, nmode: int = 2, wires: List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：BeamSplitter

Beam Splitter with fixed $\phi$ at $\pi/2$.

Matrix Representation:

\[\begin{split}U_{\text{BS}}(\theta) = \begin{pmatrix} \cos\left(\theta\right) & i\sin\left(\theta\right) \\ i\sin\left(\theta\right) & \cos\left(\theta\right) \\ \end{pmatrix}\end{split}\]

Symplectic Transformation:

\[\begin{split}BS^\dagger(\theta) \begin{pmatrix} \hat{x}_1 \\ \hat{x}_2 \\ \hat{p}_1 \\ \hat{p}_2 \\ \end{pmatrix} BS(\theta) = \begin{pmatrix} \mathrm{Re}(U_{\text{BS}}) & -\mathrm{Im}(U_{\text{BS}}) \\ \mathrm{Im}(U_{\text{BS}}) & \mathrm{Re}(U_{\text{BS}}) \\ \end{pmatrix} \begin{pmatrix} \hat{x}_1 \\ \hat{x}_2 \\ \hat{p}_1 \\ \hat{p}_2 \\ \end{pmatrix}\end{split}\]

参数:

inputs (Any, optional) – The parameter of the gate. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 2
wires (List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameter is nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

class deepquantum.photonic.gate.BeamSplitterPhi(inputs: Any | None = None, nmode: int = 2, wires: List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：BeamSplitter

Beam Splitter with fixed $\theta$ at $\pi/4$.

Matrix Representation:

\[\begin{split}U_{\text{BS}}(\phi) = \begin{pmatrix} \frac{\sqrt{2}}{2} & -\frac{\sqrt{2}}{2}e^{-i\phi} \\ \frac{\sqrt{2}}{2}e^{i\phi} & \frac{\sqrt{2}}{2} \\ \end{pmatrix}\end{split}\]

Symplectic Transformation:

\[\begin{split}BS^\dagger(\phi) \begin{pmatrix} \hat{x}_1 \\ \hat{x}_2 \\ \hat{p}_1 \\ \hat{p}_2 \\ \end{pmatrix} BS(\phi) = \begin{pmatrix} \mathrm{Re}(U_{\text{BS}}) & -\mathrm{Im}(U_{\text{BS}}) \\ \mathrm{Im}(U_{\text{BS}}) & \mathrm{Re}(U_{\text{BS}}) \\ \end{pmatrix} \begin{pmatrix} \hat{x}_1 \\ \hat{x}_2 \\ \hat{p}_1 \\ \hat{p}_2 \\ \end{pmatrix}\end{split}\]

参数:

inputs (Any, optional) – The parameter of the gate. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 2
wires (List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameter is nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

class deepquantum.photonic.gate.BeamSplitterSingle(inputs: Any | None = None, nmode: int = 2, wires: List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, convention: str = 'rx', requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：BeamSplitter

Beam Splitter with a single parameter.

Matrix Representation:

\[ \begin{align}\begin{aligned}\newcommand{\th}{\frac{\theta}{2}}\\\begin{split}U_{\text{BS-Rx}}(\theta) = \begin{pmatrix} \cos\left(\th\right) & i\sin\left(\th\right) \\ i\sin\left(\th\right) & \cos\left(\th\right) \\ \end{pmatrix}\end{split}\end{aligned}\end{align} \]

\[ \begin{align}\begin{aligned}\newcommand{\th}{\frac{\theta}{2}}\\\begin{split}U_{\text{BS-Ry}}(\theta) = \begin{pmatrix} \cos\left(\th\right) & -\sin\left(\th\right) \\ \sin\left(\th\right) & \cos\left(\th\right) \\ \end{pmatrix}\end{split}\end{aligned}\end{align} \]

\[ \begin{align}\begin{aligned}\newcommand{\th}{\frac{\theta}{2}}\\\begin{split}U_{\text{BS-H}}(\theta) = \begin{pmatrix} \cos\left(\th\right) & \sin\left(\th\right) \\ \sin\left(\th\right) & -\cos\left(\th\right) \\ \end{pmatrix}\end{split}\end{aligned}\end{align} \]

Symplectic Transformation:

参数:

inputs (Any, optional) – The parameter of the gate. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 2
wires (List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
convention (str, optional) – The convention of the type of the beam splitter, including 'rx', 'ry' and 'h'. Default: 'rx'
requires_grad (bool, optional) – Whether the parameter is nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

inputs_to_tensor(inputs: Any | None = None) → Tensor#: Convert inputs to torch.Tensor.

get_matrix(theta: Any) → Tensor#: Get the local unitary matrix acting on creation operators.

update_matrix() → Tensor#: Update the local unitary matrix acting on creation operators.

get_transform_xp(theta: Any) → Tuple[Tensor, Tensor]#: Get the local affine symplectic transformation acting on quadrature operators in xxpp order.

update_transform_xp() → Tuple[Tensor, Tensor]#: Update the local affine symplectic transformation acting on quadrature operators in xxpp order.

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

class deepquantum.photonic.gate.UAnyGate(unitary: Any, nmode: int = 1, wires: List[int] | None = None, minmax: List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, name: str = 'UAnyGate')#

基类：Gate

Arbitrary unitary gate.

参数:

unitary (Any) – Any given unitary matrix.
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
minmax (List[int] or None, optional) – The minimum and maximum indices of the modes that the quantum operation acts on. Only valid when wires is None. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
name (str, optional) – The name of the gate. Default: 'UAnyGate'

get_matrix_state(matrix: Tensor) → Tensor#

Get the local transformation matrix acting on Fock state tensors.

See https://arxiv.org/pdf/2004.11002.pdf Eq.(71)

update_matrix_state() → Tensor#: Update the local transformation matrix acting on Fock state tensors.

get_transform_xp(matrix: Tensor) → Tuple[Tensor, Tensor]#: Get the local affine symplectic transformation acting on quadrature operators in xxpp order.

update_transform_xp() → Tuple[Tensor, Tensor]#: Update the local affine symplectic transformation acting on quadrature operators in xxpp order.

class deepquantum.photonic.gate.Squeezing(inputs: Any | None = None, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：SingleGate

Squeezing gate.

Symplectic Transformation:

\[\begin{split}S^{\dagger}(r, \theta) \begin{pmatrix} \hat{x} \\ \hat{p} \\ \end{pmatrix} S(r, \theta) = \begin{pmatrix} \cosh r - \sinh r \cos\theta & -\sinh r \sin\theta \\ -\sinh r \sin\theta & \cosh r + \sinh r \cos\theta \\ \end{pmatrix} \begin{pmatrix} \hat{x} \\ \hat{p} \\ \end{pmatrix}\end{split}\]

参数:

inputs (Any, optional) – The parameters of the gate ($r$ and $\theta$). Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameters are nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

inputs_to_tensor(inputs: Any | None = None) → Tuple[Tensor, Tensor]#: Convert inputs to torch.Tensor.

get_matrix(r: Any, theta: Any) → Tensor#: Get the local matrix acting on annihilation and creation operators.

update_matrix() → Tensor#: Update the local matrix acting on annihilation and creation operators.

get_matrix_state(r: Any, theta: Any) → Tensor#

Get the local transformation matrix acting on Fock state tensors.

See https://arxiv.org/pdf/2004.11002.pdf Eq.(51) and Eq.(52)

update_matrix_state() → Tensor#: Update the local transformation matrix acting on Fock state tensors.

get_transform_xp(r: Any, theta: Any) → Tuple[Tensor, Tensor]#: Get the local affine symplectic transformation acting on quadrature operators in xxpp order.

update_transform_xp() → Tuple[Tensor, Tensor]#: Update the local affine symplectic transformation acting on quadrature operators in xxpp order.

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

class deepquantum.photonic.gate.Squeezing2(inputs: Any | None = None, nmode: int = 2, wires: List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：DoubleGate

Two-mode Squeezing gate.

Symplectic Transformation:

\[\begin{split}S_2^{\dagger}(r, \theta) \begin{pmatrix} \hat{x}_1 \\ \hat{x}_2 \\ \hat{p}_1 \\ \hat{p}_2 \\ \end{pmatrix} S_2(r, \theta) = \begin{pmatrix} \cosh r & \cos\theta\sinh r & 0 & \sin\theta\sinh r \\ \cos\theta\sinh r & \cosh r & \sin\theta\sinh r & 0 \\ 0 & \sin\theta\sinh r & \cosh r & -\cos\theta\sinh r \\ \sin\theta\sinh r & 0 & -\cos\theta\sinh r & \cosh r \\ \end{pmatrix} \begin{pmatrix} \hat{x}_1 \\ \hat{x}_2 \\ \hat{p}_1 \\ \hat{p}_2 \\ \end{pmatrix}\end{split}\]

参数:

inputs (Any, optional) – The parameters of the gate ($r$ and $\theta$). Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 2
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameters are nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

inputs_to_tensor(inputs: Any | None = None) → Tuple[Tensor, Tensor]#: Convert inputs to torch.Tensor.

get_matrix(r: Any, theta: Any) → Tensor#: Get the local matrix acting on annihilation and creation operators.

update_matrix() → Tensor#: Update the local matrix acting on annihilation and creation operators.

get_matrix_state(r: Any, theta: Any) → Tensor#

Get the local transformation matrix acting on Fock state tensors.

See https://arxiv.org/pdf/2004.11002.pdf Eq.(64-67)

update_matrix_state() → Tensor#: Update the local transformation matrix acting on Fock state tensors.

get_transform_xp(r: Any, theta: Any) → Tuple[Tensor, Tensor]#: Get the local affine symplectic transformation acting on quadrature operators in xxpp order.

update_transform_xp() → Tuple[Tensor, Tensor]#: Update the local affine symplectic transformation acting on quadrature operators in xxpp order.

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

class deepquantum.photonic.gate.Displacement(inputs: Any | None = None, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：SingleGate

Displacement gate.

Symplectic Transformation:

\[\begin{split}D^\dagger(r, \theta) \begin{pmatrix} \hat{x} \\ \hat{p} \\ \end{pmatrix} D(r, \theta) = \begin{pmatrix} \hat{x} \\ \hat{p} \\ \end{pmatrix} + \frac{\sqrt{\hbar}}{\kappa} \begin{pmatrix} r\cos\theta \\ r\sin\theta \\ \end{pmatrix}\end{split}\]

参数:

inputs (Any, optional) – The parameters of the gate ($r$ and $\theta$). Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameters are nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

inputs_to_tensor(inputs: Any | None = None) → Tuple[Tensor, Tensor]#: Convert inputs to torch.Tensor.

get_matrix(r: Any, theta: Any) → Tensor#: Get the local unitary matrix acting on annihilation and creation operators.

update_matrix() → Tensor#: Update the local unitary matrix acting on annihilation and creation operators.

get_matrix_state(r: Any, theta: Any) → Tensor#

Get the local transformation matrix acting on Fock state tensors.

See https://arxiv.org/pdf/2004.11002.pdf Eq.(57) and Eq.(58)

update_matrix_state() → Tensor#: Update the local transformation matrix acting on Fock state tensors.

get_transform_xp(r: Any, theta: Any) → Tuple[Tensor, Tensor]#: Get the local affine symplectic transformation acting on quadrature operators in xxpp order.

update_transform_xp() → Tuple[Tensor, Tensor]#: Update the local affine symplectic transformation acting on quadrature operators in xxpp order.

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

class deepquantum.photonic.gate.DisplacementPosition(inputs: Any | None = None, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Displacement

Position Displacement gate.

Symplectic Transformation:

\[\begin{split}X^\dagger(x) \begin{pmatrix} \hat{x} \\ \hat{p} \\ \end{pmatrix} X(x) = \begin{pmatrix} \hat{x} \\ \hat{p} \\ \end{pmatrix} + \begin{pmatrix} x \\ 0 \\ \end{pmatrix}\end{split}\]

参数:

inputs (Any, optional) – The parameter of the gate. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameter is nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

class deepquantum.photonic.gate.DisplacementMomentum(inputs: Any | None = None, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Displacement

Momentum Displacement gate.

Symplectic Transformation:

\[\begin{split}Z^\dagger(p) \begin{pmatrix} \hat{x} \\ \hat{p} \\ \end{pmatrix} Z(p) = \begin{pmatrix} \hat{x} \\ \hat{p} \\ \end{pmatrix} + \begin{pmatrix} 0 \\ p \\ \end{pmatrix}\end{split}\]

参数:

inputs (Any, optional) – The parameter of the gate. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameter is nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

class deepquantum.photonic.gate.DelayBS(inputs: Any | None = None, ntau: int = 1, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, loop_gates: List | None = None, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Delay

Delay loop with BeamSplitterTheta and PhaseShift.

参数:

inputs (Any, optional) – The parameters of the gate. Default: None
ntau (int, optional) – The number of modes in the delay loop. Default: 1
nmode (int, optional) – The number of spatial modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameters are nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

property theta#

property phi#

class deepquantum.photonic.gate.DelayMZI(inputs: Any | None = None, ntau: int = 1, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, requires_grad: bool = False, loop_gates: List | None = None, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Delay

Delay loop with MZI.

参数:

inputs (Any, optional) – The parameters of the gate. Default: None
ntau (int, optional) – The number of modes in the delay loop. Default: 1
nmode (int, optional) – The number of spatial modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
requires_grad (bool, optional) – Whether the parameters are nn.Parameter or buffer. Default: False (which means buffer)
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

property theta#

property phi#

deepquantum.photonic.hafnian_ module#

functions for hafnian

deepquantum.photonic.hafnian_.integer_partition(remaining: int, max_num: int) → List#: Generate all unique integer partitions of m using integers up to n.

deepquantum.photonic.hafnian_.count_unique_permutations(nums: List | array) → float64#: Count the number of unique permutations of a list of numbers.

deepquantum.photonic.hafnian_.get_submat_haf(a: Tensor, z: Tensor) → Tensor#

Get the sub-matrix for hafnian calculation.

See https://arxiv.org/abs/1805.12498 paragraph after Eq.(3.20)

deepquantum.photonic.hafnian_.poly_lambda(submat: Tensor, int_partition: List, power: int, loop: bool = False) → Tensor#: Get the coefficient of the polynomial.

deepquantum.photonic.hafnian_.hafnian(matrix: Tensor, loop: bool = False) → Tensor#

Calculate the hafnian for symmetric matrix, using the eigenvalue-trace method.

See https://arxiv.org/abs/2108.01622 Eq.(B3)

deepquantum.photonic.hafnian_.hafnian_batch(matrix: Tensor, loop: bool = False) → Tensor#: Calculate the batch hafnian.

deepquantum.photonic.mapper module#

Map the quantum gate to photonic quantum circuit

class deepquantum.photonic.mapper.UnitaryMapper(nqubit: int, nmode: int, ugate: Any, success: float, aux: List | None = None, aux_pos: List | None = None)#

基类：object

Map the quantum gate to the unitary matrix of the photonic quantum circuit based on dual-rail encoding.

参数:

nqubit (int) – The number of qubits of the quantum gates.
nmode (int) – The number of modes in the circuit.
ugate (Any) – The target quantum gate.
success (float) – The square root of the probability of the target quantum gate. The preferred value is 1/3 for 2 qubits or 1/4 for 3 qubits.
aux (List or None, optional) – Two auxiliary modes in the circuit, which could be [0,0] or [1,0]. Default: None
aux_pos (List or None, optional) – The positions of the auxiliary modes. Default: None (which means the last two modes).

create_basis(aux_position)#: Create the nqubit bases in dual-rail encoding.

get_coeff_sym(input_state: Tensor, output_states: List | None = None)#: Return the transfer state coefficient in a symbolic way.

get_indx_coeff(coeff_i, all_inputs=None)#: Get the index of y_var for given state transfer coefficients.

indx_eqs()#: Get the dictinonary of indices of y_mat for each nonlinear equations for n modes.

static single_prod(single_idx_i, y_test)#

static single_output(single_idx, y_test)#

get_transfer_mat(y)#

f_real(y)#

Construct $2^{nqubit}*2^{nqubit}$ equations for $n*n$ matrix y, obtain real solutions for real part of u_gate.

参数:: y – an array with $n^2$ element

f_real_unitary(y)#: Return the quantum gate constrains and the unitary constrains.

static unitary_constrains(u_temp: Tensor)#: Return $n^2$ equations for $n*n$ matrix with unitary condition.

f_complex_unitary(paras)#: Return quantum gate constrains and the unitary constrains.

f_complex(y)#

Construct $2^{nqubit}*2^{nqubit}$ equations for $n*n$ matrix y, obtain complex solutions.

参数:: y – an array with $2*n^2$ element

static unitary_constrains_complex(u_temp: Tensor)#: Return $2*n^2$ equations for $n*n$ complex matrix with unitary condition.

solve_eqs_real(total_trials=10, trials=1000, precision=1e-06)#: Solve the non-linear eqautions for matrix satisfying ugate with real solution.

solve_eqs_complex(total_trials=10, trials=1000, precision=1e-05)#: Solve the non-linear eqautions for matrix satisfying ugate with complex solution.

static exchange(matrix, aux1_pos)#

static sub_matrix_sym(unitary, input_state, output_state)#: Get the sub_matrix of transfer probs for given input and output state.

static set_copy_indx(state)#: Pick up indices from the nonezero elements of state, repeat times depend on the nonezero value.

static permanent(mat)#: Use Ryser formula for permanent, only valid for square matrix.

static product_factorial(state)#: Get the product of the factorial from the state, i.e., $|s_1,s_2,...s_n> --> s_1!*s_2!*...s_n!$.

static create_subset(num_coincidence)#: Get all subset from $\{1,2,...n\}$.

static save_dict(dictionary, file_path)#

static plot_u(unitary, vmax=1, vmin=0, fs=20, len_ticks=5, cl='RdBu')#

Plot the matrix in graphic way.

参数:

unitary – the plotting matrix
vmax – max value of the unitary matrix
vmin – min value of the unitary matrix
fs – fontsize
len_ticks – number of ticks in colorbar
cl – color of plotting

static is_unitary(u_temp)#: Check the matrix is unitary or not.

state_basis()#: Map ‘000’ to dual_rail.

deepquantum.photonic.measurement module#

Photonic measurements

class deepquantum.photonic.measurement.Generaldyne(cov_m: Any, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, name: str = 'Generaldyne', noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Operation

General-dyne measurement.

参数:

cov_m (Any) – The covariance matrix for the general-dyne measurement.
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
name (str, optional) – The name of the measurement. Default: ``’Generaldyne’`
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

forward(x: List[Tensor]) → List[Tensor]#

Perform a forward pass for Gaussian states.

See Quantum Continuous Variables: A Primer of Theoretical Methods (2024) by Alessio Serafini Eq.(5.143) and Eq.(5.144) in page 121

class deepquantum.photonic.measurement.Homodyne(phi: Any | None = None, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, eps: float = 0.0002, requires_grad: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1, name: str = 'Homodyne')#

基类：Generaldyne

Homodyne measurement.

参数:

phi (Any, optional) – The homodyne measurement angle. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
eps (float, optional) – The measurement accuracy. Default: 2e-4
requires_grad (bool, optional) – Whether the parameter is nn.Parameter or buffer. Default: False (which means buffer)
name (str, optional) – The name of the gate. Default: 'Homodyne'
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

inputs_to_tensor(inputs: Any | None = None) → Tensor#: Convert inputs to torch.Tensor.

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

forward(x: List[Tensor]) → List[Tensor]#: Perform a forward pass for Gaussian states.

deepquantum.photonic.operation module#

Base classes

class deepquantum.photonic.operation.Operation(name: str | None = None, nmode: int = 1, wires: int | List | None = None, cutoff: int = 2, den_mat: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Module

A base class for quantum operations.

参数:

name (str or None, optional) – The name of the quantum operation. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (int, List or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int, optional) – The Fock space truncation. Default: 2
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

tensor_rep(x: Tensor) → Tensor#: Get the tensor representation of the state.

init_para() → None#: Initialize the parameters.

forward(x: Tensor) → Tensor#: Perform a forward pass.

class deepquantum.photonic.operation.Gate(name: str | None = None, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Operation

A base class for photonic quantum gates.

参数:

name (str or None, optional) – The name of the gate. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

update_matrix() → Tensor#: Update the local unitary matrix acting on creation operators.

get_unitary() → Tensor#: Get the global unitary matrix acting on creation operators.

get_matrix_state(matrix: Tensor) → Tensor#: Get the local transformation matrix acting on Fock state tensors.

update_matrix_state() → Tensor#: Update the local transformation matrix acting on Fock state tensors.

op_state_tensor(x: Tensor) → Tensor#: Perform a forward pass for state tensors.

op_den_mat(x: Tensor) → Tensor#: Perform a forward pass for density matrices.

update_transform_xp() → Tuple[Tensor, Tensor]#: Update the local affine symplectic transformation acting on quadrature operators in xxpp order.

get_symplectic() → Tensor#: Get the global symplectic matrix acting on quadrature operators in xxpp order.

get_displacement() → Tensor#: Get the global displacement vector acting on quadrature operators in xxpp order.

op_gaussian(x: List[Tensor]) → List[Tensor]#: Perform a forward pass for Gaussian states.

get_mpo() → Tuple[List[Tensor], int]#

Convert gate to MPO form with identities at empty sites.

备注

If sites are not adjacent, insert identities in the middle, i.e.,

>>>      |       |            |   |   |
>>>    --A---x---B--   ->   --A---I---B--
>>>      |       |            |   |   |

where

>>>         a
>>>         |
>>>    --i--I--j--
>>>         |
>>>         b

means $\delta_{i,j} \delta_{a,b}$

op_mps(mps: MatrixProductState) → MatrixProductState#: Perform a forward pass for the MatrixProductState.

forward(x: Tensor | List[Tensor] | MatrixProductState) → Tensor | List[Tensor] | MatrixProductState#: Perform a forward pass.

class deepquantum.photonic.operation.Channel(name: str | None = None, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None)#

基类：Operation

A base class for photonic quantum channels.

参数:

name (str or None, optional) – The name of the channel. Default: None
nmode (int, optional) – The number of modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None

update_matrix_state() → Tensor#: Update the local Kraus matrices acting on Fock state density matrices.

op_den_mat(x: Tensor) → Tensor#: Perform a forward pass for density matrices.

update_transform_xy() → Tuple[Tensor, Tensor]#: Update the local transformation matrices X and Y acting on Gaussian states.

op_gaussian(x: List[Tensor]) → List[Tensor]#

Perform a forward pass for Gaussian states.

See Quantum Continuous Variables: A Primer of Theoretical Methods (2024) by Alessio Serafini Eq.(5.35-5.37) in page 90

forward(x: Tensor | List[Tensor]) → Tensor | List[Tensor]#: Perform a forward pass.

class deepquantum.photonic.operation.Delay(name='Delay', ntau: int = 1, nmode: int = 1, wires: int | List[int] | None = None, cutoff: int | None = None, den_mat: bool = False, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：Operation

Delay loop.

参数:

name (str or None, optional) – The name of the quantum operation. Default: 'Delay'
ntau (int, optional) – The number of modes in the delay loop. Default: 1
nmode (int, optional) – The number of spatial modes that the quantum operation acts on. Default: 1
wires (int, List[int] or None, optional) – The indices of the modes that the quantum operation acts on. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
den_mat (bool, optional) – Whether to use density matrix representation. Default: False
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

init_para(inputs: Any | None = None) → None#: Initialize the parameters.

forward(x: Tensor | List[Tensor] | MatrixProductState) → Tensor | List[Tensor] | MatrixProductState#: Perform a forward pass.

deepquantum.photonic.qmath module#

Common functions

deepquantum.photonic.qmath.dirac_ket(matrix: Tensor) → Dict#: Convert the batched Fock state tensor to the dictionary of Dirac ket.

deepquantum.photonic.qmath.sort_dict_fock_basis(state_dict: Dict, idx: int = 0) → Dict#: Sort the dictionary of Fock basis states in the descending order of probs.

deepquantum.photonic.qmath.sub_matrix(u: Tensor, input_state: Tensor, output_state: Tensor) → Tensor#

Get the submatrix for calculating the transfer amplitude and transfer probs from the given matrix, the input state and the output state. The rows are chosen according to the output state and the columns are chosen according to the input state.

参数:

u (torch.Tensor) – The unitary matrix.
input_state (torch.Tensor) – The input state.
output_state (torch.Tensor) – The output state.

deepquantum.photonic.qmath.permanent(mat: Tensor) → Tensor#: Calculate the permanent.

deepquantum.photonic.qmath.create_subset(num_coincidence: int) → Generator[Tensor, None, None]#: Create all subsets from $\{1,2,...,n\}$.

deepquantum.photonic.qmath.get_powerset(n: int) → List#: Get the powerset of $\{0,1,...,n-1\}$.

deepquantum.photonic.qmath.permanent_ryser(mat: Tensor) → Tensor#: Calculate the permanent by Ryser’s formula.

deepquantum.photonic.qmath.product_factorial(state: Tensor) → Tensor#: Get the product of the factorial from the Fock state, i.e., $|s_1,s_2,...s_n> -> s_1!s_2!...s_n!$.

deepquantum.photonic.qmath.fock_combinations(nmode: int, nphoton: int, cutoff: int | None = None, nancilla: int = 0) → List#

Generate all possible combinations of Fock states for a given number of modes, photons, and cutoff.

参数:

nmode (int) – The number of modes in the system.
nphoton (int) – The total number of photons in the system.
cutoff (int or None, optional) – The Fock space truncation. Default: None
nancilla (int, optional) – The number of ancilla modes (NOT limited by cutoff). Default: 0

返回:

A list of all possible Fock states, each represented by a list of occupation numbers for each mode.

返回类型:

List[List[int]]

示例

>>> fock_combinations(2, 3)
[[0, 3], [1, 2], [2, 1], [3, 0]]
>>> fock_combinations(3, 2)
[[0, 0, 2], [0, 1, 1], [0, 2, 0], [1, 0, 1], [1, 1, 0], [2, 0, 0]]
>>> fock_combinations(4, 4, 2)
[[1, 1, 1, 1]]

deepquantum.photonic.qmath.shift_func(l: List, nstep: int) → List#

Shift a list by a number of steps.

If nstep is positive, it shifts to the left.

deepquantum.photonic.qmath.xxpp_to_xpxp(matrix: Tensor) → Tensor#: Transform the representation in xxpp ordering to the representation in xpxp ordering.

deepquantum.photonic.qmath.xpxp_to_xxpp(matrix: Tensor) → Tensor#: Transform the representation in xpxp ordering to the representation in xxpp ordering.

deepquantum.photonic.qmath.quadrature_to_ladder(matrix: Tensor) → Tensor#: Transform the representation in xxpp ordering to the representation in aa^+ ordering.

deepquantum.photonic.qmath.ladder_to_quadrature(matrix: Tensor) → Tensor#: Transform the representation in aa^+ ordering to the representation in xxpp ordering.

deepquantum.photonic.qmath.photon_number_mean_var(cov: Tensor, mean: Tensor) → Tuple[Tensor, Tensor]#: Get the expectation value and variance of the photon number for single-mode Gaussian states.

deepquantum.photonic.qmath.takagi(a: Tensor) → Tuple[Tensor, Tensor]#

Tagaki decomposition for symmetric complex matrix.

See https://math.stackexchange.com/questions/2026110/

deepquantum.photonic.qmath.sample_sc_mcmc(prob_func: Callable, proposal_sampler: Callable, shots: int = 1024, num_chain: int = 5) → defaultdict#: Get the samples of the probability distribution function via SC-MCMC method.

deepquantum.photonic.state module#

Quantum states

class deepquantum.photonic.state.FockState(state: Any, nmode: int | None = None, cutoff: int | None = None, basis: bool = True, den_mat: bool = False)#

基类：Module

A Fock state of n modes, including Fock basis states and Fock state tensors.

参数:

state (Any) – The Fock state. It can be a vacuum state with 'vac' or 'zeros'. It can be a Fock basis state, e.g., [1,0,0], or a Fock state tensor, e.g., [(1/2**0.5, [1,0]), (1/2**0.5, [0,1])]. Alternatively, it can be a tensor representation.
nmode (int or None, optional) – The number of modes in the state. Default: None
cutoff (int or None, optional) – The Fock space truncation. Default: None
basis (bool, optional) – Whether the state is a Fock basis state or not. Default: True
den_mat (bool, optional) – Whether to use density matrix representation. Only valid for Fock state tensor. Default: False

class deepquantum.photonic.state.GaussianState(state: str | List = 'vac', nmode: int | None = None, cutoff: int = 5)#

基类：Module

A Gaussian state of n modes, representing by covariance matrix and displacement vector.

参数:

state (str or List) – The Gaussian state. It can be a vacuum state with 'vac', or arbitrary Gaussian states with [cov, mean]. cov and mean are the covariance matrix and the displacement vector of the Gaussian state, respectively. Use xxpp convention and $\hbar=2$ by default. Default: 'vac'
nmode (int or None, optional) – The number of modes in the state. Default: None
cutoff (int, optional) – The Fock space truncation. Default: 5

check_purity(rtol=1e-05, atol=1e-08)#

Check if the Gaussian state is pure state

See https://arxiv.org/pdf/quant-ph/0503237.pdf Eq.(2.5)

deepquantum.photonic.tdm module#

Time domain multiplexing

class deepquantum.photonic.tdm.QumodeCircuitTDM(nmode: int, init_state: Any, cutoff: int | None = None, name: str | None = None, noise: bool = False, mu: float = 0, sigma: float = 0.1)#

基类：QumodeCircuit

Time-domain-multiplexed photonic quantum circuit.

备注

When using large squeezing parameters, we recommend using a double data type and a smaller eps for Homodyne to avoid issues with non-positive definiteness of the covariance matrix.

参数:

nmode (int) – The number of spatial modes in the circuit.
init_state (Any) – The initial state of the circuit. It can be a vacuum state with 'vac'. For Gaussian backend, it can be arbitrary Gaussian states with [cov, mean]. Use xxpp convention and $\hbar=2$ by default.
cutoff (int or None, optional) – The Fock space truncation. Default: None
name (str or None, optional) – The name of the circuit. Default: None
noise (bool, optional) – Whether to introduce Gaussian noise. Default: False
mu (float, optional) – The mean of Gaussian noise. Default: 0
sigma (float, optional) – The standard deviation of Gaussian noise. Default: 0.1

forward(data: Tensor | None = None, state: Any | None = None, nstep: int | None = None) → List[Tensor]#

Perform a forward pass of the TDM photonic quantum circuit and return the final state.

参数:

data (torch.Tensor or None, optional) – The input data for the encoders with the shape of $(\text{batch}, \text{ntimes}, \text{nfeat})$. Default: None
state (Any, optional) – The initial state for the photonic quantum circuit. Default: None
nstep (int or None, optional) – The number of the evolved time steps. Default: None

返回:

The covariance matrix and displacement vector of the measured final state.

返回类型:

List[torch.Tensor]

get_samples(wires: int | List[int] | None = None) → Tensor#: Get the measured samples according to the given wires.

deepquantum.photonic.torontonian_ module#

functions for torontonian

deepquantum.photonic.torontonian_.get_submat_tor(a: Tensor, z: Tensor) → Tensor#: Get the sub-matrix for torontonian calculation.

deepquantum.photonic.torontonian_.torontonian(o_mat: Tensor, gamma: Tensor | None = None) → Tensor#

Calculate the torontonian function for the given matrix.

See https://research-information.bris.ac.uk/ws/portalfiles/portal/329011096/thesis.pdf Eq.(3.54)

deepquantum.photonic.torontonian_.torontonian_batch(o_mat: Tensor, gamma: Tensor | None = None) → Tensor#: Calculate the batch torontonian.

deepquantum.photonic.utils module#

Utilities

deepquantum.photonic.utils.set_hbar(hbar: float) → None#: Set the global reduced Planck constant.

deepquantum.photonic.utils.set_kappa(kappa: float) → None#: Set the global kappa.

deepquantum.photonic.utils.load_sample(filename)#: load the sample data with the given filename

deepquantum.photonic.utils.save_sample(filename, data)#: save the sample data with the given filename

deepquantum.photonic.utils.load_adj(filename)#: load the adjacent matrix with the given filename

deepquantum.photonic.utils.save_adj(filename, data)#: save the adjacent matrix with the given filename

deepquantum.photonic.utils.mem_to_chunksize(device: device, dtype: dtype) → int | None#

Return the chunk size of vmap according to device free memory and dtype.

Note: Currently only optimized for permanent and complex dtype.

deepquantum.photonic.utils.set_perm_chunksize(device: device, dtype: dtype, chunksize: int | None) → None#: Set the global chunk size for permanent calculations.

Module contents#

Photonic Module