mm.lab.Amplifier¶
- class mrmustard.lab.Amplifier(gain=1.0, nbar=0.0, gain_trainable=False, nbar_trainable=False, gain_bounds=(1.0, None), nbar_bounds=(0.0, None), modes=None)[source]¶
Bases:
Channel
The noisy amplifier channel.
It corresponds to mixing with a thermal environment and applying a two-mode squeezing gate.
X = sqrt(gain) * I Y = (gain-1) * (2*nbar + 1) * (hbar / 2) * I
If
len(modes) > 1
the gate is applied in parallel to all of the modes provided. Ifgain
is a single float, the parallel instances of the gate share that parameter. To apply mode-specific values use a list of floats. One can optionally set bounds forgain
, which the optimizer will respect.- Parameters:
gain (float or List[float]) – the list of gains (must be > 1)
nbar (float) – the average number of photons in the thermal state
nbar_trainable (bool) – whether nbar is a trainable variable
gain_trainable (bool) – whether gain is a trainable variable
gain_bounds (float, float) – bounds for the gain
nbar_bounds (float, float) – bounds for the average number of photons in the thermal state
modes (optional, List[int]) – the list of modes this gate is applied to
Attributes
The adjoint view of this Tensor (with new ``id``s). That is, ket <-> bra.
A dictionary mapping the input modes to their respective wires.
For backward compatibility.
The list of input modes that are used by this Tensor.
The list of output modes that are used by this Tensor.
The name of this tensor.
The number of modes on which the transformation acts.
A dictionary mapping the output modes to their respective wires.
The set of parameters for this transformation.
The list of all wires in this tensor, sorted as
[ket_in, ket_out, bra_in, bra_out]
.- X_matrix¶
- X_matrix_dual¶
- Y_matrix¶
- Y_matrix_dual¶
- d_vector¶
- d_vector_dual¶
- input¶
A dictionary mapping the input modes to their respective wires.
- is_gaussian = True¶
- modes¶
For backward compatibility. Don’t overuse. It returns a list of modes for this Tensor, unless it’s ambiguous.
- modes_in¶
The list of input modes that are used by this Tensor.
If this tensor has no input modes on the bra side, or if the input modes are equal on both ket and bra sides, it returns the list of modes. Otherwise, it performs the
set()
operation before returning the list (and hence, the order may be unexpected).
- modes_out¶
The list of output modes that are used by this Tensor.
If this tensor has no output modes on the bra side, or if the output modes are equal on both ket and bra sides, it returns the list of modes. Otherwise, it performs the
set()
operation before returning the list (and hence, the order may be unexpected).
- name¶
The name of this tensor.
- num_modes¶
The number of modes on which the transformation acts.
- output¶
A dictionary mapping the output modes to their respective wires.
- parallelizable = True¶
- parameter_set¶
The set of parameters for this transformation.
- short_name = 'Amp'¶
- wires¶
The list of all wires in this tensor, sorted as
[ket_in, ket_out, bra_in, bra_out]
.
Methods
XYd
([allow_none])Returns the
`(X, Y, d)`
triple.XYd_dual
([allow_none])Returns the
`(X, Y, d)`
triple of the dual of the current transformation.bargmann
([numpy])change_modes
([modes_in_ket, modes_out_ket, ...])Changes the modes in this tensor.
choi
([cutoffs, shape, dual])Returns the Choi representation of the transformation.
dual
(state)Applies the dual of this transformation to the given
state
and returns the transformed state.primal
(state)Applies this transformation to the given
state
and returns the transformed state.shape
([default_dim, out_in])Returns the shape of the underlying tensor, as inferred from the dimensions of the individual wires.
unpack_shape
(shape)Unpack the given
shape
into the shapes of the input and output wires on ket and bra sides.value
(shape)The value of this tensor.
- XYd(allow_none=True)¶
Returns the
`(X, Y, d)`
triple.Override in subclasses if computing
X
,Y
andd
together is more efficient.- Return type:
Tuple
[Optional
[ndarray
[Tuple
[int
,int
],TypeVar
(R
,float16
,float32
,float64
)]],Optional
[ndarray
[Tuple
[int
,int
],TypeVar
(R
,float16
,float32
,float64
)]],Optional
[ndarray
[Tuple
[int
],TypeVar
(R
,float16
,float32
,float64
)]]]
- XYd_dual(allow_none=True)¶
Returns the
`(X, Y, d)`
triple of the dual of the current transformation.Override in subclasses if computing
Xdual
,Ydual
andddual
together is more efficient.- Return type:
tuple
[Optional
[ndarray
[Tuple
[int
,int
],TypeVar
(R
,float16
,float32
,float64
)]],Optional
[ndarray
[Tuple
[int
,int
],TypeVar
(R
,float16
,float32
,float64
)]],Optional
[ndarray
[Tuple
[int
],TypeVar
(R
,float16
,float32
,float64
)]]]
- bargmann(numpy=False)¶
- change_modes(modes_in_ket=None, modes_out_ket=None, modes_in_bra=None, modes_out_bra=None)¶
Changes the modes in this tensor.
- Parameters:
name – The name of this tensor.
modes_in_ket (
Optional
[list
[int
]]) – The input modes on the ket side.modes_out_ket (
Optional
[list
[int
]]) – The output modes on the ket side.modes_in_bra (
Optional
[list
[int
]]) – The input modes on the bra side.modes_out_bra (
Optional
[list
[int
]]) – The output modes on the bra side.
- Raises:
ValueError – if one or more wires in this tensor are already connected.
- Return type:
None
- choi(cutoffs=None, shape=None, dual=False)¶
Returns the Choi representation of the transformation.
If specified,
shape
takes precedence overcutoffs
. Theshape
is in the order(out_L, in_L, out_R, in_R)
.- Parameters:
cutoffs (
Optional
[Sequence
[int
]]) – the cutoffs of the input and output modesshape (
Optional
[Sequence
[int
]]) – the shape of the Choi matrixdual (
bool
) – whether to return the dual Choi
- dual(state)¶
Applies the dual of this transformation to the given
state
and returns the transformed state.
- primal(state)¶
Applies this transformation to the given
state
and returns the transformed state.
- shape(default_dim=None, out_in=False)¶
Returns the shape of the underlying tensor, as inferred from the dimensions of the individual wires.
If
out_in
isFalse
, the shape returned is in the order(in_ket, in_bra, out_ket, out_bra)
. Otherwise, it is in the order(out_ket, out_bra, in_ket, in_bra)
.- Parameters:
default_dim (
Optional
[int
]) – The default dimension of wires with unspecified dimension.out_in – Whether to return output shapes followed by input shapes or viceversa.
- unpack_shape(shape)¶
Unpack the given
shape
into the shapes of the input and output wires on ket and bra sides.- Parameters:
shape (
Tuple
[int
]) – A shape.- Returns:
The shape of the input wires on the ket side. shape_out_ket: The shape of the output wires on the ket side. shape_in_bra: The shape of the input wires on the bra side. shape_out_bra: The shape of the output wires on the bra side.
- Return type:
shape_in_ket
- value(shape)¶
The value of this tensor.
- Parameters:
shape (
Tuple
[int
]) – the shape of this tensor- Returns:
the unitary matrix in Fock representation
- Return type:
ComplexTensor