qml.kUpCCGSD¶
-
class
kUpCCGSD
(weights, wires, k=1, delta_sz=0, init_state=None, id=None)[source]¶ Bases:
pennylane.operation.Operation
Implements the k-Unitary Pair Coupled-Cluster Generalized Singles and Doubles (k-UpCCGSD) ansatz.
The k-UpCCGSD ansatz calls the
FermionicSingleExcitation()
andFermionicDoubleExcitation()
templates to exponentiate the product of \(k\) generalized singles and pair coupled-cluster doubles excitation operators. Here, “generalized” means that the single and double excitation terms do not distinguish between occupied and unoccupied orbitals. Additionally, the term “pair coupled-cluster” refers to the fact that the double excitations contain only those two-body excitations that move a pair of electrons from one spatial orbital to another. This k-UpCCGSD belongs to the family of Unitary Coupled Cluster (UCC) based ansätze, commonly used to solve quantum chemistry problems on quantum computers.The k-UpCCGSD unitary, within the first-order Trotter approximation for a given integer \(k\), is given by:
\[\hat{U}(\vec{\theta}) = \prod_{l=1}^{k} \bigg(\prod_{p,r}\exp{\Big\{ \theta_{r}^{p}(\hat{c}^{\dagger}_p\hat{c}_r - \text{H.c.})\Big\}} \ \prod_{i,j} \Big\{\exp{\theta_{j_\alpha j_\beta}^{i_\alpha i_\beta} (\hat{c}^{\dagger}_{i_\alpha}\hat{c}^{\dagger}_{i_\beta} \hat{c}_{j_\alpha}\hat{c}_{j_\beta} - \text{H.c.}) \Big\}}\bigg)\]where \(\hat{c}\) and \(\hat{c}^{\dagger}\) are the fermionic annihilation and creation operators. The indices \(p, q\) run over the spin orbitals and \(i, j\) run over the spatial orbitals. The singles and paired doubles amplitudes \(\theta_{r}^{p}\) and \(\theta_{j_\alpha j_\beta}^{i_\alpha i_\beta}\) represent the set of variational parameters.
- Parameters
weights (tensor_like) – Tensor containing the parameters \(\theta_{pr}\) and \(\theta_{pqrs}\) entering the Z rotation in
FermionicSingleExcitation()
andFermionicDoubleExcitation()
. These parameters are the coupled-cluster amplitudes that need to be optimized for each generalized single and pair double excitation terms.wires (Iterable) – wires that the template acts on
k (int) – Number of times UpCCGSD unitary is repeated.
delta_sz (int) – Specifies the selection rule
sz[p] - sz[r] = delta_sz
for the spin-projectionsz
of the orbitals involved in the generalized single excitations.delta_sz
can take the values \(0\) and \(\pm 1\).init_state (array[int]) – Length
len(wires)
occupation-number vector representing the HF state.init_state
is used to initialize the wires.
Usage Details
The number of wires has to be equal to the number of spin-orbitals included in the active space, and should be even.
The number of trainable parameters scales linearly with the number of layers as \(2 k n\), where \(n\) is the total number of generalized singles and paired doubles excitation terms.
An example of how to use this template is shown below:
import pennylane as qml from pennylane import numpy as np # Build the electronic Hamiltonian symbols = ["H", "H"] coordinates = np.array([0.0, 0.0, -0.6614, 0.0, 0.0, 0.6614]) H, qubits = qml.qchem.molecular_hamiltonian(symbols, coordinates) # Define the Hartree-Fock state electrons = 2 ref_state = qml.qchem.hf_state(electrons, qubits) # Define the device dev = qml.device('default.qubit', wires=qubits) # Define the ansatz @qml.qnode(dev) def ansatz(weights): qml.kUpCCGSD(weights, wires=[0, 1, 2, 3], k=1, delta_sz=0, init_state=ref_state) return qml.expval(H) # Get the shape of the weights for this template layers = 1 shape = qml.kUpCCGSD.shape(k=layers, n_wires=qubits, delta_sz=0) # Initialize the weight tensors np.random.seed(24) weights = np.random.random(size=shape) # Define the optimizer opt = qml.GradientDescentOptimizer(stepsize=0.4) # Store the values of the cost function energy = [ansatz(weights)] # Store the values of the circuit weights angle = [weights] max_iterations = 100 conv_tol = 1e-06 for n in range(max_iterations): weights, prev_energy = opt.step_and_cost(ansatz, weights) energy.append(ansatz(weights)) angle.append(weights) conv = np.abs(energy[-1] - prev_energy) if n % 4 == 0: print(f"Step = {n}, Energy = {energy[-1]:.8f} Ha") if conv <= conv_tol: break print("\n" f"Final value of the ground-state energy = {energy[-1]:.8f} Ha") print("\n" f"Optimal value of the circuit parameters = {angle[-1]}")
Step = 0, Energy = -1.08949110 Ha Step = 4, Energy = -1.13370605 Ha Step = 8, Energy = -1.13581648 Ha Step = 12, Energy = -1.13613171 Ha Step = 16, Energy = -1.13618030 Ha Step = 20, Energy = -1.13618779 Ha Final value of the ground-state energy = -1.13618779 Ha Optimal value of the circuit parameters = [[0.97879636 0.46093583 0.98108824 0.45864352 0.65531446 0.44558289]]
Parameter shape
The shape of the weights argument can be computed by the static method
shape()
and used when creating randomly initialised weight tensors:shape = qml.kUpCCGSD.shape(n_layers=2, n_wires=4) weights = np.random.random(size=shape)
>>> weights.shape (2, 6)
Attributes
Arithmetic depth of the operator.
The basis of an operation, or for controlled gates, of the target operation.
Batch size of the operator if it is used with broadcasted parameters.
Control wires of the operator.
Gradient recipe for the parameter-shift method.
Integer hash that uniquely represents the operator.
Dictionary of non-trainable variables that this operation depends on.
Custom string to label a specific operator instance.
This property determines if an operator is hermitian.
String for the name of the operator.
Number of dimensions per trainable parameter of the operator.
Number of trainable parameters that the operator depends on.
Number of wires the operator acts on.
Returns the frequencies for each operator parameter with respect to an expectation value of the form \(\langle \psi | U(\mathbf{p})^\dagger \hat{O} U(\mathbf{p})|\psi\rangle\).
Trainable parameters that the operator depends on.
A
PauliSentence
representation of the Operator, orNone
if it doesn’t have one.Wires that the operator acts on.
-
arithmetic_depth
¶ Arithmetic depth of the operator.
-
basis
¶ The basis of an operation, or for controlled gates, of the target operation. If not
None
, should take a value of"X"
,"Y"
, or"Z"
.For example,
X
andCNOT
havebasis = "X"
, whereasControlledPhaseShift
andRZ
havebasis = "Z"
.- Type
str or None
-
batch_size
¶ Batch size of the operator if it is used with broadcasted parameters.
The
batch_size
is determined based onndim_params
and the provided parameters for the operator. If (some of) the latter have an additional dimension, and this dimension has the same size for all parameters, its size is the batch size of the operator. If no parameter has an additional dimension, the batch size isNone
.- Returns
Size of the parameter broadcasting dimension if present, else
None
.- Return type
int or None
-
control_wires
¶ Control wires of the operator.
For operations that are not controlled, this is an empty
Wires
object of length0
.- Returns
The control wires of the operation.
- Return type
-
grad_method
= None¶
-
grad_recipe
= None¶ Gradient recipe for the parameter-shift method.
This is a tuple with one nested list per operation parameter. For parameter \(\phi_k\), the nested list contains elements of the form \([c_i, a_i, s_i]\) where \(i\) is the index of the term, resulting in a gradient recipe of
\[\frac{\partial}{\partial\phi_k}f = \sum_{i} c_i f(a_i \phi_k + s_i).\]If
None
, the default gradient recipe containing the two terms \([c_0, a_0, s_0]=[1/2, 1, \pi/2]\) and \([c_1, a_1, s_1]=[-1/2, 1, -\pi/2]\) is assumed for every parameter.- Type
tuple(Union(list[list[float]], None)) or None
-
has_adjoint
= False¶
-
has_decomposition
= True¶
-
has_diagonalizing_gates
= False¶
-
has_generator
= False¶
-
has_matrix
= False¶
-
hash
¶ Integer hash that uniquely represents the operator.
- Type
int
-
hyperparameters
¶ Dictionary of non-trainable variables that this operation depends on.
- Type
dict
-
id
¶ Custom string to label a specific operator instance.
-
is_hermitian
¶ This property determines if an operator is hermitian.
-
name
¶ String for the name of the operator.
-
ndim_params
¶ Number of dimensions per trainable parameter of the operator.
By default, this property returns the numbers of dimensions of the parameters used for the operator creation. If the parameter sizes for an operator subclass are fixed, this property can be overwritten to return the fixed value.
- Returns
Number of dimensions for each trainable parameter.
- Return type
tuple
-
num_params
¶
-
num_wires
: Union[int, pennylane.operation.WiresEnum] = -1¶ Number of wires the operator acts on.
-
parameter_frequencies
¶ Returns the frequencies for each operator parameter with respect to an expectation value of the form \(\langle \psi | U(\mathbf{p})^\dagger \hat{O} U(\mathbf{p})|\psi\rangle\).
These frequencies encode the behaviour of the operator \(U(\mathbf{p})\) on the value of the expectation value as the parameters are modified. For more details, please see the
pennylane.fourier
module.- Returns
Tuple of frequencies for each parameter. Note that only non-negative frequency values are returned.
- Return type
list[tuple[int or float]]
Example
>>> op = qml.CRot(0.4, 0.1, 0.3, wires=[0, 1]) >>> op.parameter_frequencies [(0.5, 1), (0.5, 1), (0.5, 1)]
For operators that define a generator, the parameter frequencies are directly related to the eigenvalues of the generator:
>>> op = qml.ControlledPhaseShift(0.1, wires=[0, 1]) >>> op.parameter_frequencies [(1,)] >>> gen = qml.generator(op, format="observable") >>> gen_eigvals = qml.eigvals(gen) >>> qml.gradients.eigvals_to_frequencies(tuple(gen_eigvals)) (1.0,)
For more details on this relationship, see
eigvals_to_frequencies()
.
-
parameters
¶ Trainable parameters that the operator depends on.
-
pauli_rep
¶ A
PauliSentence
representation of the Operator, orNone
if it doesn’t have one.
Methods
adjoint
()Create an operation that is the adjoint of this one.
compute_decomposition
(weights, wires, …[, …])Representation of the operator as a product of other operators.
compute_diagonalizing_gates
(*params, wires, …)Sequence of gates that diagonalize the operator in the computational basis (static method).
compute_eigvals
(*params, **hyperparams)Eigenvalues of the operator in the computational basis (static method).
compute_matrix
(*params, **hyperparams)Representation of the operator as a canonical matrix in the computational basis (static method).
compute_sparse_matrix
(*params, **hyperparams)Representation of the operator as a sparse matrix in the computational basis (static method).
Representation of the operator as a product of other operators.
Sequence of gates that diagonalize the operator in the computational basis.
eigvals
()Eigenvalues of the operator in the computational basis.
expand
()Returns a tape that contains the decomposition of the operator.
Generator of an operator that is in single-parameter-form.
label
([decimals, base_label, cache])A customizable string representation of the operator.
map_wires
(wire_map)Returns a copy of the current operator with its wires changed according to the given wire map.
matrix
([wire_order])Representation of the operator as a matrix in the computational basis.
pow
(z)A list of new operators equal to this one raised to the given power.
queue
([context])Append the operator to the Operator queue.
shape
(k, n_wires, delta_sz)Returns the shape of the weight tensor required for this template.
simplify
()Reduce the depth of nested operators to the minimum.
The parameters required to implement a single-qubit gate as an equivalent
Rot
gate, up to a global phase.sparse_matrix
([wire_order])Representation of the operator as a sparse matrix in the computational basis.
terms
()Representation of the operator as a linear combination of other operators.
-
adjoint
()¶ Create an operation that is the adjoint of this one.
Adjointed operations are the conjugated and transposed version of the original operation. Adjointed ops are equivalent to the inverted operation for unitary gates.
- Returns
The adjointed operation.
-
static
compute_decomposition
(weights, wires, s_wires, d_wires, k, init_state, delta_sz=None)[source]¶ Representation of the operator as a product of other operators.
\[O = O_1 O_2 \dots O_n.\]See also
- Parameters
weights (tensor_like) – tensor containing the parameters entering the Z rotation
wires (Any or Iterable[Any]) – wires that the operator acts on
k (int) – number of times UpCCGSD unitary is repeated
s_wires (Iterable[Any]) – single excitation wires
d_wires (Iterable[Any]) – double excitation wires
init_state (array[int]) – Length
len(wires)
occupation-number vector representing the HF state.
- Returns
decomposition of the operator
- Return type
list[Operator]
-
static
compute_diagonalizing_gates
(*params, wires, **hyperparams)¶ Sequence of gates that diagonalize the operator in the computational basis (static method).
Given the eigendecomposition \(O = U \Sigma U^{\dagger}\) where \(\Sigma\) is a diagonal matrix containing the eigenvalues, the sequence of diagonalizing gates implements the unitary \(U^{\dagger}\).
The diagonalizing gates rotate the state into the eigenbasis of the operator.
See also
- Parameters
params (list) – trainable parameters of the operator, as stored in the
parameters
attributewires (Iterable[Any], Wires) – wires that the operator acts on
hyperparams (dict) – non-trainable hyperparameters of the operator, as stored in the
hyperparameters
attribute
- Returns
list of diagonalizing gates
- Return type
list[Operator]
-
static
compute_eigvals
(*params, **hyperparams)¶ Eigenvalues of the operator in the computational basis (static method).
If
diagonalizing_gates
are specified and implement a unitary \(U^{\dagger}\), the operator can be reconstructed as\[O = U \Sigma U^{\dagger},\]where \(\Sigma\) is the diagonal matrix containing the eigenvalues.
Otherwise, no particular order for the eigenvalues is guaranteed.
See also
- Parameters
*params (list) – trainable parameters of the operator, as stored in the
parameters
attribute**hyperparams (dict) – non-trainable hyperparameters of the operator, as stored in the
hyperparameters
attribute
- Returns
eigenvalues
- Return type
tensor_like
-
static
compute_matrix
(*params, **hyperparams)¶ Representation of the operator as a canonical matrix in the computational basis (static method).
The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order.
See also
- Parameters
*params (list) – trainable parameters of the operator, as stored in the
parameters
attribute**hyperparams (dict) – non-trainable hyperparameters of the operator, as stored in the
hyperparameters
attribute
- Returns
matrix representation
- Return type
tensor_like
-
static
compute_sparse_matrix
(*params, **hyperparams)¶ Representation of the operator as a sparse matrix in the computational basis (static method).
The canonical matrix is the textbook matrix representation that does not consider wires. Implicitly, this assumes that the wires of the operator correspond to the global wire order.
See also
- Parameters
*params (list) – trainable parameters of the operator, as stored in the
parameters
attribute**hyperparams (dict) – non-trainable hyperparameters of the operator, as stored in the
hyperparameters
attribute
- Returns
sparse matrix representation
- Return type
scipy.sparse._csr.csr_matrix
-
decomposition
()¶ Representation of the operator as a product of other operators.
\[O = O_1 O_2 \dots O_n\]A
DecompositionUndefinedError
is raised if no representation by decomposition is defined.See also
- Returns
decomposition of the operator
- Return type
list[Operator]
-
diagonalizing_gates
()¶ Sequence of gates that diagonalize the operator in the computational basis.
Given the eigendecomposition \(O = U \Sigma U^{\dagger}\) where \(\Sigma\) is a diagonal matrix containing the eigenvalues, the sequence of diagonalizing gates implements the unitary \(U^{\dagger}\).
The diagonalizing gates rotate the state into the eigenbasis of the operator.
A
DiagGatesUndefinedError
is raised if no representation by decomposition is defined.See also
- Returns
a list of operators
- Return type
list[Operator] or None
-
eigvals
()¶ Eigenvalues of the operator in the computational basis.
If
diagonalizing_gates
are specified and implement a unitary \(U^{\dagger}\), the operator can be reconstructed as\[O = U \Sigma U^{\dagger},\]where \(\Sigma\) is the diagonal matrix containing the eigenvalues.
Otherwise, no particular order for the eigenvalues is guaranteed.
Note
When eigenvalues are not explicitly defined, they are computed automatically from the matrix representation. Currently, this computation is not differentiable.
A
EigvalsUndefinedError
is raised if the eigenvalues have not been defined and cannot be inferred from the matrix representation.See also
- Returns
eigenvalues
- Return type
tensor_like
-
expand
()¶ Returns a tape that contains the decomposition of the operator.
Warning
This function is deprecated and will be removed in version 0.39. The same behaviour can be achieved simply through ‘qml.tape.QuantumScript(self.decomposition())’.
- Returns
quantum tape
- Return type
-
generator
()¶ Generator of an operator that is in single-parameter-form.
For example, for operator
\[U(\phi) = e^{i\phi (0.5 Y + Z\otimes X)}\]we get the generator
>>> U.generator() 0.5 * Y(0) + Z(0) @ X(1)
The generator may also be provided in the form of a dense or sparse Hamiltonian (using
Hermitian
andSparseHamiltonian
respectively).The default value to return is
None
, indicating that the operation has no defined generator.
-
label
(decimals=None, base_label=None, cache=None)¶ A customizable string representation of the operator.
- Parameters
decimals=None (int) – If
None
, no parameters are included. Else, specifies how to round the parameters.base_label=None (str) – overwrite the non-parameter component of the label
cache=None (dict) – dictionary that carries information between label calls in the same drawing
- Returns
label to use in drawings
- Return type
str
Example:
>>> op = qml.RX(1.23456, wires=0) >>> op.label() "RX" >>> op.label(base_label="my_label") "my_label" >>> op = qml.RX(1.23456, wires=0, id="test_data") >>> op.label() "RX("test_data")" >>> op.label(decimals=2) "RX\n(1.23,"test_data")" >>> op.label(base_label="my_label") "my_label("test_data")" >>> op.label(decimals=2, base_label="my_label") "my_label\n(1.23,"test_data")"
If the operation has a matrix-valued parameter and a cache dictionary is provided, unique matrices will be cached in the
'matrices'
key list. The label will contain the index of the matrix in the'matrices'
list.>>> op2 = qml.QubitUnitary(np.eye(2), wires=0) >>> cache = {'matrices': []} >>> op2.label(cache=cache) 'U(M0)' >>> cache['matrices'] [tensor([[1., 0.], [0., 1.]], requires_grad=True)] >>> op3 = qml.QubitUnitary(np.eye(4), wires=(0,1)) >>> op3.label(cache=cache) 'U(M1)' >>> cache['matrices'] [tensor([[1., 0.], [0., 1.]], requires_grad=True), tensor([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]], requires_grad=True)]
-
map_wires
(wire_map)[source]¶ Returns a copy of the current operator with its wires changed according to the given wire map.
- Parameters
wire_map (dict) – dictionary containing the old wires as keys and the new wires as values
- Returns
new operator
- Return type
-
matrix
(wire_order=None)¶ Representation of the operator as a matrix in the computational basis.
If
wire_order
is provided, the numerical representation considers the position of the operator’s wires in the global wire order. Otherwise, the wire order defaults to the operator’s wires.If the matrix depends on trainable parameters, the result will be cast in the same autodifferentiation framework as the parameters.
A
MatrixUndefinedError
is raised if the matrix representation has not been defined.See also
- Parameters
wire_order (Iterable) – global wire order, must contain all wire labels from the operator’s wires
- Returns
matrix representation
- Return type
tensor_like
-
pow
(z)¶ A list of new operators equal to this one raised to the given power.
- Parameters
z (float) – exponent for the operator
- Returns
list[
Operator
]
-
queue
(context=<class 'pennylane.queuing.QueuingManager'>)¶ Append the operator to the Operator queue.
-
static
shape
(k, n_wires, delta_sz)[source]¶ Returns the shape of the weight tensor required for this template. :param k: Number of layers :type k: int :param n_wires: Number of qubits :type n_wires: int :param delta_sz: Specifies the selection rules
sz[p] - sz[r] = delta_sz
:type delta_sz: int :param for the spin-projectionsz
of the orbitals involved in the single excitations.: :paramdelta_sz
can take the values \(0\) and \(\pm 1\).:- Returns
shape
- Return type
tuple[int]
-
simplify
()¶ Reduce the depth of nested operators to the minimum.
- Returns
simplified operator
- Return type
-
single_qubit_rot_angles
()¶ The parameters required to implement a single-qubit gate as an equivalent
Rot
gate, up to a global phase.- Returns
A list of values \([\phi, \theta, \omega]\) such that \(RZ(\omega) RY(\theta) RZ(\phi)\) is equivalent to the original operation.
- Return type
tuple[float, float, float]
-
sparse_matrix
(wire_order=None)¶ Representation of the operator as a sparse matrix in the computational basis.
If
wire_order
is provided, the numerical representation considers the position of the operator’s wires in the global wire order. Otherwise, the wire order defaults to the operator’s wires.A
SparseMatrixUndefinedError
is raised if the sparse matrix representation has not been defined.See also
- Parameters
wire_order (Iterable) – global wire order, must contain all wire labels from the operator’s wires
- Returns
sparse matrix representation
- Return type
scipy.sparse._csr.csr_matrix
-
terms
()¶ Representation of the operator as a linear combination of other operators.
\[O = \sum_i c_i O_i\]A
TermsUndefinedError
is raised if no representation by terms is defined.- Returns
list of coefficients \(c_i\) and list of operations \(O_i\)
- Return type
tuple[list[tensor_like or float], list[Operation]]