qml.resource.DoubleFactorization¶

class DoubleFactorization(one_electron, two_electron, error=0.0016, rank_r=None, rank_m=None, rank_max=None, tol_factor=1e-05, tol_eigval=1e-05, br=7, alpha=10, beta=20, chemist_notation=False)[source]¶

Bases: pennylane.operation.Operation

Estimate the number of non-Clifford gates and logical qubits for a quantum phase estimation algorithm in second quantization with a double-factorized Hamiltonian.

Atomic units are used throughout the class.

Parameters

one_electron (array[array[float]]) – one-electron integrals
two_electron (tensor_like) – two-electron integrals
error (float) – target error in the algorithm
rank_r (int) – rank of the first factorization of the two-electron integral tensor
rank_m (int) – average rank of the second factorization of the two-electron integral tensor
tol_factor (float) – threshold error value for discarding the negligible factors
tol_eigval (float) – threshold error value for discarding the negligible factor eigenvalues
br (int) – number of bits for ancilla qubit rotation
alpha (int) – number of bits for the keep register
beta (int) – number of bits for the rotation angles
chemist_notation (bool) – if True, the two-electron integrals need to be in chemist notation

Example

>>> symbols  = ['O', 'H', 'H']
>>> geometry = np.array([[0.00000000,  0.00000000,  0.28377432],
>>>                      [0.00000000,  1.45278171, -1.00662237],
>>>                      [0.00000000, -1.45278171, -1.00662237]])
>>> mol = qml.qchem.Molecule(symbols, geometry, basis_name='sto-3g')
>>> core, one, two = qml.qchem.electron_integrals(mol)()
>>> algo = qml.resource.DoubleFactorization(one, two)
>>> print(algo.lamb,  # the 1-Norm of the Hamiltonian
>>>       algo.gates, # estimated number of non-Clifford gates
>>>       algo.qubits # estimated number of logical qubits
>>>       )
53.62085493277858 103969925 290

Theory

To estimate the gate and qubit costs for implementing this method, the Hamiltonian needs to be factorized using the factorize() function following [PRX Quantum 2, 030305 (2021)]. The objective of the factorization is to find a set of symmetric matrices, \(L^{(r)}\), such that the two-electron integral tensor in chemist notation, \(V\), can be computed as

\[V_{ijkl} = \sum_r^R L_{ij}^{(r)} L_{kl}^{(r) T},\]

with the rank \(R \leq n^2\), where \(n\) is the number of molecular orbitals. The matrices \(L^{(r)}\) are diagonalized and for each matrix the eigenvalues that are smaller than a given threshold (and their corresponding eigenvectors) are discarded. The average number of the retained eigenvalues, \(M\), determines the rank of the second factorization step. The 1-norm of the Hamiltonian can then be computed using the norm() function from the electron integrals and the eigenvalues of the matrices \(L^{(r)}\).

The total number of gates and qubits for implementing the quantum phase estimation algorithm for the given Hamiltonian can then be computed using the functions gate_cost() and qubit_cost() with a target error that has the default value of 0.0016 Ha (chemical accuracy). The costs are computed using Eqs. (C39-C40) of [PRX Quantum 2, 030305 (2021)].

Attributes

`arithmetic_depth`	Arithmetic depth of the operator.
`basis`	The basis of an operation, or for controlled gates, of the target operation.
`batch_size`	Batch size of the operator if it is used with broadcasted parameters.
`control_wires`	Control wires of the operator.
`gates`	Return the total number of Toffoli gates needed to implement the double factorization algorithm.
`grad_method`
`grad_recipe`	Gradient recipe for the parameter-shift method.
`has_adjoint`
`has_decomposition`
`has_diagonalizing_gates`
`has_generator`
`has_matrix`
`has_qfunc_decomposition`
`has_sparse_matrix`
`hash`	Integer hash that uniquely represents the operator.
`hyperparameters`	Dictionary of non-trainable variables that this operation depends on.
`id`	Custom string to label a specific operator instance.
`is_hermitian`	This property determines if an operator is hermitian.
`lamb`	Return the 1-Norm of the Hamiltonian.
`name`	String for the name of the operator.
`ndim_params`	Number of dimensions per trainable parameter of the operator.
`num_params`	Number of trainable parameters that the operator depends on.
`num_wires`	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 \(\langle \psi \| U(\mathbf{p})^\dagger \hat{O} U(\mathbf{p})\|\psi\rangle\).
`parameters`	Trainable parameters that the operator depends on.
`pauli_rep`	A `PauliSentence` representation of the Operator, or `None` if it doesn't have one.
`qubits`	Return the number of logical qubits needed to implement the double factorization method.
`resource_keys`
`resource_params`	A dictionary containing the minimal information needed to compute a resource estimate of the operator's decomposition.
`wires`	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 and CNOT have basis = "X", whereas ControlledPhaseShift and RZ have basis = "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 on ndim_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 is None.

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 length 0.

Returns: The control wires of the operation.
Return type: Wires

gates¶

Return the total number of Toffoli gates needed to implement the double factorization algorithm.

The expression for computing the cost is taken from Eqs. (45) and (C39) of [PRX Quantum 2, 030305 (2021)].

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 = False¶

has_diagonalizing_gates = False¶

has_generator = False¶

has_matrix = False¶

has_qfunc_decomposition = False¶

has_sparse_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.

lamb¶

Return the 1-Norm of the Hamiltonian.

The 1-norm of a double-factorized molecular Hamiltonian is computed using Eqs. (15-17) of [Phys. Rev. Research 3, 033055 (2021)]

\[\lambda = ||T|| + \frac{1}{4} \sum_r ||L^{(r)}||^2,\]

where the Schatten 1-norm for a given matrix \(T\) is defined as

\[||T|| = \sum_k |\text{eigvals}[T]_k|.\]

The matrices \(L^{(r)}\) are obtained from factorization of the two-electron integral tensor \(V\) such that

\[V_{ijkl} = \sum_r L_{ij}^{(r)} L_{kl}^{(r) T}.\]

The matrix \(T\) is constructed from the one- and two-electron integrals as

\[T = h_{ij} - \frac{1}{2} \sum_l V_{illj} + \sum_l V_{llij}.\]

The two-electron integral tensor is arranged in chemist notation.

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¶

Number of trainable parameters that the operator depends on.

By default, this property returns as many parameters as were used for the operator creation. If the number of parameters for an operator subclass is fixed, this property can be overwritten to return the fixed value.

Returns: number of parameters
Return type: int

num_wires = -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, or None if it doesn’t have one.

qubits¶

Return the number of logical qubits needed to implement the double factorization method.

The expression for computing the cost is taken from Eq. (C40) of [PRX Quantum 2, 030305 (2021)].

resource_keys = {}¶

resource_params¶

A dictionary containing the minimal information needed to compute a resource estimate of the operator’s decomposition.

The keys of this dictionary should match the resource_keys attribute of the operator class. Two instances of the same operator type should have identical resource_params iff their decompositions exhibit the same counts for each gate type, even if the individual gate parameters differ.

Examples

The MultiRZ has non-empty resource_keys:

>>> qml.MultiRZ.resource_keys
{"num_wires"}

The resource_params of an instance of MultiRZ will contain the number of wires:

>>> op = qml.MultiRZ(0.5, wires=[0, 1])
>>> op.resource_params
{"num_wires": 2}

Note that another MultiRZ may have different parameters but the same resource_params:

>>> op2 = qml.MultiRZ(0.7, wires=[1, 2])
>>> op2.resource_params
{"num_wires": 2}

wires¶

Wires that the operator acts on.

Returns: wires
Return type: Wires

Methods

`adjoint`()	Create an operation that is the adjoint of this one.
`compute_decomposition`(*params[, wires])	Representation of the operator as a product of other operators (static method).
`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_qfunc_decomposition`(*args, ...)	Experimental method to compute the dynamic decomposition of the operator with program capture enabled.
`compute_sparse_matrix`(*params[, format])	Representation of the operator as a sparse matrix in the computational basis (static method).
`decomposition`()	Representation of the operator as a product of other operators.
`diagonalizing_gates`()	Sequence of gates that diagonalize the operator in the computational basis.
`eigvals`()	Eigenvalues of the operator in the computational basis.
`estimation_cost`(lamb, error)	Return the number of calls to the unitary needed to achieve the desired error in quantum phase estimation.
`gate_cost`(n, lamb, error, rank_r, rank_m, ...)	Return the total number of Toffoli gates needed to implement the double factorization algorithm.
`generator`()	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.
`norm`(one, two, eigvals)	Return the 1-norm of a molecular Hamiltonian from the one- and two-electron integrals and eigenvalues of the factorized two-electron integral tensor.
`pow`(z)	A list of new operators equal to this one raised to the given power.
`qubit_cost`(n, lamb, error, rank_r, rank_m, ...)	Return the number of logical qubits needed to implement the double factorization method.
`queue`([context])	Append the operator to the Operator queue.
`simplify`()	Reduce the depth of nested operators to the minimum.
`single_qubit_rot_angles`()	The parameters required to implement a single-qubit gate as an equivalent `Rot` gate, up to a global phase.
`sparse_matrix`([wire_order, format])	Representation of the operator as a sparse matrix in the computational basis.
`terms`()	Representation of the operator as a linear combination of other operators.
`unitary_cost`(n, rank_r, rank_m, rank_max[, ...])	Return the number of Toffoli gates needed to implement the qubitization unitary operator for the double factorization algorithm.

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(*params, wires=None, **hyperparameters)¶

Representation of the operator as a product of other operators (static method).

\[O = O_1 O_2 \dots O_n.\]

Note

Operations making up the decomposition should be queued within the compute_decomposition method.

See also

decomposition().

Parameters

*params (list) – trainable parameters of the operator, as stored in the parameters attribute
wires (Iterable[Any], Wires) – wires that the operator acts on
**hyperparams (dict) – non-trainable hyperparameters of the operator, as stored in the hyperparameters attribute

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

diagonalizing_gates().

Parameters

params (list) – trainable parameters of the operator, as stored in the parameters attribute
wires (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.

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

Operator.matrix() and qml.matrix()

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_qfunc_decomposition(*args, **hyperparameters)¶

Experimental method to compute the dynamic decomposition of the operator with program capture enabled.

When the program capture feature is enabled with qml.capture.enable(), the decomposition of the operator is computed with this method if it is defined. Otherwise, the compute_decomposition() method is used.

The exception to this rule is when the operator is returned from the compute_decomposition() method of another operator, in which case the decomposition is performed with compute_decomposition() (even if this method is defined), and not with this method.

When compute_qfunc_decomposition is defined for an operator, the control flow operations within the method (specifying the decomposition of the operator) are recorded in the JAX representation.

Note

This method is experimental and subject to change.

See also

compute_decomposition().

Parameters

*args (list) – positional arguments passed to the operator, including trainable parameters and wires
**hyperparameters (dict) – non-trainable hyperparameters of the operator, as stored in the hyperparameters attribute

static compute_sparse_matrix(*params, format='csr', **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

sparse_matrix()

Parameters

*params (list) – trainable parameters of the operator, as stored in the parameters attribute
format (str) – format of the returned scipy sparse matrix, for example ‘csr’
**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

compute_decomposition().

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

compute_diagonalizing_gates().

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

compute_eigvals()

Returns: eigenvalues
Return type: tensor_like

static estimation_cost(lamb, error)[source]¶

Return the number of calls to the unitary needed to achieve the desired error in quantum phase estimation.

The expression for computing the cost is taken from Eq. (45) of [PRX Quantum 2, 030305 (2021)].

Parameters

lamb (float) – 1-norm of a second-quantized Hamiltonian
error (float) – target error in the algorithm

Returns

number of calls to unitary

Return type

int

Example

>>> lamb = 72.49779513025341
>>> error = 0.001
>>> estimation_cost(lamb, error)
113880

static gate_cost(n, lamb, error, rank_r, rank_m, rank_max, br=7, alpha=10, beta=20)[source]¶

Return the total number of Toffoli gates needed to implement the double factorization algorithm.

The expression for computing the cost is taken from Eqs. (45) and (C39) of [PRX Quantum 2, 030305 (2021)].

Parameters

n (int) – number of molecular spin-orbitals
lamb (float) – 1-norm of a second-quantized Hamiltonian
error (float) – target error in the algorithm
rank_r (int) – rank of the first factorization of the two-electron integral tensor
rank_m (float) – average rank of the second factorization of the two-electron tensor
rank_max (int) – maximum rank of the second factorization of the two-electron tensor
br (int) – number of bits for ancilla qubit rotation
alpha (int) – number of bits for the keep register
beta (int) – number of bits for the rotation angles

Returns

the number of Toffoli gates for the double factorization method

Return type

int

Example

>>> n = 14
>>> lamb = 52.98761457453095
>>> error = 0.001
>>> rank_r = 26
>>> rank_m = 5.5
>>> rank_max = 7
>>> br = 7
>>> alpha = 10
>>> beta = 20
>>> gate_cost(n, lamb, error, rank_r, rank_m, rank_max, br, alpha, beta)
167048631

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 LinearCombination and SparseHamiltonian respectively).

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)¶

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: Operator

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

compute_matrix()

Parameters: wire_order (Iterable) – global wire order, must contain all wire labels from the operator’s wires
Returns: matrix representation
Return type: tensor_like

static norm(one, two, eigvals)[source]¶

Return the 1-norm of a molecular Hamiltonian from the one- and two-electron integrals and eigenvalues of the factorized two-electron integral tensor.

The 1-norm of a double-factorized molecular Hamiltonian is computed using Eqs. (15-17) of [Phys. Rev. Research 3, 033055 (2021)]

\[\lambda = ||T|| + \frac{1}{4} \sum_r ||L^{(r)}||^2,\]

where the Schatten 1-norm for a given matrix \(T\) is defined as

\[||T|| = \sum_k |\text{eigvals}[T]_k|.\]

The matrices \(L^{(r)}\) are obtained from factorization of the two-electron integral tensor \(V\) such that

\[V_{ijkl} = \sum_r L_{ij}^{(r)} L_{kl}^{(r) T}.\]

The matrix \(T\) is constructed from the one- and two-electron integrals as

\[T = h_{ij} - \frac{1}{2} \sum_l V_{illj} + \sum_l V_{llij}.\]

Note that the two-electron integral tensor must be arranged in chemist notation.

Parameters

one (array[array[float]]) – one-electron integrals
two (array[array[float]]) – two-electron integrals
eigvals (array[float]) – eigenvalues of the matrices obtained from factorizing the two-electron integral tensor

Returns

1-norm of the Hamiltonian

Return type

array[float]

Example

>>> symbols  = ['H', 'H', 'O']
>>> geometry = np.array([[0.00000000,  0.00000000,  0.28377432],
>>>                      [0.00000000,  1.45278171, -1.00662237],
>>>                      [0.00000000, -1.45278171, -1.00662237]], requires_grad=False)
>>> mol = qml.qchem.Molecule(symbols, geometry, basis_name='sto-3g')
>>> core, one, two = qml.qchem.electron_integrals(mol)()
>>> two = np.swapaxes(two, 1, 3) # convert to the chemists notation
>>> _, eigvals, _ = qml.qchem.factorize(two, 1e-5)
>>> print(norm(one, two, eigvals))
52.98762043980203

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]

static qubit_cost(n, lamb, error, rank_r, rank_m, rank_max, br=7, alpha=10, beta=20)[source]¶

Return the number of logical qubits needed to implement the double factorization method.

The expression for computing the cost is taken from Eq. (C40) of [PRX Quantum 2, 030305 (2021)].

Parameters

n (int) – number of molecular spin-orbitals
lamb (float) – 1-norm of a second-quantized Hamiltonian
error (float) – target error in the algorithm
rank_r (int) – rank of the first factorization of the two-electron integral tensor
rank_m (float) – average rank of the second factorization of the two-electron tensor
rank_max (int) – maximum rank of the second factorization of the two-electron tensor
br (int) – number of bits for ancilla qubit rotation
alpha (int) – number of bits for the keep register
beta (int) – number of bits for the rotation angles

Returns

number of logical qubits for the double factorization method

Return type

int

Example

>>> n = 14
>>> lamb = 52.98761457453095
>>> error = 0.001
>>> rank_r = 26
>>> rank_m = 5.5
>>> rank_max = 7
>>> br = 7
>>> alpha = 10
>>> beta = 20
>>> qubit_cost(n, lamb, error, rank_r, rank_m, rank_max, br, alpha, beta)
292

queue(context=<class 'pennylane.queuing.QueuingManager'>)¶: Append the operator to the Operator queue.

simplify()¶

Reduce the depth of nested operators to the minimum.

Returns: simplified operator
Return type: Operator

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, format='csr')¶

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

compute_sparse_matrix()

Parameters

wire_order (Iterable) – global wire order, must contain all wire labels from the operator’s wires
format (str) – format of the returned scipy sparse matrix, for example ‘csr’

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]]

static unitary_cost(n, rank_r, rank_m, rank_max, br=7, alpha=10, beta=20)[source]¶

Return the number of Toffoli gates needed to implement the qubitization unitary operator for the double factorization algorithm.

The expression for computing the cost is taken from Eq. (C39) of [PRX Quantum 2, 030305 (2021)].

Parameters

n (int) – number of molecular spin-orbitals
rank_r (int) – rank of the first factorization of the two-electron integral tensor
rank_m (float) – average rank of the second factorization of the two-electron tensor
rank_max (int) – maximum rank of the second factorization of the two-electron tensor
br (int) – number of bits for ancilla qubit rotation
alpha (int) – number of bits for the keep register
beta (int) – number of bits for the rotation angles

Returns

number of Toffoli gates to implement the qubitization unitary

Return type

int

Example

>>> n = 14
>>> rank_r = 26
>>> rank_m = 5.5
>>> rank_max = 7
>>> br = 7
>>> alpha = 10
>>> beta = 20
>>> unitary_cost(n, rank_r, rank_m, rank_max, br, alpha, beta)
2007

code/api/pennylane.resource.DoubleFactorization

Download Python script

Download Notebook

View on GitHub

qml.resource.DoubleFactorization¶

Theory

Attributes

Methods

Contents

Downloads

qml.resource.DoubleFactorization¶

Theory

Attributes

Methods

Contents

Downloads

Related