Method and System for Quantum Chemistry Modelling
There is described a hybrid quantum-classical computing method and system that leverages quantum processing to generate data that enables the training of neural networks for the purpose of density functional theory (DFT) functional determination. Physical systems are modeled on a quantum processing module and simulated to generate energy values and electronic density functions as training data with sufficient degrees of quality and accuracy. The training data may be in classical form and are used to train a neural network. The trained neural network may then be employed to parameterize DFT functionals.
1 . A method comprising:
receiving, at a density functional theory (DFT) functional module, an electronic density of a physical system; and
determining, by the DFT functional module, from the electronic density, parameters of a DFT functional that model one or more aspects of the physical system, wherein the DFT functional module is trained using training data, the training data including classical data generated from a quantum processing module.
2 . The method of claim 1 , wherein the training data includes a data pair comprising a training electronic density and a training exchange-correlation energy.
3 . The method of claim 1 , wherein the generating of the classical data by the quantum processing module further includes:
constructing a Hamiltonian of the physical system;
mapping fermionic operators of the Hamiltonian to qubit operators;
constructing, from the qubit operators, a set of unitaries;
applying the set of unitaries in accordance with a quantum algorithm onto one or more qubit registers; and
generating the classical data.
4 . The method of claim 3 , wherein the classical data includes one or more of an electronic density function, a total system energy, a classical shadow, and a reduced density matrix.
5 . The method of claim 1 , further including approximating the DFT functional using a Kohn-Sham method, wherein the DFT functional is parameterized using a hybrid functional construction.
6 . The method of claim 5 , wherein the hybrid functional construction is one of an internal method where the DFT functional, represented by E XC , is expressed as
E
XC
=
∫
∑
i
=
1
N
c
i
E
(
i
)
[
n
]
(
r
)
d
3
r
and an external method where the DFT functional is expressed as
E
XC
=
∑
i
=
1
N
c
i
∫
E
(
i
)
[
n
]
(
r
)
d
3
r
=
∑
i
=
1
N
c
i
E
(
i
)
,
wherein the coefficients c i are the parameters of the DFT functional.
7 . The method of claim 3 , wherein the Hamiltonian is constructed based on one of a first quantization formalism and a second quantization formalism.
8 . The method of claim 7 , wherein the Hamiltonian is constructed based on the second quantization formalism, and the fermionic operator to qubit operator mapping is based on a Jordan-Wigner transformation.
9 . The method of claim 3 , wherein the quantum algorithm is any one of quantum phase estimation, variational quantum eigensolver (VQE), adiabatic quantum algorithm, Krylov subspace method, and imaginary-time evolution.
10 . The method of claim 3 , wherein the quantum algorithm is quantum phase estimation, and the applying further includes:
preparing at least one auxiliary qubit in an equal superposition state;
applying each unitary U of the set of unitaries in the form of U 2 t , where t is the number of the at least one auxiliary qubit, onto each of the at least one auxiliary qubit;
applying an inverse quantum Fourier transform on each of the at least one auxiliary qubit;
measuring each of the at least one auxiliary qubit in a computational basis to generate an output bit string;
estimating a phase value based on the output bit string; and
determining a ground-state energy value based on the estimated phase value.
11 . The method of claim 2 , wherein the training of the DFT functional module further includes:
iteratively performing:
sampling the training data,
obtaining a predicted exchange-correlation energy based on the training electronic density,
calculating a loss value between the predicted exchange-correlation energy and the training exchange-correlation energy, and
updating a weight matrix of the DFT functional module based on the loss value; and
storing the updated weight matrix of the DFT functional module.
12 . A system comprising:
a quantum processing module configured to generate classical data for training a density functional theory (DFT) functional module; and
a classical processing module configured to:
generate, by providing an electronic density of a physical system as input to the DFT functional module, parameters of a DFT functional that are used to model one or more aspects of the physical system.
13 . The system of claim 12 , wherein the DFT functional module is a trained deep neural network.
14 . The system of claim 13 , wherein the parameters of the DFT functional are weights of the trained deep neural network.
15 . The system of claim 12 , wherein the quantum processing module is based on any one of superconducting qubits, photonic qubits, trapped-ion qubits, silicon-based qubits, and neutral-atom-based qubits.
16 . The system of claim 12 , wherein the DFT functional module is trained using training data, the training data including the classical data, and the quantum processing module is configured to generate the classical data by:
constructing a Hamiltonian of the physical system;
mapping fermionic operators of the Hamiltonian onto qubit operators;
constructing, from the qubit operators, a set of unitaries;
applying the set of unitaries in accordance with a quantum algorithm onto one or more qubit registers; and
generating the classical data.
17 . The system of claim 16 , wherein the classical data includes one or more of an electronic density function, a total system energy, a classical shadow, and a reduced density matrix.
18 . The system of claim 12 , wherein the DFT functional is parameterized using a hybrid functional construction, the hybrid functional construction being one of an internal method where the DFT functional, represented by E XC , is expressed as
E
XC
=
∫
∑
i
=
1
N
c
i
E
(
i
)
[
n
]
(
r
)
d
3
r
and an external method where the DFT functional is expressed as
E
XC
=
∑
i
=
1
N
c
i
∫
E
(
i
)
[
n
]
(
r
)
d
3
r
=
∑
i
=
1
N
c
i
E
(
i
)
,
wherein the coefficients c i are the parameters of the DFT functional.
19 . The system of claim 16 , wherein the Hamiltonian is constructed based on one of a first quantization formalism and a second quantization formalism.
20 . The system of claim 16 , wherein the quantum algorithm is any one of quantum phase estimation, variational quantum eigensolver (VQE), adiabatic quantum algorithm, Krylov subspace method, and imaginary-time evolution.