VQE Application in Quantum Chemistry Computing

Overview

Quantum chemistry refers to solving the numerical values of the time-dependent or time-independent Schrödinger equations by using the basic theory and method of quantum mechanics. Quantum chemical simulation on high-performance computers has become an important method to study the physical and chemical properties of materials. However, the exact solution of the Schrödinger equation has exponential complexity, which severely constrains the scale of the chemical system that can be simulated. The development of quantum computing in recent years provides a feasible way to solve this problem. It is expected that the Schrödinger equation can be solved with high accuracy on quantum computers under the complexity of polynomials.

Peruzzo et al. first applied the VQE and unitary coupled-cluster theory to quantum chemistry simulation in 2014 to solve the ground state energy of He-H⁺. The VQE is a hybrid quantum-classical algorithm and is widely used in chemical simulation based on quantum algorithms. This tutorial describes how to use the VQE to solve the ground-state energy of a molecular system.

This tutorial consists of the following parts:

Introduction to the quantum chemistry
VQE application
Using MindQuantum to perform VQE simulation with efficient and automatic derivation

This document applies to the CPU environment. You can obtain the complete executable sample code at https://gitee.com/mindspore/mindquantum/blob/r0.8/tutorials/source/7.vqe_for_quantum_chemistry.py.

Environment Preparation

In this tutorial, the following environments need to be installed:

NumPy
SciPy
MindQuantum
MindSpore
PySCF
OpenFermion
OpenFermion-PySCF

The preceding dependencies can be installed by running the pip command.

Importing Dependencies

Import the modules on which this tutorial depends.

from openfermion.chem import MolecularData
from openfermionpyscf import run_pyscf
from mindquantum.core.gates import X
from mindquantum.core.circuit import Circuit
from mindquantum.core.operators import Hamiltonian
from mindquantum.simulator import Simulator
from mindquantum.algorithm.nisq import generate_uccsd
import mindspore as ms

ms.set_context(mode=ms.PYNATIVE_MODE, device_target="CPU")

Quantum Chemistry Computing Method

The core of quantum chemistry is to solve the Schrödinger equation. In general, the solution of time-dependent Schrödinger equation is more complex, so Born-Oppenheimer approximation (BO approximation) is introduced. In BO approximation, the mass of the nucleus is far greater than that of electrons, and the velocity of the nucleus is far lower than that of electrons. Therefore, the nucleus and electrons can be separated from each other, and the time-independent electron motion equation (also called the time-independent Schrödinger equation) can be obtained as follows:

\[ \hat{H} |\Psi\rangle = E |\Psi\rangle \]

\(\hat{H}\) contains the following three items:

\[ \hat{H} = \hat{K} _{e} + \hat{V} _{ee} + \hat{V} _{Ne} \]

They are electron kinetic energy, electron-electron potential energy and electron-nuclear potential energy.

There are many numerical algorithms that can be used to solve the time-independent Schrödinger equation. This tutorial introduces one of these methods: the wave function. Wave function directly solves the eigenfunction and eigenenergy of a given molecular Hamiltonian. At present, there are a large number of open-source software packages, such as PySCF, which can be implemented. Here is a simple example: lithium hydride molecules, using the OpenFermion and OpenFermion-PySCF plug-ins. First, define the molecular structure:

dist = 1.5
geometry = [
    ["Li", [0.0, 0.0, 0.0 * dist]],
    ["H",  [0.0, 0.0, 1.0 * dist]],
]
basis = "sto3g"
spin = 0
print("Geometry: \n", geometry)

Geometry:
    [['Li', [0.0, 0.0, 0.0]], ['H', [0.0, 0.0, 1.5]]]

The code above defines a Li-H key with a length of 1.5Å molecules. The STO-3G basis set is used for computing. Then, OpenFermion-PySCF is used to call PySCF to perform Hartree-Fock (HF), coupled-cluster with singles and doubles (CCSD), and full configuration interaction (FCI) computing. These three methods belong to the wave function. Before starting the computing, first make a brief introduction to these methods.

Wave Function

One of the methods to solve the time-independent Schrödinger equation is the Hartree-Fock (HF) method, which was proposed by Hartree et al. in the 1930s and is the basic method in quantum chemistry computing. The HF method introduces a single determinant approximation, that is, a wave function of the \(N\)-electronic system is represented by a wave function in a determinant form:

\[ | \Psi \rangle = | \psi_{1} \psi_{2} \psi_{3} \dots \psi_{N} \rangle \]

Where \(| \psi_{1} \psi_{2} \psi_{3} \dots \rangle\) represents the Nth-order determinants formed by a set of spin-orbit wave functions \(\{ \pi_{i} \}\). The spin-orbit wave function \(\psi_{i}\) may be further expanded with a set of basis functions in known forms:

\[\psi_{i} = \phi_{i} \eta_{i}\]

\[\phi_{i} = \sum_{\mu}{C_{\mu i} \chi_{\mu}}\]

\(\{\chi_{\mu}\}\) is referred to as a basis function, and may be a Gaussian function or the like. This approximation considers the exchange between electrons, but neglects the correlation between electrons, so it cannot correctly compute the properties such as dissociation energy.

The improvement of the HF method can be derived from the wave function expansion theorem. The wave function expansion theorem can be expressed as follows: if \(\{ \psi_{i} \}\) is a complete set of spin-orbit wave functions, the \(N\)-electronic system wave function may be accurately expanded by a determinant wave function formed by \(\{ \psi_{i} \}\):

\[ | \Psi \rangle = \sum^{\infty} _ {i_{1} < i_{2} < \dots < i_{N}} {C_{i_{1} i_{2} \dots i_{N}} | \psi_{i_{1}} \psi_{i_{2}} \dots \psi_{i_{N}} \rangle} \]

You can obtain the configuration interaction (CI) method:

\[ | \Psi_{CI} \rangle = C_{0} | \Psi_{HF} \rangle + \sum^{a\rightarrow\infty} _{i\in occ, a\not\in occ}{C^{a} _{i} | \Psi^{a} _{i} \rangle } + \sum^{ab\rightarrow\infty} _{ij\in occ, ab\not\in occ}{C^{ab} _{ij} | \Psi^{ab} _{ij} \rangle } \]

\(| \Psi^{a}_{i} \rangle + \dots\) in the preceding formula represents a single excitation wave function of the electron from the orbit \(i\) to the orbit \(a\), and so on. A CI that considers only single excitation and double excitation is called a configuration interaction with singles and doubles (CISD). The CI that takes into account all the ground-state HF wave functions to N excitation wave functions is called full configuration interaction (FCI). The FCI wave function is the exact solution of the time-independent Schrödinger equation under the given basis function.

Second Quantization

Under the second quantization expression, the Hamiltonian of the system has the following form:

\[ \hat{H} = \sum_{p, q}{h^p_q E^p_q} + \sum_{p, q, r, s}{\frac{1}{2} g^{pq} _ {rs} E^{pq}_{rs}} \]

\(E^{p} _{q}\) and \(E^{pq}_ {rs}\) are as follows:

\[\begin{split} \begin{align*} E^{pq} _{rs} &= a^{\dagger} _{p} a^{\dagger} _{q} a _ {r} a _ {s}\\ E^p_q &= a^{\dagger}_pa_q \end{align*} \end{split}\]

\(a^{\dagger} _{p}\) and \(a_ {q}\) are creation operator and annihilation operator, respectively.

The excited-state wave function can be expressed conveniently by using a second quantization expression method:

\[ | \Psi^{abc\dots} _ {ijk\dots} \rangle = a^{\dagger} _ {a} a^{\dagger} _ {b} a^{\dagger} _ {c} \dots a _ {i} a_{j} a_{k} \dots | \Psi \rangle \]

An improvement to the CI method is the coupled-cluster theory (CC). Exponential operators are introduced to CC:

\[ | \Psi_{CC} \rangle = \exp{(\hat{T})} | \Psi_{HF} \rangle \]

The coupled-cluster operator \(\hat{T}\) is the sum of excitation operators.

\[ \hat{T} = \sum_{p\not\in occ, q\in occ}{\theta^{p} _ {q} E^{p} _ {q}} + \sum_{pq\not\in occ, rs\in occ}{\theta^{pq} _ {rs} E^{pq} _ {rs}} + \dots \]

\(\theta\) is similar to \(C\) in the CI method, and is the parameter to be solved. It is easy to know from the Taylor’s expansion of the exponent that even if the coupled-cluster operator \(\hat{T}\) includes only a low-order excitation term, the \(\exp{(\hat{T})}\) may also implicitly include a high-order excitation term. This also makes a convergence speed of the CC method to the FCI wave function much faster than that of the CI. For truncation to K excitation, for example, K=2, the accuracy of the CCSD exceeds that of the CISD.

The effect of electron correlation is to reduce the total energy, so the ground state energy of HF is slightly higher than that of CCSD and FCI. In addition, it is easy to find that the computing volume of FCI is much greater than that of CCSD and HF. The MolecularData function encapsulated by OpenFermion and the run_pyscf function encapsulated by OpenFermion-PySCF are used for demonstration.

molecule_of = MolecularData(
    geometry,
    basis,
    multiplicity=2 * spin + 1
)
molecule_of = run_pyscf(
    molecule_of,
    run_scf=1,
    run_ccsd=1,
    run_fci=1
)

print("Hartree-Fock energy: %20.16f Ha" % (molecule_of.hf_energy))
print("CCSD energy: %20.16f Ha" % (molecule_of.ccsd_energy))
print("FCI energy: %20.16f Ha" % (molecule_of.fci_energy))

Hartree-Fock energy:  -7.8633576215351200 Ha
CCSD energy:  -7.8823529091527051 Ha
FCI energy:  -7.8823622867987249 Ha

In the preceding example, HF, CCSD, and FCI are used to compute the total energy. If you collect statistics on the runtime, you will find that \(T_{HF}<T_{CCSD}\ll T_{FCI}\). It is more obvious if you use the system with larger calculation amount, such as ethylene molecule. In addition, for the total computed energy, you will find that \(E_{HF}>E_{CCSD}>E_{FCI}\). After the computing is complete, save the result to the molecule_file file (molecule_of.filename).

molecule_of.save()
molecule_file = molecule_of.filename
print(molecule_file)

One of the major obstacles to quantum chemistry is the volume of computation. As the system size (electron number and atomic number) increases, the time required for solving the FCI wave function and ground state energy increases by about \(2^{N}\). Even for small molecules such as ethylene molecules, FCI computing is not easy. Quantum computers provide a possible solution to this problem. Research shows that quantum computers can simulate the time-dependent evolution of Hamiltonian in terms of polynomial time complexity. Compared with classical computers, quantum computers exponentially accelerate the chemical simulation on quantum processors. This tutorial introduces one of the quantum algorithms: VQE.

Variational Quantum Eigensolver (VQE)

The VQE is a hybrid quantum-classical algorithm. It uses the variational principle to solve the ground state wave function. The optimization of variational parameters is carried out on the classical computer.

Variational Principle

The variational principle may be expressed in the following form:

\[ E_{0} \le \frac{\langle \Psi_{t} | \hat{H} | \Psi_{t} \rangle}{\langle \Psi_{t} | \Psi_{t} \rangle} \]

In the preceding formula, \(| \Psi_{t} \rangle\) indicates the probe wave function. The variational principle shows that the ground state energy obtained by any probe wave function is always greater than or equal to the real ground state energy under certain conditions. The variational principle provides a method for solving the molecular ground state Schrödinger equation. A parameterized function \(f(\theta)\) is used as an approximation of the accurate ground state wave function, and the accurate ground state energy is approximated by optimizing the parameter \(\theta\).

Initial State Preparation

The \(N\)-electron HF wave function also has a very concise form under the quadratic quantization expression:

\[ | \Psi_{HF} \rangle = \prod^{i=0} _{N-1}{a^{\dagger} _{i}| 0 \rangle} \]

The above formula builds a bridge from quantum chemical wave function to quantum computing: \(|0\rangle\) is used to represent a non-occupied orbit, and \(|1\rangle\) is used to represent an orbit occupied by an electron. Therefore, the \(N\)-electron HF wave function may be mapped to a string of \(M+N\) quantum bits \(| 00\dots 11\dots \rangle\). \(M\) indicates the number of unoccupied tracks.

The following code builds an HF initial state wave function corresponding to the LiH molecule. In Jordan-Wigner transformation, \(N\) \(\text{X}\) gates are applied to \(|000\dots\rangle\).

hartreefock_wfn_circuit = Circuit([X.on(i) for i in range(molecule_of.n_electrons)])
print(hartreefock_wfn_circuit)

q0: ──X──

q1: ──X──

q2: ──X──

q3: ──X──

We can build a probe wave function in the following form:

\[ | \Psi_{t} \rangle = U(\theta) | \Psi_{HF} \rangle \]

\(U(\theta)\) represents a unitary transformation that may be simulated by using a quantum circuit. \(| \Psi_{HF} \rangle\) is used as an initial state, and may be conveniently prepared by using a plurality of single-bit \(\text{X}\) gates. A specific form of the \(U(\theta) | \Psi_{HF} \rangle\) is also referred to as wave function ansatz.

Wave Function Ansatz

The coupled-cluster theory mentioned above is a very efficient wave function ansatz. To use it on a quantum computer, you need to make the following modifications:

\[ | \Psi_{UCC} \rangle = \exp{(\hat{T} - \hat{T}^{\dagger})} | \Psi_{HF} \rangle \]

UCC is short for unitary coupled-cluster theory. \(\hat{T}^{\dagger}\) represents the Hermite conjugate of \(\hat{T}\). In this way, \(\exp{(\hat{T} - \hat{T}^{\dagger})}\) is the unitary operator. Peruzzo et al. first performed chemical simulation experiments on quantum computers using VQE and unitary coupled-cluster with singles and doubles (UCCSD) in 2014. It should be noted that, by default, the parameter \(\{\theta\}\) in the coupled-cluster operator is a real number. There is no problem with this hypothesis in molecular systems. In periodic systems, the study of Liu Jie et al. suggests that a unitary coupled-cluster can result in errors due to the neglect of the complex numbers. This tutorial does not discuss the application of unitary coupled-cluster in periodic systems.

The generate_uccsd function in the circuit module of MindQuantum can be used to read the computing result saved in molecule_file, build the UCCSD wave function by one click, and obtain the corresponding quantum circuit.

ansatz_circuit, \
init_amplitudes, \
ansatz_parameter_names, \
hamiltonian_QubitOp, \
n_qubits, n_electrons = generate_uccsd(molecule_file, threshold=-1)

ccsd:-7.882352909152705.
fci:-7.882362286798725.

generate_uccsd packs functions related to the unitary coupled-cluster, including multiple steps such as deriving a molecular Hamiltonian, building a unitary coupled-cluster ansatz operator, and extracting a coupled-cluster coefficient computed by CCSD. This function reads the molecule by entering its file path. The parameter th indicates the to-be-updated gradient threshold of a parameter in the quantum circuit. In the section Building a Unitary Coupled-Cluster Ansatz Step by Step, we will demonstrate how to use the related interfaces of MindQuantum to complete the steps. A complete quantum circuit includes an HF initial state and a UCCSD ansatz, as shown in the following code:

total_circuit = hartreefock_wfn_circuit + ansatz_circuit
total_circuit.summary()
print("Number of parameters: %d" % (len(ansatz_parameter_names)))

============================Circuit Summary============================
|Total number of gates  : 15172.                                      |
|Parameter gates        : 640.                                        |
|with 44 parameters are : p0, p8, p1, p9, p2, p10, p3, p11, p4, p12...|
|Number qubit of circuit: 12                                          |
=======================================================================
Number of parameters: 44

For the LiH molecule, the UCCSD wave function ansatz includes 44 variational parameters. The total number of quantum bit gates of the circuit is 12612, and a total of 12 quantum bits are needed for simulation.

VQE Procedure

The procedure for solving the molecular ground state by using the VQE is as follows:

Prepare the HF initial state: \(| 00\dots11\dots \rangle\).
Define the wave function ansatz, such as UCCSD.
Convert the wave function into a variational quantum circuit.
Initialize the variational parameters, for example, set all parameters to 0.
Obtain the energy \(E(\theta)\) of the molecular Hamiltonian under the set of variational parameters and the derivative \(\{ {\partial E} / {\partial \theta_{i}} \}\) of the energy about the parameters by means of multiple measurements on the quantum computer.
Use optimization algorithms, such as gradient descent and BFGS, to update variational parameters on classical computers.
Transfer the new variational parameters to the quantum circuit for updating.
Repeat steps 5 to 7 until the convergence criteria are met.
End.

In step 5, the derivative \(\{ {\partial E} / {\partial \theta_{i}} \}\) of the energy about the parameter may be computed by using a parameter-shift rule on a quantum computer, or may be computed by simulating a parameter-shift rule or a finite difference method in a simulator. This is a relatively time-consuming process. Based on the MindSpore framework, MindQuantum provides the automatic derivation function similar to machine learning, which can efficiently compute the derivatives of variational quantum circuits in simulation. The following uses MindQuantum to build a parameterized UCCSD quantum circuit with an automatic derivation function:

sim = Simulator('mqvector', total_circuit.n_qubits)
molecule_pqc = sim.get_expectation_with_grad(Hamiltonian(hamiltonian_QubitOp), total_circuit)

You can obtain the energy \(E(\theta)=\langle \Psi_{UCC}(\theta) | \hat{H} | \Psi_{UCC}(\theta) \rangle\) corresponding to the variational parameter and the derivative of each variational parameter by transferring a specific value of the parameter to molecule_pqc.

Next, steps 5 to 7 in VQE optimization need to be performed, that is, parameterized quantum circuits need to be optimized. Based on the MindSpore framework, you can use the parameterized quantum circuit operator molecule_pqc to build a neural network model, and then optimize the variational parameters by using a method similar to training the neural network.

from mindquantum.framework import MQAnsatzOnlyLayer

molecule_pqcnet = MQAnsatzOnlyLayer(molecule_pqc, 'Zeros')

Here, we manually build a basic MQAnsatzOnlyLayer as a model example. This model can be used similar to a conventional machine learning model, for example, optimizing weights and calculating derivatives.

The built MQAnsatzOnlyLayer uses the "Zeros" keyword to initialize all variational parameters to 0. The computing result of CCSD or second order Møller-Plesset perturbation theory (MP2) can also be used as the initial value of the variational parameters of unitary coupled-clusters. In this case, \(E(\vec{0})=\langle \Psi_{UCC}(\vec{0}) | \hat{H} | \Psi_{UCC}(\vec{0}) \rangle = E_{HF}\).

initial_energy = molecule_pqcnet()
print("Initial energy: %20.16f" % (initial_energy.asnumpy()))

Initial energy:  -7.8633575439453125

Finally, the Adam optimizer of MindSpore is used for optimization. The learning rate is set to \(1\times 10^{-2}\), and the optimization termination standard is set to \(\left.|\epsilon|\right. = \left.|E^{k+1} - E^{k}|\right. \le 1\times 10^{-8}\).

optimizer = ms.nn.Adagrad(molecule_pqcnet.trainable_params(), learning_rate=4e-2)
train_pqcnet = ms.nn.TrainOneStepCell(molecule_pqcnet, optimizer)

eps = 1.e-8
energy_diff = eps * 1000
energy_last = initial_energy.asnumpy() + energy_diff
iter_idx = 0
while abs(energy_diff) > eps:
    energy_i = train_pqcnet().asnumpy()
    if iter_idx % 5 == 0:
        print("Step %3d energy %20.16f" % (iter_idx, float(energy_i)))
    energy_diff = energy_last - energy_i
    energy_last = energy_i
    iter_idx += 1

print("Optimization completed at step %3d" % (iter_idx - 1))
print("Optimized energy: %20.16f" % (energy_i))
print("Optimized amplitudes: \n", molecule_pqcnet.weight.asnumpy())

Step   0 energy  -7.8633575439453125
Step   5 energy  -7.8726239204406738
Step  10 energy  -7.8821778297424316
Step  15 energy  -7.8822836875915527
Step  20 energy  -7.8823199272155762
Step  25 energy  -7.8823370933532715
Optimization completed at step  27
Optimized energy:  -7.8823390007019043
Optimized amplitudes:
 [ 2.7273933e-04  1.9072455e-03  2.9598266e-02  1.5151843e-02
  1.5533751e-06  9.0890197e-04 -5.1002303e-07  1.4072354e-02
 -1.2779084e-05  9.0818782e-04  4.1782801e-06  1.4077923e-02
 -5.2711589e-04  4.2598479e-04  2.6523003e-03  5.3988658e-02
  1.9281749e-04 -1.2321149e-07 -3.1405324e-07  1.9214883e-06
  1.0062347e-06  1.0903094e-06 -1.4285328e-05 -7.8342858e-08
  7.5983076e-04 -1.1218661e-04  8.8862755e-08 -7.2354385e-07
 -5.3567935e-02  3.0693393e-03 -1.3467404e-10 -4.6068820e-09
  4.5405173e-09  1.1786257e-06  8.9121182e-05 -9.4004070e-05
 -1.0432483e-10 -2.8091000e-07  3.0693379e-03 -9.6705053e-06
  7.7026058e-04 -7.3031953e-04  2.3029093e-06  3.5888454e-04]

It can be seen that the computing result of unitary coupled-cluster is very close to that of FCI, and has good accuracy.

Building a Unitary Coupled-Cluster Ansatz Step by Step

In the preceding part, the generate_uccsd is used to build all the content required for designing the unitary coupled-cluster. In this section, the steps are split, we get the coupled-cluster operator, the corresponding quantum circuit and the initial guess of the variational parameters from the classical CCSD results. First, import some extra dependencies, including the related functions of the HiQfermion module in MindQuantum.

from mindquantum.algorithm.nisq import Transform
from mindquantum.algorithm.nisq import get_qubit_hamiltonian
from mindquantum.algorithm.nisq import uccsd_singlet_generator, uccsd_singlet_get_packed_amplitudes
from mindquantum.core.operators import TimeEvolution
from mindquantum.framework import MQAnsatzOnlyLayer

The molecule Hamiltonian uses get_qubit_hamiltonian to read the previous computing result. The result is as follows:

hamiltonian_QubitOp = get_qubit_hamiltonian(molecule_of)

The unitary coupled-cluster operator \(\hat{T} - \hat{T}^{\dagger}\) can be built using uccsd_singlet_generator. Provide the total number of quantum bits (total number of spin orbits) and the total number of electrons, and set anti_hermitian=True.

ucc_fermion_ops = uccsd_singlet_generator(
    molecule_of.n_qubits, molecule_of.n_electrons, anti_hermitian=True)

The ucc_fermion_ops built in the previous step is parameterized. Use the Jordan-Wigner transformation to map the Fermi excitation operator to the Pauli operator:

ucc_qubit_ops = Transform(ucc_fermion_ops).jordan_wigner()

Next, we need to obtain the quantum circuit corresponding to the unitary operator \(\exp{(\hat{T} - \hat{T}^{\dagger})}\). TimeEvolution can generate the circuit corresponding to \(\exp{(-i\hat{H}t)}\), where \(\hat{H}\) is a Hermitian operator, and \(t\) is a real number. Note that when TimeEvolution is used, ucc_qubit_ops already contains the complex number factor \(i\). Therefore, you need to divide ucc_qubit_ops by \(i\) or extract the imaginary part of ucc_qubit_ops.

ansatz_circuit = TimeEvolution(ucc_qubit_ops.imag, 1.0).circuit
ansatz_parameter_names = ansatz_circuit.params_name

ansatz_parameter_names is used to record the parameter names in the circuit. So far, we have obtained the contents required by the VQE quantum circuit, including the Hamiltonian hamiltonian_QubitOp and the parameterized wave function ansatz ansatz_circuit. By referring to the preceding steps, we can obtain a complete state preparation circuit. hartreefock_wfn_circuit mentioned above is used as the Hartree-Fock reference state:

total_circuit = hartreefock_wfn_circuit + ansatz_circuit
total_circuit.summary()

==================================Circuit Summary==================================
|Total number of gates  : 15172.                                                  |
|Parameter gates        : 640.                                                    |
|with 44 parameters are : s_0, d1_0, s_1, d1_1, s_2, d1_2, s_3, d1_3, s_4, d1_4...|
|Number qubit of circuit: 12                                                      |
===================================================================================

Next, you need to provide a reasonable initial value for the variational parameter. The PQCNet built in the preceding text uses 0 as the initial guess by default, which is feasible in most cases. However, using CCSD’s computational data as a starting point for UCC may be better. Use the uccsd_singlet_get_packed_amplitudes function to extract CCSD parameters from molecule_of.

init_amplitudes_ccsd = uccsd_singlet_get_packed_amplitudes(
    molecule_of.ccsd_single_amps, molecule_of.ccsd_double_amps, molecule_of.n_qubits, molecule_of.n_electrons)
init_amplitudes_ccsd = [init_amplitudes_ccsd[param_i] for param_i in ansatz_parameter_names]

MQAnsatzOnlyLayer can be used to easily obtain a machine learning model based on a variational quantum circuit by using a parameter and a quantum circuit:

grad_ops = Simulator('mqvector', total_circuit.n_qubits).get_expectation_with_grad(
    Hamiltonian(hamiltonian_QubitOp.real),
    total_circuit)

molecule_pqcnet = MQAnsatzOnlyLayer(grad_ops)

init_amplitudes_ccsd (coupled-cluster coefficient computed by CCSD) is used as an initial variational parameter:

molecule_pqcnet.weight = ms.Parameter(ms.Tensor(init_amplitudes_ccsd, molecule_pqcnet.weight.dtype))
initial_energy = molecule_pqcnet()
print("Initial energy: %20.16f" % (initial_energy.asnumpy()))

Initial energy:  -7.8173098564147949

In this example, CCSD’s initial guess does not provide a better starting point. You can test and explore more molecules and more types of initial values (such as initial guesses of random numbers). Finally, the VQE is optimized. The optimizer still uses Adam, and the convergence standard remains unchanged. The code used for optimization is basically the same as that described in the preceding sections. You only need to update the corresponding variables.

optimizer = ms.nn.Adagrad(molecule_pqcnet.trainable_params(), learning_rate=4e-2)
train_pqcnet = ms.nn.TrainOneStepCell(molecule_pqcnet, optimizer)

print("eps: ", eps)
energy_diff = eps * 1000
energy_last = initial_energy.asnumpy() + energy_diff
iter_idx = 0
while abs(energy_diff) > eps:
    energy_i = train_pqcnet().asnumpy()
    if iter_idx % 5 == 0:
        print("Step %3d energy %20.16f" % (iter_idx, float(energy_i)))
    energy_diff = energy_last - energy_i
    energy_last = energy_i
    iter_idx += 1

print("Optimization completed at step %3d" % (iter_idx - 1))
print("Optimized energy: %20.16f" % (energy_i))
print("Optimized amplitudes: \n", molecule_pqcnet.weight.asnumpy())

eps:  1e-08
Step   0 energy  -7.8173098564147949
Step   5 energy  -7.8740758895874023
Step  10 energy  -7.8818783760070801
Step  15 energy  -7.8821649551391602
Step  20 energy  -7.8822622299194336
Step  25 energy  -7.8823080062866211
Optimization completed at step  28
Optimized energy:  -7.8823189735412598
Optimized amplitudes:
 [-2.92540470e-04  1.91678782e-03 -2.62904949e-02  1.46486172e-02
 -1.80548541e-05  9.08615650e-04  6.06753974e-06  1.40150227e-02
 -7.58499027e-06  9.07906622e-04  2.58140676e-06  1.40205137e-02
  5.15393389e-04  4.25452046e-04 -2.52626487e-03  5.41330352e-02
  1.68450730e-04 -1.45874014e-06  2.46176114e-05 -5.74097339e-06
 -6.37176697e-07  1.41116643e-05 -6.13132488e-06 -7.78824597e-06
  7.36984774e-04 -1.16545329e-04  1.00961029e-06  4.41450794e-07
 -5.27810790e-02  3.05663864e-03  5.34516487e-10 -1.11836842e-08
  1.16560805e-08  1.39018812e-05  1.05607708e-03 -1.11408660e-03
  7.64744101e-10 -3.32643208e-06  3.05663352e-03  5.93083496e-06
  4.49250219e-04 -4.74061235e-04 -1.41295470e-06  3.50885763e-04]

Summary

In this case, the ground state energy of the LiH molecule is obtained by using the quantum neural network in two methods. In the first method, we use the generate_uccsd function packaged by MindQuantum to generate a quantum neural network that can solve this problem. In the second method, we build a similar quantum neural network step by step. The final results are the same.