Abstract
We present a hybrid quantum-classical neural network that can be trained to perform electronic structure calculation and generate potential energy curves of simple molecules. The method is based on the combination of parameterized quantum circuits and measurements. With unsupervised training, the neural network can generate electronic potential energy curves based on training at certain bond lengths. To demonstrate the power of the proposed new method, we present the results of using the quantum-classical hybrid neural network to calculate ground state potential energy curves of simple molecules such as H, LiH, and BeH. The results are very accurate and the approach could potentially be used to generate complex molecular potential energy surfaces.
Highlights
Many quantum algorithms have been proposed to solve quantum chemistry problems [2,3,4], such as the Phase Estimation Algorithm; Aspuru-Guzik et al [5,6,7,8] to calculate eigenstate energies of simple molecules; the Variational Quantum Eigensolver (VQE) [9,10,11] to solve electronic structure problems; quantum algorithms for open quantum dynamics [12]; and benchmark calculations for two-electron molecules conducted on quantum computers [13]
Quantum machine learning should not solely focus on parameterized quantum circuit (PQC) and nonlinear operations are needed for the quantum neural network
We proposed a new hybrid quantum-classical neural network by combing PQC
Summary
Quantum computing has shown its great potential in advancing quantum chemistry research [1].Many quantum algorithms have been proposed to solve quantum chemistry problems [2,3,4], such as the Phase Estimation Algorithm; Aspuru-Guzik et al [5,6,7,8] to calculate eigenstate energies of simple molecules; the Variational Quantum Eigensolver (VQE) [9,10,11] to solve electronic structure problems; quantum algorithms for open quantum dynamics [12]; and benchmark calculations for two-electron molecules conducted on quantum computers [13]. Using quantum computing techniques to perform machine learning tasks [14] has received much attention recently including quantum data classification [15,16], quantum generative learning [17,18], and quantum neural network approximating nonlinear functions [19]. Applying the various quantum machine learning techniques to quantum chemistry is a natural extension [20,21]. Previous studies focused solely on quantum circuits with only a few nonlinear operations, which are introduced by data encoding [19,22] or repeated measurements until success [23]. Quantum machine learning should not solely focus on PQC and nonlinear operations are needed for the quantum neural network
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