Neural network encoded variational quantum algorithms

• URL: http://arxiv.org/abs/2308.01068v1
• Date: Wed, 2 Aug 2023 10:32:57 GMT
• Title: Neural network encoded variational quantum algorithms
• Authors: Jiaqi Miao, Chang-Yu Hsieh and Shi-Xin Zhang
• Abstract summary: We introduce a general framework called neural network (NN) encoded variational quantum algorithms (VQAs) NN-VQA feeds input (such as parameters of a Hamiltonian) from a given problem to a neural network and uses its outputs to parameterize an ansatz circuit for the standard VQA. We present results on NN-variational quantum eigensolver (VQE) for solving the ground state of parameterized XXZ spin models.
• Score: 0.241710192205034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a general framework called neural network (NN) encoded variational quantum algorithms (VQAs), or NN-VQA for short, to address the challenges of implementing VQAs on noisy intermediate-scale quantum (NISQ) computers. Specifically, NN-VQA feeds input (such as parameters of a Hamiltonian) from a given problem to a neural network and uses its outputs to parameterize an ansatz circuit for the standard VQA. Combining the strengths of NN and parameterized quantum circuits, NN-VQA can dramatically accelerate the training process of VQAs and handle a broad family of related problems with varying input parameters with the pre-trained NN. To concretely illustrate the merits of NN-VQA, we present results on NN-variational quantum eigensolver (VQE) for solving the ground state of parameterized XXZ spin models. Our results demonstrate that NN-VQE is able to estimate the ground-state energies of parameterized Hamiltonians with high precision without fine-tuning, and significantly reduce the overall training cost to estimate ground-state properties across the phases of XXZ Hamiltonian. We also employ an active-learning strategy to further increase the training efficiency while maintaining prediction accuracy. These encouraging results demonstrate that NN-VQAs offer a new hybrid quantum-classical paradigm to utilize NISQ resources for solving more realistic and challenging computational problems.

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