Neural Predictor based Quantum Architecture Search

Abstract

Variational quantum algorithms (VQAs) are widely speculated to deliver quantum advantages for practical problems under the quantum-classical hybrid computational paradigm in the near term. Both theoretical and practical developments of VQAs share many similarities with those of deep learning. For instance, a key component of VQAs is the design of task-dependent parameterized quantum circuits (PQCs) as in the case of designing a good neural architecture in deep learning. Partly inspired by the recent success of AutoML and neural architecture search (NAS), quantum architecture search (QAS) is a collection of methods devised to engineer an optimal task-specific PQC. It has been proven that QAS-designed VQAs can outperform expert-crafted VQAs under various scenarios. In this work, we propose to use a neural network based predictor as the evaluation policy for QAS. We demonstrate a neural predictor guided QAS can discover powerful PQCs, yielding state-of-the-art results for various examples from quantum simulation and quantum machine learning. Notably, neural predictor guided QAS provides a better solution than that by the random-search baseline while using an order of magnitude less of circuit evaluations. Moreover, the predictor for QAS as well as the optimal ansatz found by QAS can both be transferred and generalized to address similar problems.

10.2 pages + supplemental materials, 10 figures

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Cited by in corpus (14)

• The Variational Quantum Eigensolver: a review of methods and best practices
• QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
• Artificial Intelligence and Machine Learning for Quantum Technologies
• TensorCircuit: a Quantum Software Framework for the NISQ Era
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation
• Variational Quantum-Neural Hybrid Eigensolver
• Quantum Architecture Search with Meta-learning
• Quantum Architecture Search via Deep Reinforcement Learning
• Probing many-body localization by excited-state VQE
• Hierarchical quantum circuit representations for neural architecture search
• Automated Quantum Circuit Design with Nested Monte Carlo Tree Search
• GSQAS: Graph Self-supervised Quantum Architecture Search
• Supervised learning of random quantum circuits via scalable neural networks
• Accelerating variational quantum algorithms with multiple quantum processors