Variational quantum compiling with double Q-learning

Abstract

Quantum compiling aims to construct a quantum circuit V by quantum gates drawn from a native gate alphabet, which is functionally equivalent to the target unitary U. It is a crucial stage for the running of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices. However, the space for structure exploration of quantum circuit is enormous, resulting in the requirement of human expertise, hundreds of experimentations or modifications from existing quantum circuits. In this paper, we propose a variational quantum compiling (VQC) algorithm based on reinforcement learning (RL), in order to automatically design the structure of quantum circuit for VQC with no human intervention. An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q-learning with ε-greedy exploration strategy and experience replay. At first, the agent randomly explores a number of quantum circuits with different structures, and then iteratively discovers structures with higher performance on the learning task. Simulation results show that the proposed method can make exact compilations with less quantum gates compared to previous VQC algorithms. It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices, and enable quantum algorithms especially for complex algorithms to be executed within coherence time.

21 pages, 10 figures

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• GSQAS: Graph Self-supervised Quantum Architecture Search
• A hybrid classical-quantum approach to speed-up Q-learning
• Taming quantum systems: A tutorial for using shortcuts-to-adiabaticity, quantum optimal control, and reinforcement learning
• Detection of entangled states supported by reinforcement learning
• Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification
• Quantum continual learning of quantum data realizing knowledge backward transfer
• Unitary Synthesis of Clifford+T Circuits with Reinforcement Learning
• Assisted quantum simulation of open quantum systems
• Quantum Circuit Discovery for Fault-Tolerant Logical State Preparation with Reinforcement Learning
• State Classification via a Random-Walk-Based Quantum Neural Network
• Bidirectional information flow quantum state tomography
• Policy Gradient Approach to Compilation of Variational Quantum Circuits