Quantum Architecture Search via Deep Reinforcement Learning

Abstract

Recent advances in quantum computing have drawn considerable attention to building realistic application for and using quantum computers. However, designing a suitable quantum circuit architecture requires expert knowledge. For example, it is non-trivial to design a quantum gate sequence for generating a particular quantum state with as fewer gates as possible. We propose a quantum architecture search framework with the power of deep reinforcement learning (DRL) to address this challenge. In the proposed framework, the DRL agent can only access the Pauli-$X$, $Y$, $Z$ expectation values and a predefined set of quantum operations for learning the target quantum state, and is optimized by the advantage actor-critic (A2C) and proximal policy optimization (PPO) algorithms. We demonstrate a successful generation of quantum gate sequences for multi-qubit GHZ states without encoding any knowledge of quantum physics in the agent. The design of our framework is rather general and can be employed with other DRL architectures or optimization methods to study gate synthesis and compilation for many quantum states.

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Cited by in corpus (14)

• The Variational Quantum Eigensolver: a review of methods and best practices
• Variational Quantum Reinforcement Learning via Evolutionary Optimization
• Variational Quantum-Neural Hybrid Eigensolver
• Quantum Architecture Search with Meta-learning
• Reinforcement learning-enhanced protocols for coherent population-transfer in three-level quantum systems
• Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
• Automated Quantum Circuit Design with Nested Monte Carlo Tree Search
• GSQAS: Graph Self-supervised Quantum Architecture Search
• Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks
• Enhancing variational quantum state diagonalization using reinforcement learning techniques
• A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search
• Quantum Architecture Search for Quantum Monte Carlo Integration via Conditional Parameterized Circuits with Application to Finance
• Application of ZX-calculus to Quantum Architecture Search
• Fast quantum gate design with deep reinforcement learning using real-time feedback on readout signals