Reinforcement Learning with Quantum Variational Circuits

2020/08/15 by Owen Lockwood, Lockwood, Owen, Mei Si +1 · 8 citations
Computer Science · #Computability, Logic, AI Algorithms #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2008.07524

openalex publication_date 2020/08/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning.

Citations

• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
• Dueling Network Architectures for Deep Reinforcement Learning
• Quantum machine learning
• Quantum Computing in the NISQ era and beyond
• Dota 2 with Large Scale Deep Reinforcement Learning

Cited by

• Multivariate Time Series Forecasting with Adaptive Non-Local Observables
• Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks
• Quantum-Embedded Dynamic Security Control using Hybrid Deep Reinforcement Learning
• Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization
• From Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardware
• It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensembles
• Breaking Through Barren Plateaus: Reinforcement Learning Initializations for Deep Variational Quantum Circuits
• Quantum Reinforcement Learning by Adaptive Non-local Observables

Related

• Variational Quantum Circuits for Deep Reinforcement Learning
• A Survey on Quantum Reinforcement Learning
• Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation
• Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems
• Optimizing Quantum Variational Circuits with Deep Reinforcement Learning