Reinforcement Learning for Quantum Phase Estimation Using Deep Q-Network
subject: Deep Q Network(DQN); Eigenphases; IBM Quantum Backend; Noisy Environment; OpenAI Gym; Quantum Circuit; Quantum Optimization; Quantum Phase Estimation(QPE); Reinforcement Learning; Unitary Operator
description: Quantum Phase Estimation(QPE) is a fundamental quantum algorithm that is used for the estimation of eigenphases of unitary operators. Its main goal is to determine the phase associated with each eigenstate. Usually, it take steps such as prepare quantum states, apply controlled unitaries, inverse quantum fourier transformation, and measurement. This study uses the OpenAI Gym framework to build a customized QPE environment. Here, the phase of a randomly generated target unitary operator is estimated using a quantum circuit. Through interaction with this environment, the DQN agent learns the best course of action to increase phase estimation accuracy. It exhibits more flexibility in noisy environments and reduces estimating mistakes. With its insights and approaches for further study in this area, this effort represents a significant advancement in the use of Deep Reinforcement Learning in quantum computing. A Comparative analysis between IBM Quantum(ibm kyiv) and the Aer Simulator on the OpenAI Gym environment using RL agents has been done. 2025 IEEE.
creator: Sundharess, B.; Vikash Krishna, R.; Ss, Gopika; Sinha, Somnath; Donald, Cecil; Paul, Aditi
source: 2025 IEEE Madhya Pradesh Section Conference, MPCON 2025;pp.316-321
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISBN: 979-833151285-9;
language: English
type: Conference paper
coverage: Sundharess B., Christ University, Department of Computer Science, Bangalore, India; Vikash Krishna R., Christ University, Department of Computer Science, Bangalore, India; Ss G., Christ University, Department of Computer Science, Bangalore, India; Sinha S., Christ University, Department of Computer Science, Bangalore, India; Donald C., Christ University, Department of Computer Science, Bangalore, India; Paul A., Banasthali Vidhapith, Department of Computer Science, Jaipur, India