Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sundharess, B.; Vikash Krishna, R.; Ss, Gopika; Sinha, Somnath; Donald, Cecil; Paul, Aditi Title A name given to the resource Reinforcement Learning for Quantum Phase Estimation Using Deep Q-Network Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE Madhya Pradesh Section Conference, MPCON 2025;pp.316-321 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MPCON66082.2025.11256566" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MPCON66082.2025.11256566</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031456433?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031456433?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Deep Q Network(DQN); Eigenphases; IBM Quantum Backend; Noisy Environment; OpenAI Gym; Quantum Circuit; Quantum Optimization; Quantum Phase Estimation(QPE); Reinforcement Learning; Unitary Operator Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151285-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online