Quantum Circuit Design: A Reinforcement Learning Challenge

Altmann, Philipp ; Bärligea, Adelina; Stein, Jonas ; Kölle, Michael; Gabor, Thomas ; Phan, Thomy und Linnhoff-Popien, Claudia (2024): Quantum Circuit Design: A Reinforcement Learning Challenge. AAMAS 2024: International Conference on Autonomous Agents and Multiagent Systems, Auckland, New Zealand, 06.- 10. Mai 2024. Dastani, Mehdi; Sichman, Jaime Simão; Alechina, Natasha und Dignum, Virginia (eds.) : In: AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems. pp. 2123-2125

Abstract

To assess the prospects of using reinforcement learning (RL) for selecting and parameterizing quantum gates to build viable circuit architectures, we introduce the quantum circuit designer (QCD). By considering quantum control a decision-making problem, we strive to profit from advanced RL exploration mechanisms to overcome the need for granular specification and hand-crafted architectures. To evaluate current state-of-the-art RL algorithms, we define generic objectives that arise from quantum architecture search and circuit optimization. Those evaluation results reveal challenges inherent to learning optimal quantum control.

• Item Type: Conference or Workshop Item (Paper)
• Keywords: architecture search ; circuit optimization ; quantum computing ; reinforcement learning
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISBN: 979-8-4007-0486-4
• Place of Publication: Richland, SC
• Language: English