A Reinforcement Learning Environment for directed Quantum Circuit Synthesis

A Reinforcement Learning Environment for directed Quantum Circuit Synthesis

Abstract:

Fueled by recent advances in quantum computing technologies, the design of optimized quantum circuits including reliable quantum state preparation are topics gaining more and more importance. Common approaches often require a high amount of Know-How and manual calculation hampering implementation, especially if the involved circuits increase in qubit number and gate count. Hence, addressing the rise in possible gate-to- qubit combinations by utilizing machine learning techniques represents a promising step in the development of the field. The following study aims to provide a reinforcement learning environment enabling the training of agents on the directed quantum-circuit design for the preparation of quantum states. Thus, the trained agents are enabled to create quantum circuits facilitating the preparation of desired target states, which can be handed over as inputs. In the course of this, all generated quantum-circuits are built utilizing gates from the Clifford+T gate set only. Based on the implemented environment, we conducted experiments to investigate the relation between the depth of the reconstructed quantum circuits and the involved target state parameters. The explored parameter-space included the respective qubit number and circuit-depth used for the target initialization. By providing a division of the parameter-space into several difficulty regions and a collection of well-known states, we facilitated benchmarking of different reinforcement learning algorithms on the quantum-circuit synthesis problem. Specific findings of the study include the generation of PPO-algorithm-based agents, which outperform the random-baseline. Through the application of the trained agents on the benchmarking tests we show their ability to reliably design minimal quantum-circuits for a selection of 2-qubit Bell states.

Author:

Tom Schubert

Advisors:

Claudia Linnhoff-Popien, Michael Kölle, Philipp Altmann

Student Thesis | Published November 2023 | Copyright © QAR-Lab
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