Quantum Logic Gate Synthesis as a Markov Decision Process

Understand

Reinforcement learning has witnessed recent applications to a variety of tasks in quantum programming.

• The underlying assumption is that those tasks could be modeled as Markov Decision Processes (MDPs).
• Here, we investigate the feasibility of this assumption by exploring its consequences for two fundamental tasks in quantum programming: state preparation and gate compilation.
• By forming discrete MDPs, focusing exclusively on the single-qubit case (both with and without noise), we solve for the optimal policy exactly through policy iteration.