Presentation
Agent-Per-Qubit: Automated Qubit Placement for Fault-Tolerant Quantum Computing
DescriptionQuantum computing holds the potential to revolutionize numerous fields, yet the practical execution of quantum circuits depends on an efficient compilation process, including the placement of logical qubits on a quantum chip. This placement is a hard combinatorial problem that often defeats traditional heuristics and manual optimization. We present QAgent, a novel multi-agent reinforcement learning (RL) framework that autonomously optimizes logical qubit layouts on quantum processor. In QAgent, one agent is assigned to each logical qubit, and agents jointly learn placement policies that minimize circuit execution cost. To address the challenges of sparse rewards and credit assignment, we propose the Breakthrough Return Bonus (BRB), a dynamic reward shaping mechanism that encourages meaningful layout improvements and accelerates convergence. Extensive experiments on diverse quantum circuit benchmarks show that QAgent reduces execution costs by up to 53.9% compared to leading approaches, and significantly enhances circuit success rates. Ablation studies confirm that BRB is essential for stable training and effective policy optimization. These results demonstrate the promise of AI-driven, workload-aware placement for advancing fault-tolerant quantum computation.
Event Type
Research Manuscript
TimeTuesday, July 282:47pm - 3:00pm PDT
LocationMtg Room 101A