Machine Learning-Guided Compilation for Reconfigurable Logical Quantum Architectures

Early fault-tolerant quantum computers will face a fundamental resource imbalance: limited physical qubits but a demand to execute increasingly large logical circuits. To make efficient use of these constrained resources, future processors will likely adopt densely packed and reconfigurable logical layouts. These conditions challenge conventional static mapping approaches that assume fixed logical qubit placements. We present a machine learning–guided Monte Carlo Tree Search (MCTS) compiler that adapts to these settings by exploring placement and routing decisions dynamically. Beyond improving compilation, this framework provides a means to explore how dense, reconfigurable logical architectures can best exploit the limited resources of early fault-tolerant quantum processors.