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About this paper
Mohammad Abrarul Hasanat, Jason Ludmir, Tirthak Patel, Rohan Basu Roy
TuniQ introduces a reinforcement learning framework that adaptively selects quantum compilation passes to simultaneously optimize output fidelity and classical compilation time. This system achieved an average 20% improvement in Total Variation Distance and a 34% reduction in classical compilation time compared to Qiskit's fidelity-optimized baseline.
Key insights
- Adaptive, context-aware selection of compilation passes, accounting for circuit structure, hardware topology, and real-time noise, is crucial for jointly optimizing quantum circuit fidelity and classical compilation efficiency.
- Reinforcement learning, with a carefully designed state space and reward function, can effectively learn complex, cross-stage interdependencies in quantum compilation, leading to superior global outcomes compared to fixed sequences or local optimizations.
- By selectively applying only beneficial passes and skipping redundant ones, compilation time can be significantly reduced without compromising, and often enhancing, output fidelity.
Summary by alphaXiv.
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