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Quantum Compiling with Reinforcement Learning on a Superconducting Processor
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Quantum Compiling with Reinforcement Learning on a Superconducting Processor
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To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology. NISQ processors feature tens to a few hundreds of noisy qubits with limited coherence times and gate operations with errors, so NISQ algorithms naturally require employing circuits of short lengths via quantum compilation. Here, we develop a reinforcement learning (RL)-based quantum compiler for a superconducting processor and demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. We show that for the three-qubit quantum Fourier transformation, a compiled circuit using only seven CZ gates with unity circuit fidelity can be achieved. The compiler is also able to find optimal circuits under device topological constraints, with lengths considerably shorter than those by the conventional method. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation, offering valuable insights for the advancement of RL-based compilers.
Forward citations
Cited by 4 Pith papers
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Replay-buffer engineering for noise-robust quantum circuit optimization
Treating the replay buffer as a central lever in RL for quantum circuit optimization yields 4-32x sample efficiency gains, up to 67.5% faster episodes, and 85-90% fewer steps to accuracy on noisy molecular and compila...
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Real-time adaptive quantum error correction by model-free multi-agent learning
Adaptive quantum error correction: multi-agent RL discovers QEC circuits offline; a bandit-controlled variational layer retrains online, cutting logical infidelity about 18x (qubit) and 3x (qutrit) under drifting bit/...
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AlphaCNOT: Learning CNOT Minimization with Model-Based Planning
AlphaCNOT combines reinforcement learning with Monte Carlo Tree Search planning to reduce CNOT gate counts by up to 32% versus heuristics in quantum circuit synthesis.
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ZAP: Zoned Architecture and Performant Compiler for Field Programmable Atom Array
ZAP presents a zoned architecture and single-pass compiler for field-programmable atom arrays that delivers over 1000x faster compilation than prior tools while preserving or improving circuit fidelity on structured a...
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