Quantum Compiling with Reinforcement Learning on a Superconducting Processor
This addresses the challenge of efficient quantum compilation for NISQ processors, which is crucial for advancing quantum computing, though it is incremental as it builds on existing RL and compilation techniques.
The researchers tackled the problem of implementing quantum algorithms on noisy intermediate-scale quantum (NISQ) processors by developing a reinforcement learning-based quantum compiler, achieving a compiled circuit for three-qubit quantum Fourier transformation with only seven CZ gates and unity circuit fidelity, and finding circuits shorter than conventional methods under device constraints.
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.