Quantum Compiling by Deep Reinforcement Learning

Abstract

The architecture of circuital quantum computers requires computing layers devoted to compiling high-level quantum algorithms into lower-level circuits of quantum gates. The general problem of quantum compiling is to approximate any unitary transformation that describes the quantum computation, as a sequence of elements selected from a finite base of universal quantum gates. The existence of an approximating sequence of one qubit quantum gates is guaranteed by the Solovay-Kitaev theorem, which implies sub-optimal algorithms to establish it explicitly. Since a unitary transformation may require significantly different gate sequences, depending on the base considered, such a problem is of great complexity and does not admit an efficient approximating algorithm. Therefore, traditional approaches are time-consuming tasks, unsuitable to be employed during quantum computation. We exploit the deep reinforcement learning method as an alternative strategy, which has a significantly different trade-off between search time and exploitation time. Deep reinforcement learning allows creating single-qubit operations in real time, after an arbitrary long training period during which a strategy for creating sequences to approximate unitary operators is built. The deep reinforcement learning based compiling method allows for fast computation times, which could in principle be exploited for real-time quantum compiling.

arXiv comment11 pages, 4 figures

References in corpus (23)

• Elementary gates for quantum computation
• Machine learning and the physical sciences
• Demonstration of a small programmable quantum computer with atomic qubits
• Experimental Comparison of Two Quantum Computing Architectures
• Optimal Quantum Circuits for General Two-Qubit Gates
• Hindsight Experience Replay
• Active learning machine learns to create new quantum experiments
• Flow Navigation by Smart Microswimmers via Reinforcement Learning
• Machine learning meets quantum physics
• Optimizing Quantum Error Correction Codes with Reinforcement Learning
• Efficient Discrete Approximations of Quantum Gates
• Deep Reinforcement Learning for Quantum Gate Control
• Basic circuit compilation techniques for an ion-trap quantum machine
• Topological Quantum Compiling with Reinforcement Learning
• Quantum error correction for the toric code using deep reinforcement learning
• Coherent Transport of Quantum States by Deep Reinforcement Learning
• Direct dialling of Haar random unitary matrices
• Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
• Measurement-based adaptation protocol with quantum reinforcement learning
• Transport quantum logic gates for trapped ions
• Coherent Tunneling by Adiabatic Passage of an exchange-only spin qubit in a double quantum dot chain
• On Clifford groups in quantum computing
• Quantum compiling with diffusive sets of gates