Topological Quantum Compiling with Reinforcement Learning
This addresses the challenge of efficient quantum compiling for quantum computing, offering a generally applicable method that is incremental in applying reinforcement learning to a specific quantum problem.
The paper tackles the problem of quantum compiling by introducing a deep reinforcement learning algorithm that compiles arbitrary single-qubit gates into near-optimal sequences of elementary gates, achieving high accuracy without ancillary qubits, as demonstrated with Fibonacci anyons.
Quantum compiling, a process that decomposes the quantum algorithm into a series of hardware-compatible commands or elementary gates, is of fundamental importance for quantum computing. We introduce an efficient algorithm based on deep reinforcement learning that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set. It generates near-optimal gate sequences with given accuracy and is generally applicable to various scenarios, independent of the hardware-feasible universal set and free from using ancillary qubits. For concreteness, we apply this algorithm to the case of topological compiling of Fibonacci anyons and obtain near-optimal braiding sequences for arbitrary single-qubit unitaries. Our algorithm may carry over to other challenging quantum discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum physics.