Quantum compiling by deep reinforcement learning
Paris, M.G.A., Prati, E. · Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) · 2021
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 Solovay-Kitaev theorem guarantees the existence of such an approximating sequence.Though, the solutions to the quantum compiling problem suffer from a tradeoff between the length of the sequences, the precompilation time, and the execution time.Traditional approaches are time-consuming, unsuitable to be employed during computation.Here, we propose a deep reinforcement learning method as an alternative strategy, which requires a single precompilation procedure to learn a general strategy to approximate single-qubit unitaries.We show that this approach reduces the overall execution time, improving the tradeoff between the length of the sequence and execution time, potentially allowing real-time operations.