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Showing 1–3 of 3 results
Advanced filters: Author: Marcello Restelli Clear advanced filters

  • Quantum compilers are characterized by a trade-off between the length of the sequences, the precompilation time, and the execution time. Here, the authors propose an approach based on deep reinforcement learning to approximate unitary operators as circuits, and show that this approach decreases the execution time, potentially allowing real-time quantum compiling.

    • Lorenzo Moro
    • Matteo G. A. Paris
    • Enrico Prati
    ResearchOpen Access
    Communications Physics
    Volume: 4, P: 1-8

  • Many problems in physics do not have an exact solution method, so their resolution has been sometimes possible only by guessing test functions. The authors apply Deep Reinforcement Learning (DRL) to control coherent transport of quantum states in arrays of quantum dots and demonstrate that DRL can solve the control problem in the absence of a known analytical solution even under disturbance conditions.

    • Riccardo Porotti
    • Dario Tamascelli
    • Enrico Prati
    ResearchOpen Access
    Communications Physics
    Volume: 2, P: 1-9