Politecnico di Torino - Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY

+39 011 090 6100 info@tech-share.it

AI based quantum compiler

Artificial intelligencebased-gate quantum computerInformatica Tsd Engquantum compilingquantum computerreinforcement learning

Introduction

Gate model quantum computers require specialized software, compilers, to translate quantum instructions to low-level instructions. The present invention relates to a computer implemented method for quantum compiling based on artificial intelligence.

Technical features

The architecture of circuital quantum computers requires computing layers devoted to compiling high-level quantum algorithms into lower-level circuits of quantum gates. Traditional approaches are time-consuming tasks, unsuitable to be employed in the mean of the quantum computation. By using artificial intelligence trained through multiple reinforcement learning iterations, the invention allows creating single-qubit operations with a minimum computational time. In particular, the method exploits a neural network that iteratively builds the desired quantum circuit by appending one logic gate at a time by choosing from a set of low-level logic gates available. At the end of the learning procedure, it is possible to recall the compilation strategy encoded in the network’s weights in a minimum time. The quantum compilation method allows a query time and calculation of the correct sequence which could in principle be exploited for real-time quantum compiling.

Possible Applications

  • Quantum computing, more precisely quantum compilation for quantum computing;
  • Renewable energies;
  • Cybersecurity;
  • Drug discovery;
  • Finance.

Advantages

  • Fast execution time;
  • Real-time quantum compiling;
  • Automatic learning;
  • It is possible to employ an arbitrary set of quantum logic gates;
  • Hardware agnostic.