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;
• 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.