Quantum circuit synthesis of Bell and GHZ states using projective simulation in the NISQ era

TL;DR

This paper explores using reinforcement learning, specifically Projective Simulation, to synthesize quantum circuits for Bell and GHZ states on NISQ devices, aiming to reduce circuit complexity and improve noise resilience.

Contribution

It demonstrates the viability of applying Projective Simulation reinforcement learning to quantum circuit synthesis for small-scale NISQ devices, highlighting its potential and limitations.

Findings

The RL agent successfully synthesized GHZ states on 5-qubit IBM quantum hardware.

Performance decreased as the number of qubits increased.

The approach offers a promising direction for noise-aware quantum circuit design.

Abstract

Quantum Computing has been evolving in the last years. Although nowadays quantum algorithms performance has shown superior to their classical counterparts, quantum decoherence and additional auxiliary qubits needed for error tolerance routines have been huge barriers for quantum algorithms efficient use. These restrictions lead us to search for ways to minimize algorithms costs, i.e the number of quantum logical gates and the depth of the circuit. For this, quantum circuit synthesis and quantum circuit optimization techniques are explored. We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis for noise quantum computers with limited number of qubits. The agent had the task of creating quantum circuits up to 5 qubits to generate GHZ states in the IBM Tenerife (IBM QX4) quantum processor. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased.