Quantum circuit synthesis of Bell and GHZ states using projective simulation in the NISQ era
This work addresses the challenge of minimizing quantum algorithm costs for researchers in the NISQ era, though it is incremental as it applies an existing reinforcement learning method to a specific quantum synthesis task.
The study tackled the problem of quantum circuit synthesis for generating GHZ states on noisy, limited-qubit quantum computers by using Projective Simulation, a reinforcement learning technique, and found that the agent performed well but its learning capacity decreased as qubit count increased to up to 5 qubits.
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.