Speakers
Description
Quantum Approximate Optimization Algorithm (QAOA) simulations were performed on the Hybrilit quantum testbed at JINR. The testbed infrastructure is well suited for such research: significant computational resources are available, including multi-core CPUs, GPUs, and RAM; JupyterHub, as well as quantum simulators Cirq, Qiskit, qsim, and other are deployed. The problem of finding the ground state of the Ising model with a longitudinal magnetic field was solved for two- and three-dimensional lattices of various sizes. Quantum circuits with registers of up to 27 qubits were investigated. Optimization of variational ansatz parameters was carried out using both gradient-based and gradient-free methods. The processes and results of optimization performed by different methods were compared across several parameters. The dependence of computational efficiency on the configuration of used computational resources was demonstrated.
