no code implementations • 5 Feb 2024 • Yash J. Patel, Akash Kundu, Mateusz Ostaszewski, Xavier Bonet-Monroig, Vedran Dunjko, Onur Danaci
In the case of parameter optimization alone, noise effects have been observed to dramatically influence the performance of the optimizer and final outcomes, which is a key line of study.
1 code implementation • 19 Jun 2023 • Akash Kundu, Przemysław Bedełek, Mateusz Ostaszewski, Onur Danaci, Yash J. Patel, Vedran Dunjko, Jarosław A. Miszczak
We demonstrate that the circuits proposed by the reinforcement learning methods are shallower than the standard variational quantum state diagonalization algorithm and thus can be used in situations where hardware capabilities limit the depth of quantum circuits.
1 code implementation • 13 Jul 2022 • Yash J. Patel, Sofiene Jerbi, Thomas Bäck, Vedran Dunjko
Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems.