Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach

Understand

Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems using parameterized quantum circuits (PQCs).

• The design of these circuits influences the ability of the algorithm to efficiently explore the solution space and converge to more optimal solutions.
• Choosing an appropriate circuit topology, gate set, and parameterization scheme is determinant to achieve good performance.
• In addition, it is not only problem-dependent, but the quantum hardware used also has a significant impact on the results.