A Comparative Study on Solving Optimization Problems With Exponentially Fewer Qubits

A Comparative Study on Solving Optimization Problems With Exponentially Fewer Qubits

Blog Article

Variational quantum optimization algorithms, such as the variational quantum eigensolver (VQE) or the quantum approximate optimization algorithm (QAOA), are among the most studied quantum algorithms.In our work, we evaluate and improve an algorithm based on the VQE, which uses exponentially fewer qubits compared to Corn on the cob holder the QAOA.We highlight the numerical instabilities generated by encoding the problem into the Handbags variational ansatz and propose a classical optimization procedure to find the ground state of the ansatz in fewer iterations with a better or similar objective.In addition, we propose a method to embed the linear interpolation of the MaxCut problem on a quantum device.Furthermore, we compare classical optimizers for this variational ansatz on quadratic unconstrained binary optimization and graph partitioning problems.