Pulse-based variational quantum optimization and metalearning in superconducting circuits

TL;DR

This paper introduces a pulse-based variational quantum optimization framework for superconducting circuits, utilizing analog control of quantum states and meta-learning to enhance performance over traditional gate-based methods.

Contribution

It presents a novel hardware-level variational algorithm that optimizes pulse parameters directly, and combines it with meta-learning to improve initial parameter selection.

Findings

Pulse-based optimization effectively drives quantum states to target solutions.

Meta-learning enhances the initial parameter choice, improving convergence.

The approach outperforms conventional gate-based variational algorithms.

Abstract

Solving optimization problems using variational algorithms stands out as a crucial application for noisy intermediate-scale devices. Instead of constructing gate-based quantum computers, our focus centers on designing variational quantum algorithms within the analog paradigm. This involves optimizing parameters that directly control pulses, driving quantum states towards target states without the necessity of compiling a quantum circuit. In this work, we introduce pulse-based variational quantum optimization (PBVQO) as a hardware-level framework. We illustrate the framework by optimizing external fluxes on superconducting quantum interference devices, effectively driving the wave function of this specific quantum architecture to the ground state of an encoded problem Hamiltonian. Given that the performance of variational algorithms heavily relies on appropriate initial parameters, we introduce a global optimizer as a meta-learning technique to tackle a simple problem. The synergy between PBVQO and meta-learning provides an advantage over conventional gate-based variational algorithms.