Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms

TL;DR

Q-MAML introduces a meta-learning framework for variational quantum algorithms that improves parameter initialization, enabling faster convergence and better generalization across different quantum optimization problems in the NISQ era.

Contribution

It applies a classical meta-learning approach to quantum algorithms, enhancing initial parameter selection for variational quantum algorithms to improve efficiency and adaptability.

Findings

Learner estimates initial parameters that generalize across problems.

Framework achieves faster convergence with fewer quantum circuit updates.

Effective across various Hamiltonian optimization tasks.

Abstract

In the Noisy Intermediate-Scale Quantum (NISQ) era, using variational quantum algorithms (VQAs) to solve optimization problems has become a key application. However, these algorithms face significant challenges, such as choosing an effective initial set of parameters and the limited quantum processing time that restricts the number of optimization iterations. In this study, we introduce a new framework for optimizing parameterized quantum circuits (PQCs) that employs a classical optimizer, inspired by Model-Agnostic Meta-Learning (MAML) technique. This approach aim to achieve better parameter initialization that ensures fast convergence. Our framework features a classical neural network, called Learner}, which interacts with a PQC using the output of Learner as an initial parameter. During the pre-training phase, Learner is trained with a meta-objective based on the quantum circuit cost function. In the adaptation phase, the framework requires only a few PQC updates to converge to a more accurate value, while the learner remains unchanged. This method is highly adaptable and is effectively extended to various Hamiltonian optimization problems. We validate our approach through experiments, including distribution function mapping and optimization of the Heisenberg XYZ Hamiltonian. The result implies that the Learner successfully estimates initial parameters that generalize across the problem space, enabling fast adaptation.