Abstract
Variational quantum algorithms (VQAs) are widely speculated to deliver quantum advantages for practical problems under the quantum–classical hybrid computational paradigm in the near term. Both theoretical and practical developments of VQAs share many similarities with those of deep learning. For instance, a key component of VQAs is the design of task-dependent parameterized quantum circuits (PQCs) as in the case of designing a good neural architecture in deep learning. Partly inspired by the recent success of AutoML and neural architecture search (NAS), quantum architecture search (QAS) is a collection of methods devised to engineer an optimal task-specific PQC. It has been proven that QAS-designed VQAs can outperform expert-crafted VQAs in various scenarios. In this work, we propose to use a neural network based predictor as the evaluation policy for QAS. We demonstrate a neural predictor guided QAS can discover powerful quantum circuit ansatz, yielding state-of-the-art results for various examples from quantum simulation and quantum machine learning. Notably, neural predictor guided QAS provides a better solution than that by the random-search baseline while using an order of magnitude less of circuit evaluations. Moreover, the predictor for QAS as well as the optimal ansatz found by QAS can both be transferred and generalized to address similar problems.
Highlights
Variational quantum algorithms [1, 2] are readily accessible in the noisy intermediate scale quantum (NISQ) era [3] and show some promising signs for practical quantum advantage in the near future
Inspired by neural architecture search (NAS) from the AutoML community [35–38], we introduced the concept of quantum architecture search (QAS), which refers to a collection of effective methods that systematically search for an optimal quantum circuit ansatz for a given problem in Ref. [39]
We present the main results of neural predictor based QAS on two specific Variational quantum algorithms (VQAs) tasks: 1. supervised learning task for binary classification on the fashion-MNIST dataset and 2. quantum simulation to estimate the ground state energy of the transverse field Ising model (TFIM)
Summary
Variational quantum algorithms [1, 2] (with a relatively low consumption on quantum resources) are readily accessible in the noisy intermediate scale quantum (NISQ) era [3] and show some promising signs for practical quantum advantage in the near future. Some prominent examples of VQA include variational quantum eigensolvers (VQE) [4–9], quantum approximation optimization algorithms (QAOA) [10–14] and variational quantum machine learning (QML) [15–27]. While QAS is an elegant idea, it faces an important challenge of exploring and evaluating many quantum ansatzes during the training This computational bottleneck is intrinsic to all design-by-search methodologies including NAS in deep learning. The first one is weights sharing, where trainable parameters are reused instead of standalone training for each ansatz Such weights sharing policy is utilized in one-shot search [52– 54] and DARTS [55] in NAS, and the same idea has been exploited in the corresponding QAS frameworks, quantum circuit architecture search [56] and differentiable quantum architecture search [39], respectively. 2. We demonstrate that the proposed neural predictor based QAS performs competitively in some VQE and QML tasks as it efficiently discovers state-ofthe-art quantum architectures. We develop a sophisticated transfer protocol incorporating beam search and evolutionary techniques to achieve this
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
