A semi-agnostic ansatz with variable structure for variational quantum algorithms
Bilkis, Matias (Universitat Autònoma de Barcelona. Departament de Física)
Cerezo, Marco (Los Alamos National Laboratory. Center for Nonlinear Studies)
Verdon, Guillaume (University of Waterloo. Institute for Quantum Computing)
Coles, Patrick (Los Alamos National Laboratory. Theoretical Division)
Cincio, Lukasz (Los Alamos National Laboratory. Theoretical Division)

Data:	2023
Resum:	Quantum machine learning-and specifically Variational Quantum Algorithms (VQAs)-offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.
Ajuts:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1127 Agencia Estatal de Investigación PID2019-107609GB-I00
Nota:	Altres ajuts: acords transformatius de la UAB
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Quantum machine learning ; Variational quantum algorithms ; Quantum circuit discovery
Publicat a:	Quantum machine intelligence, Vol. 5, Issue 2 (December 2023) , art. 43, ISSN 2524-4914

DOI: 10.1007/s42484-023-00132-1

22 p, 2.1 MB

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