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.
Bilkis, M., et al. "A semi-agnostic ansatz with variable structure for variational quantum algorithms." Quantum Machine Intelligence, vol. 5, no. 2, Nov. 2023. https://doi.org/10.1007/s42484-023-00132-1
Bilkis, M., Cerezo de la Roca, Marco Vinicio Sebastian, Verdon, Guillaume, Coles, Patrick Joseph, & Cincio, Lukasz (2023). A semi-agnostic ansatz with variable structure for variational quantum algorithms. Quantum Machine Intelligence, 5(2). https://doi.org/10.1007/s42484-023-00132-1
Bilkis, M., Cerezo de la Roca, Marco Vinicio Sebastian, Verdon, Guillaume, et al., "A semi-agnostic ansatz with variable structure for variational quantum algorithms," Quantum Machine Intelligence 5, no. 2 (2023), https://doi.org/10.1007/s42484-023-00132-1
@article{osti_2467400,
author = {Bilkis, M. and Cerezo de la Roca, Marco Vinicio Sebastian and Verdon, Guillaume and Coles, Patrick Joseph and Cincio, Lukasz},
title = {A semi-agnostic ansatz with variable structure for variational quantum algorithms},
annote = {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.},
doi = {10.1007/s42484-023-00132-1},
url = {https://www.osti.gov/biblio/2467400},
journal = {Quantum Machine Intelligence},
issn = {ISSN 2524-4906},
number = {2},
volume = {5},
place = {United States},
publisher = {Springer Nature},
year = {2023},
month = {11}}
Murali, Prakash; Mckay, David C.; Martonosi, Margaret
ASPLOS '20: Architectural Support for Programming Languages and Operating Systems, Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systemshttps://doi.org/10.1145/3373376.3378477