# Variational Quantum Eigensolver with Reduced Circuit Complexity

@inproceedings{Zhang2021VariationalQE, title={Variational Quantum Eigensolver with Reduced Circuit Complexity}, author={Yu Zhang and Lukasz Cincio and Christian Francisco Andres Negre and Piotr Czarnik and Patrick J. Coles and Petr M. Anisimov and Susan M. Mniszewski and Sergei Tretiak and Pavel A. Dub}, year={2021} }

Yu Zhang, ∗ Lukasz Cincio, Christian F. A. Negre, Piotr Czarnik, Patrick Coles, Petr M. Anisimov, Susan M. Mniszewski, Sergei Tretiak, 4 and Pavel A. Dub Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA Accelerators and Electrodynamics Group, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA Center for Integrated…

## 4 Citations

A comprehensive study on how to construct local bases in deep variational quantum eigensolver for molecular systems

- Physics
- 2022

Luca Erhart, ∗ Kosuke Mitarai, 2, 3, † Wataru Mizukami, 2, 3, ‡ and Keisuke Fujii 2, 4, § Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan…

Accelerating variational quantum algorithms with multiple quantum processors

- Computer ScienceArXiv
- 2021

An efficient distributed optimization scheme, called QUDIO, that can be readily mixed with other advanced VQAs-based techniques to narrow the gap between the state of the art and applications with quantum advantage.

QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines

- Computer Science
- 2022

It is proved that the merging process in MaxCut can be further cast into a new MaxCut problem and thus be addressed by QAOAs or other MaxCut solvers, and it is proven that the approximation ratio of QAOA 2 is lower bounded by 1 / 2.

Improving the efficiency of learning-based error mitigation

- Computer Science
- 2022

An error mitigation scheme that builds on Cliﬀord data regression (CDR) is introduced that improves the frugality by carefully choosing the training data and exploiting the symmetries of the problem.

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