[HTML][HTML] The variational quantum eigensolver: a review of methods and best practices

J Tilly, H Chen, S Cao, D Picozzi, K Setia, Y Li, E Grant… - Physics Reports, 2022‏ - Elsevier
The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

Diagnosing barren plateaus with tools from quantum optimal control

M Larocca, P Czarnik, K Sharma, G Muraleedharan… - Quantum, 2022‏ - quantum-journal.org
Abstract Variational Quantum Algorithms (VQAs) have received considerable attention due
to their potential for achieving near-term quantum advantage. However, more work is …

Theoretical guarantees for permutation-equivariant quantum neural networks

L Schatzki, M Larocca, QT Nguyen, F Sauvage… - npj Quantum …, 2024‏ - nature.com
Despite the great promise of quantum machine learning models, there are several
challenges one must overcome before unlocking their full potential. For instance, models …

Equivalence of quantum barren plateaus to cost concentration and narrow gorges

A Arrasmith, Z Holmes, M Cerezo… - Quantum Science and …, 2022‏ - iopscience.iop.org
Optimizing parameterized quantum circuits (PQCs) is the leading approach to make use of
near-term quantum computers. However, very little is known about the cost function …

Avoiding barren plateaus via transferability of smooth solutions in a Hamiltonian variational ansatz

AA Mele, GB Mbeng, GE Santoro, M Collura, P Torta - Physical Review A, 2022‏ - APS
A large ongoing research effort focuses on variational quantum algorithms (VQAs),
representing leading candidates to achieve computational speed-ups on current quantum …

Lie-algebraic classical simulations for variational quantum computing

ML Goh, M Larocca, L Cincio, M Cerezo… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Classical simulation of quantum dynamics plays an important role in our understanding of
quantum complexity, and in the development of quantum technologies. Compared to other …

Trainability barriers and opportunities in quantum generative modeling

MS Rudolph, S Lerch, S Thanasilp, O Kiss… - npj Quantum …, 2024‏ - nature.com
Quantum generative models provide inherently efficient sampling strategies and thus show
promise for achieving an advantage using quantum hardware. In this work, we investigate …

Hyperparameter importance and optimization of quantum neural networks across small datasets

C Moussa, YJ Patel, V Dunjko, T Bäck, JN Van Rijn - Machine Learning, 2024‏ - Springer
As restricted quantum computers become available, research focuses on finding meaningful
applications. For example, in quantum machine learning, a special type of quantum circuit …

Unsupervised strategies for identifying optimal parameters in quantum approximate optimization algorithm

C Moussa, H Wang, T Bäck, V Dunjko - EPJ Quantum Technology, 2022‏ - epjqt.epj.org
As combinatorial optimization is one of the main quantum computing applications, many
methods based on parameterized quantum circuits are being developed. In general, a set of …

Optimized low-depth quantum circuits for molecular electronic structure using a separable-pair approximation

JS Kottmann, A Aspuru-Guzik - Physical Review A, 2022‏ - APS
We present a classically tractable model that leads to optimized low-depth quantum circuits
leveraging separable-pair approximations. The obtained circuits are well suited as a …