Challenges and opportunities in quantum machine learning

M Cerezo, G Verdon, HY Huang, L Cincio… - Nature Computational …, 2022 - nature.com
At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …

Representation theory for geometric quantum machine learning

M Ragone, P Braccia, QT Nguyen, L Schatzki… - arXiv preprint arXiv …, 2022 - arxiv.org
Recent advances in classical machine learning have shown that creating models with
inductive biases encoding the symmetries of a problem can greatly improve performance …

Theory for equivariant quantum neural networks

QT Nguyen, L Schatzki, P Braccia, M Ragone, PJ Coles… - PRX Quantum, 2024 - APS
Quantum neural network architectures that have little to no inductive biases are known to
face trainability and generalization issues. Inspired by a similar problem, recent …

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 …

Introduction to Haar Measure Tools in Quantum Information: A Beginner's Tutorial

AA Mele - Quantum, 2024 - quantum-journal.org
The Haar measure plays a vital role in quantum information, but its study often requires a
deep understanding of representation theory, posing a challenge for beginners. This tutorial …

Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing

M Cerezo, M Larocca, D García-Martín, NL Diaz… - arXiv preprint arXiv …, 2023 - arxiv.org
A large amount of effort has recently been put into understanding the barren plateau
phenomenon. In this perspective article, we face the increasingly loud elephant in the room …

Exponential concentration in quantum kernel methods

S Thanasilp, S Wang, M Cerezo, Z Holmes - Nature Communications, 2024 - nature.com
Abstract Kernel methods in Quantum Machine Learning (QML) have recently gained
significant attention as a potential candidate for achieving a quantum advantage in data …

Exponential concentration and untrainability in quantum kernel methods

S Thanasilp, S Wang, M Cerezo, Z Holmes - arXiv preprint arXiv …, 2022 - arxiv.org
Kernel methods in Quantum Machine Learning (QML) have recently gained significant
attention as a potential candidate for achieving a quantum advantage in data analysis …

Understanding quantum machine learning also requires rethinking generalization

E Gil-Fuster, J Eisert, C Bravo-Prieto - Nature Communications, 2024 - nature.com
Quantum machine learning models have shown successful generalization performance
even when trained with few data. In this work, through systematic randomization …

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 …