Theory for equivariant quantum neural networks
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 …
face trainability and generalization issues. Inspired by a similar problem, recent …
On the universality of sn-equivariant k-body gates
On the universality of Sn -equivariant k-body gates - IOPscience Skip to content IOP
Science home Accessibility Help Search Journals Journals list Browse more than 100 …
Science home Accessibility Help Search Journals Journals list Browse more than 100 …
Non-Unitary Quantum Machine Learning
We introduce several novel probabilistic quantum algorithms that overcome the normal
unitary restrictions in quantum machine learning by leveraging the Linear Combination of …
unitary restrictions in quantum machine learning by leveraging the Linear Combination of …
Equivariant Variational Quantum Eigensolver to detect Phase Transitions through Energy Level Crossings
G Crognaletti, G Di Bartolomeo, M Vischi… - arXiv preprint arXiv …, 2024 - arxiv.org
Level spectroscopy stands as a powerful method for identifying the transition point that
delineates distinct quantum phases. Since each quantum phase exhibits a characteristic …
delineates distinct quantum phases. Since each quantum phase exhibits a characteristic …
Image Classification with Rotation-Invariant Variational Quantum Circuits
PS Sebastian, M Cañizo, R Orús - arXiv preprint arXiv:2403.15031, 2024 - arxiv.org
Variational quantum algorithms are gaining attention as an early application of Noisy
Intermediate-Scale Quantum (NISQ) devices. One of the main problems of variational …
Intermediate-Scale Quantum (NISQ) devices. One of the main problems of variational …
Can Geometric Quantum Machine Learning Lead to Advantage in Barcode Classification?
We consider the problem of distinguishing two vectors (visualized as images or barcodes)
and learning if they are related to one another. For this, we develop a geometric quantum …
and learning if they are related to one another. For this, we develop a geometric quantum …
Feynman's “Simulating Physics with Computers”
This invited essay belongs to a series considering highly influential articles published by the
International Journal of Theoretical Physics. In this paper, we highlight the physical content …
International Journal of Theoretical Physics. In this paper, we highlight the physical content …
Estimates of loss function concentration in noisy parametrized quantum circuits
Variational quantum computing provides a versatile computational approach, applicable to a
wide range of fields such as quantum chemistry, machine learning, and optimization …
wide range of fields such as quantum chemistry, machine learning, and optimization …