Provably trainable rotationally equivariant quantum machine learning
Exploiting the power of quantum computation to realize superior machine learning
algorithms has been a major research focus of recent years, but the prospects of quantum …
algorithms has been a major research focus of recent years, but the prospects of quantum …
Symmetry breaking in geometric quantum machine learning in the presence of noise
Geometric quantum machine learning based on equivariant quantum neural networks
(EQNNs) recently appeared as a promising direction in quantum machine learning. Despite …
(EQNNs) recently appeared as a promising direction in quantum machine learning. Despite …
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 …
Symmetry-invariant quantum machine learning force fields
Machine learning techniques are essential tools to compute efficient, yet accurate, force
fields for atomistic simulations. This approach has recently been extended to incorporate …
fields for atomistic simulations. This approach has recently been extended to incorporate …
Quantum Convolutional Neural Networks are (Effectively) Classically Simulable
Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising
model for Quantum Machine Learning (QML). In this work we tie their heuristic success to …
model for Quantum Machine Learning (QML). In this work we tie their heuristic success to …
Enforcing exact permutation and rotational symmetries in the application of quantum neural networks on point cloud datasets
Z Li, L Nagano, K Terashi - Physical Review Research, 2024 - APS
Recent developments in the field of quantum machine learning have promoted the idea of
incorporating physical symmetries in the structure of quantum circuits. A crucial milestone in …
incorporating physical symmetries in the structure of quantum circuits. A crucial milestone in …
ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
This paper presents a comparative analysis of the performance of Equivariant Quantum
Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their …
Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their …
Permutation-equivariant quantum convolutional neural networks
The Symmetric group $ S_ {n} $ manifests itself in large classes of quantum systems as the
invariance of certain characteristics of a quantum state with respect to permuting the qubits …
invariance of certain characteristics of a quantum state with respect to permuting the qubits …
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 …