[HTML][HTML] Out-of-distribution generalization for learning quantum dynamics

MC Caro, HY Huang, N Ezzell, J Gibbs… - Nature …, 2023 - nature.com
Generalization bounds are a critical tool to assess the training data requirements of
Quantum Machine Learning (QML). Recent work has established guarantees for in …

Model-independent learning of quantum phases of matter with quantum convolutional neural networks

YJ Liu, A Smith, M Knap, F Pollmann - Physical Review Letters, 2023 - APS
Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for
gapped quantum phases of matter. Here, we propose a model-independent protocol for …

Quantum data learning for quantum simulations in high-energy physics

L Nagano, A Miessen, T Onodera, I Tavernelli… - Physical Review …, 2023 - APS
Quantum machine learning with parametrised quantum circuits has attracted significant
attention over the past years as an early application for the era of noisy quantum processors …

Counterdiabatic optimized driving in quantum phase sensitive models

FP Barone, O Kiss, M Grossi, S Vallecorsa… - New Journal of …, 2024 - iopscience.iop.org
State preparation plays a pivotal role in numerous quantum algorithms, including quantum
phase estimation. This paper extends and benchmarks counterdiabatic driving protocols …

Approximately equivariant quantum neural network for p4m group symmetries in images

SY Chang, M Grossi, B Le Saux… - 2023 IEEE International …, 2023 - ieeexplore.ieee.org
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which
can be efficiently simulated with a low depth on near-term quantum hardware in the …

Quantum Computing for High-Energy Physics: State of the Art and Challenges

A Di Meglio, K Jansen, I Tavernelli, C Alexandrou… - PRX Quantum, 2024 - APS
Quantum computers offer an intriguing path for a paradigmatic change of computing in the
natural sciences and beyond, with the potential for achieving a so-called quantum …

Finite-size criticality in fully connected spin models on superconducting quantum hardware

M Grossi, O Kiss, F De Luca, C Zollo, I Gremese… - Physical Review E, 2023 - APS
The emergence of a collective behavior in a many-body system is responsible for the
quantum criticality separating different phases of matter. Interacting spin systems in a …

Hybrid ground-state quantum algorithms based on neural Schrödinger forging

P de Schoulepnikoff, O Kiss, S Vallecorsa, G Carleo… - Physical Review …, 2024 - APS
Entanglement forging based variational algorithms leverage the bipartition of quantum
systems for addressing ground-state problems. The primary limitation of these approaches …

[HTML][HTML] Importance sampling for stochastic quantum simulations

O Kiss, M Grossi, A Roggero - Quantum, 2023 - quantum-journal.org
Simulating many-body quantum systems is a promising task for quantum computers.
However, the depth of most algorithms, such as product formulas, scales with the number of …

Nuclear physics in the era of quantum computing and quantum machine learning

JE García‐Ramos, Á Sáiz, JM Arias… - Advanced Quantum …, 2024 - Wiley Online Library
In this paper, the application of quantum simulations and quantum machine learning is
explored to solve problems in low‐energy nuclear physics. The use of quantum computing …