[HTML][HTML] Out-of-distribution generalization for learning quantum dynamics
Generalization bounds are a critical tool to assess the training data requirements of
Quantum Machine Learning (QML). Recent work has established guarantees for in …
Quantum Machine Learning (QML). Recent work has established guarantees for in …
Model-independent learning of quantum phases of matter with quantum convolutional neural networks
Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for
gapped quantum phases of matter. Here, we propose a model-independent protocol for …
gapped quantum phases of matter. Here, we propose a model-independent protocol for …
Quantum data learning for quantum simulations in high-energy physics
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 …
attention over the past years as an early application for the era of noisy quantum processors …
Counterdiabatic optimized driving in quantum phase sensitive models
State preparation plays a pivotal role in numerous quantum algorithms, including quantum
phase estimation. This paper extends and benchmarks counterdiabatic driving protocols …
phase estimation. This paper extends and benchmarks counterdiabatic driving protocols …
Approximately equivariant quantum neural network for p4m group symmetries in images
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 …
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
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 …
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
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 …
quantum criticality separating different phases of matter. Interacting spin systems in a …
Hybrid ground-state quantum algorithms based on neural Schrödinger forging
Entanglement forging based variational algorithms leverage the bipartition of quantum
systems for addressing ground-state problems. The primary limitation of these approaches …
systems for addressing ground-state problems. The primary limitation of these approaches …
[HTML][HTML] Importance sampling for stochastic quantum simulations
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 …
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 …
explored to solve problems in low‐energy nuclear physics. The use of quantum computing …