Quantum machine learning: from physics to software engineering
Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …
technology and artificial intelligence. This review provides a two-fold overview of several key …
Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation
Quantum computing is a game-changing technology for global academia, research centers
and industries including computational science, mathematics, finance, pharmaceutical …
and industries including computational science, mathematics, finance, pharmaceutical …
Hybrid quantum–classical generative adversarial networks for image generation via learning discrete distribution
It has been reported that quantum generative adversarial networks have a potential
exponential advantage over classical generative adversarial networks. However, quantum …
exponential advantage over classical generative adversarial networks. However, quantum …
Synergistic pretraining of parametrized quantum circuits via tensor networks
Parametrized quantum circuits (PQCs) represent a promising framework for using present-
day quantum hardware to solve diverse problems in materials science, quantum chemistry …
day quantum hardware to solve diverse problems in materials science, quantum chemistry …
Understanding quantum machine learning also requires rethinking generalization
Quantum machine learning models have shown successful generalization performance
even when trained with few data. In this work, through systematic randomization …
even when trained with few data. In this work, through systematic randomization …
Orbital-optimized pair-correlated electron simulations on trapped-ion quantum computers
Variational quantum eigensolvers (VQE) are among the most promising approaches for
solving electronic structure problems on near-term quantum computers. A critical challenge …
solving electronic structure problems on near-term quantum computers. A critical challenge …
Recent advances for quantum neural networks in generative learning
Quantum computers are next-generation devices that hold promise to perform calculations
beyond the reach of classical computers. A leading method towards achieving this goal is …
beyond the reach of classical computers. A leading method towards achieving this goal is …
Quantum machine learning for image classification
A Senokosov, A Sedykh, A Sagingalieva… - Machine Learning …, 2024 - iopscience.iop.org
Image classification, a pivotal task in multiple industries, faces computational challenges
due to the burgeoning volume of visual data. This research addresses these challenges by …
due to the burgeoning volume of visual data. This research addresses these challenges by …
Trainability barriers and opportunities in quantum generative modeling
Quantum generative models provide inherently efficient sampling strategies and thus show
promise for achieving an advantage using quantum hardware. In this work, we investigate …
promise for achieving an advantage using quantum hardware. In this work, we investigate …
Enhancing generative models via quantum correlations
Generative modeling using samples drawn from the probability distribution constitutes a
powerful approach for unsupervised machine learning. Quantum mechanical systems can …
powerful approach for unsupervised machine learning. Quantum mechanical systems can …