Large-scale quantum reservoir learning with an analog quantum computer
Quantum machine learning has gained considerable attention as quantum technology
advances, presenting a promising approach for efficiently learning complex data patterns …
advances, presenting a promising approach for efficiently learning complex data patterns …
Digital-analog quantum genetic algorithm using Rydberg-atom arrays
Digital-analog quantum computing (DAQC) combines digital gates with analog operations,
offering an alternative paradigm for universal quantum computation. This approach …
offering an alternative paradigm for universal quantum computation. This approach …
Uncovering Emergent Spacetime Supersymmetry with Rydberg Atom Arrays
In the zoo of emergent symmetries in quantum many-body physics, the previously
unrealized emergent spacetime supersymmetry (SUSY) is particularly intriguing. Although it …
unrealized emergent spacetime supersymmetry (SUSY) is particularly intriguing. Although it …
Demonstration of robust and efficient quantum property learning with shallow shadows
Extracting information efficiently from quantum systems is a major component of quantum
information processing tasks. Randomized measurements, or classical shadows, enable …
information processing tasks. Randomized measurements, or classical shadows, enable …
Digital-analog quantum convolutional neural networks for image classification
We propose digital-analog quantum kernels for enhancing the detection of complex features
in the classification of images. We consider multipartite-entangled analog blocks, stemming …
in the classification of images. We consider multipartite-entangled analog blocks, stemming …
Generation of quantum phases of matter and finding a maximum-weight independent set of unit-disk graphs using Rydberg atoms
Recent progress in quantum computing and quantum simulation of many-body systems with
arrays of neutral atoms using Rydberg excitation has provided unforeseen opportunities …
arrays of neutral atoms using Rydberg excitation has provided unforeseen opportunities …
Classification of the Fashion-MNIST Dataset on a Quantum Computer
The potential impact of quantum machine learning algorithms on industrial applications
remains an exciting open question. Conventional methods for encoding classical data into …
remains an exciting open question. Conventional methods for encoding classical data into …
Solving Power Grid Optimization Problems with Rydberg Atoms
The rapid development of neutral atom quantum hardware provides a unique opportunity to
design hardware-centered algorithms for solving real-world problems aimed at establishing …
design hardware-centered algorithms for solving real-world problems aimed at establishing …
Digital-Analog Quantum Machine Learning
L Lamata - arxiv preprint arxiv:2411.10744, 2024 - arxiv.org
Machine Learning algorithms are extensively used in an increasing number of systems,
applications, technologies, and products, both in industry and in society as a whole. They …
applications, technologies, and products, both in industry and in society as a whole. They …
Operator Learning Renormalization Group
In this paper, we present a general framework for quantum many-body simulations called
the operator learning renormalization group (OLRG). Inspired by machine learning …
the operator learning renormalization group (OLRG). Inspired by machine learning …