Results 71 to 80 of about 8,042,432 (193)

Quantum Machine Learning Playground

IEEE Computer Graphics and Applications
Accepted to IEEE Computer Graphics and Applications.
Pascal Debus, Sebastian Issel, Kilian Tscharke   +2 more
openaire   +4 more sources

Quantum circuit complexity and unsupervised machine learning of topological order

Nature Communications
Enabling the discovery of unknown quantum many-body phases of matter remains a fundamental challenge in machine learning for quantum physics. Here, inspired by the close relationship between Kolmogorov complexity and unsupervised machine learning, we ...
Yanming Che, Clemens Gneiting, Xiaoguang Wang, Franco Nori   +3 more
doaj   +1 more source

The need for open source software in machine learning [PDF]

, 2007
Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse ...
Ratsch, Gunnar, Scholkopf, Bernhard, Vincent, P., LeCun, Y, Ong, C., Braun, M.L., Rasmussen, Carl Edward, Rasmussen, CE, Rätsch, G., Bengio, S., Müller, K.-R., Müller, KR, LeCunn, Yann, Muller, Klaus-Robert, Vincent , P., Pereira, Fernando, Vincent, Pascal, Bottou, Leon, Pereira, F, ,, Braun, ML, Ong, CS, Rasmussen, C.E., Smola, A, Sonnenburg, S., Holmes, G, Bengio, S, Weston, Jason, Williamson, RC, Sonnenburg, S, Williamson, Robert C., Bottou, L., Holmes, G., Ong, Cheng Soon, Pereira, F., Braun, M., Sonnenburg, Soren, Smola, Alexander, Williamson, R.C., Holmes , G., Schölkopf, B, Holmes, Geoffrey, Braun, Mikio L., Bengio, Samy, Schölkopf, B., Rasmussen, C., Williamson, R., Rätch, G, Weston, J, Smola, A., Ong, C.S., Müller, K., Bottou, L, Weston, J., Vincent, P, LeCun, Y.   +55 more
core  

A Primer on Quantum Machine Learning

CoRR
Quantum machine learning (QML) is a computational paradigm that seeks to apply quantum-mechanical resources to solve learning problems. As such, the goal of this framework is to leverage quantum processors to tackle optimization, supervised, unsupervised and reinforcement learning, and generative modeling-among other tasks-more efficiently than ...
Su Yeon Chang, M. Cerezo
openaire   +2 more sources

Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets

IEEE Access
The Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called “Pegasus.” This groundbreaking system opens new possibilities for solving highly complex problems.
Hadi Salloum, Kamil Sabbagh, Vladislav Savchuk, Ruslan Lukin, Osama Orabi, Marat Isangulov, Manuel Mazzara   +6 more
doaj   +1 more source

On a quantum inspired approach to train machine learning models

Applied AI Letters, 2023
In this work, a novel technique to train machine learning models is introduced, which is based on digital simulations of certain types of quantum systems.
Jean Michel Sellier
doaj   +1 more source

Deep learning for quantum sciences: Selected topics

This chapter discusses more specialized examples on how machine learning can be used to solve problems in quantum sciences. We start by explaining the concept of differentiable programming and its use cases in quantum sciences.
Carleo, Giuseppe, Stornati, Paolo, Tomza, Michał, Carrasquilla, Juan, Krems, Roman, Arnold, Julian, Płodzień, Marcin, Dauphin, Alexandre, Okuła, Robert, Koch, Rouven, van Nieuwenburg, Evert, Büttner, Miriam, Gabrié, Marylou, Dunjko, Vedran, Requena, Borja, Dawid, Anna, Muñoz-Gil, Gorka, Gresch, Alexander, Vicentini, Filippo, Marquardt, Florian, Lewenstein, Maciej, Greplová, Eliška, Donatella, Kaelan, Cervera-Lierta, Alba, Wang, Lei, Nicoli, Kim A., Wetzel, Sebastian J., Vargas-Hernández, Rodrigo A.   +27 more
core   +1 more source

Learning curves for decision making in supervised machine learning: a survey

Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e.g., the number of training examples or the number of ...
van Rijn J.N., Mohr F.
core   +1 more source

Quantum tangent kernel

Physical Review Research
The quantum kernel method is one of the key approaches to quantum machine learning, which has the advantage of not requiring optimization and its theoretical simplicity.
Norihito Shirai, Kenji Kubo, Kosuke Mitarai, Keisuke Fujii   +3 more
doaj   +1 more source

Potential and limitations of random Fourier features for dequantizing quantum machine learning [PDF]

Quantum
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where {parameterized quantum circuits} (PQCs) are used as learning models.
Ryan Sweke, Erik Recio-Armengol, Sofiene Jerbi, Elies Gil-Fuster, Bryce Fuller, Jens Eisert, Johannes Jakob Meyer   +6 more
doaj   +1 more source