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An invitation to distributed quantum neural networks [PDF]
, 2023 Deep neural networks have established themselves as one of the most promising machine learning techniques. Training such models at large scales is often parallelized, giving rise to the concept of distributed deep learning.
Pira, L +3 more
core +1 more source
New trends in quantum machine learning (a) [PDF]
Europhysics Letters, 2020 Abstract Here we will give a perspective on new possible interplays between machine learning and quantum physics, including also practical cases and applications. We will explore the ways in which machine learning could benefit from new quantum technologies and algorithms to find new ways to speed up their computations by ...
Buffoni L., Caruso F.
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Design and analysis of quantum machine learning: a survey
Connection ScienceMachine learning has demonstrated tremendous potential in solving real-world problems. However, with the exponential growth of data amount and the increase of model complexity, the processing efficiency of machine learning declines rapidly.
Linshu Chen +6 more
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Quantum dynamics of machine learning
Acta Physica SinicaIn order to solve the current lack of rigorous theoretical models in the machine learning process, in this paper the iterative motion process of machine learning is modeled by using quantum dynamic method based on the principles of first-principles thinking.
Peng Wang +1 more
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Emerging experimental and computational methods for studying redox‐regulated structural transitions
FEBS Letters, EarlyView.Redox reactions can reshape proteins and alter how they behave in cells, with important consequences for health and disease. This review explores emerging experimental and computational approaches for discovering these redox‐sensitive protein switches, revealing their structural effects, and predicting their behavior, opening new opportunities to ...
Tasneem Rass +2 more
wiley +1 more source
Advances in quantum machine learning
, 2015 Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion.
Adcock, Jeremy +9 more
openaire +3 more sources
Quantum Embedding Search for Quantum Machine Learning
IEEE Access, 2022 Quantum Embedding Search for Quantum Machine ...
Nam Nguyen 0003, Kwang-Cheng Chen
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Advanced Functional Materials, EarlyView.Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner +14 more
wiley +1 more source
New Journal of PhysicsThe standard model of quantum computation is based on quantum circuits, where the number and quality of the quantum gates composing the circuit influence the runtime and fidelity of the computation. The fidelity of the decomposition of quantum algorithms,
Aritra Sarkar +7 more
doaj +1 more source
Experimental quantum end-to-end learning on a superconducting processor
npj Quantum Information, 2023 Machine learning can be enhanced by a quantum computer via its inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediate-scale quantum devices, it was proposed that the learning model can be designed ...
Xiaoxuan Pan +12 more
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