Results 51 to 60 of about 137,793 (228)
Experimental demonstration of quantum learning speed-up with classical input data
, 2018 We consider quantum-classical hybrid machine learning in which large-scale input channels remain classical and small-scale working channels process quantum operations conditioned on classical input data. This does not require the conversion of classical (
Bang, Jeongho +6 more
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An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials
Advanced Engineering Materials, EarlyView.An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut +16 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
Variational Quantum Circuits for Deep Reinforcement Learning
IEEE Access, 2020 The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains.
Samuel Yen-Chi Chen +5 more
doaj +1 more source
Enhanced Quantum Synchronization via Quantum Machine Learning
, 2019 We study the quantum synchronization between a pair of two-level systems inside two coupled cavities. By using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum ...
Cárdenas-López, F. A. +3 more
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Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
2025 IEEE International Symposium on Circuits and Systems (ISCAS)In ...
Jun Qi 0002 +3 more
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Machine learning for quantum physics and quantum physics for machine learning
, 2023 Research at the intersection of machine learning (ML) and quantum physics is a recent growing field due to the enormous expectations and the success of both fields. ML is arguably one of the most promising technologies that has and will continue to disrupt many aspects of our lives.
openaire +4 more sources
Advanced Functional Materials, EarlyView.The layer‐by‐layer (LbL) assembly of coordination solids, enabled by the surface‐mounted metal‐organic framework (SURMOF) platform, is on the cusp of generating the organic counterpart of the epitaxy of inorganics. The programmable and sequential SURMOF protocol, optimized by machine learning (ML), is suited for accessing high‐quality thin films of ...
Zhengtao Xu +2 more
wiley +1 more source
Electroactive Metal–Organic Frameworks for Electrocatalysis
Advanced Functional Materials, EarlyView.Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska +7 more
wiley +1 more source
Taking advantage of noise in quantum reservoir computing
Scientific Reports, 2023 The biggest challenge that quantum computing and quantum machine learning are currently facing is the presence of noise in quantum devices. As a result, big efforts have been put into correcting or mitigating the induced errors. But, can these two fields
L. Domingo, G. Carlo, F. Borondo
doaj +1 more source