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Robot‐Assisted Measurement of the Critical Micelle Concentration
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.The study introduces (SIMO) smart integrator for manual operations, a robotic platform for precise, repeatable determination of (CMC) critical micelle concentration in surfactants. SIMO reduces standard deviation by 80% compared to manual methods. Surfactant, dye, and diluent selection, robotic protocols, and data handling are detailed.
Vincenzo Scamarcio +3 more
wiley +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone +11 more
wiley +1 more source
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Quantum Driven Machine Learning
International Journal of Theoretical Physics, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shivani Saini +3 more
openaire +1 more source
2021
In this chapter and the next one, we will explore the exciting areas of quantum machine learning and quantum deep learning. Machine learning and deep learning have seen great success in recent years because of the increase in the computational power at our disposal and because of the high-end research in these fields.
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In this chapter and the next one, we will explore the exciting areas of quantum machine learning and quantum deep learning. Machine learning and deep learning have seen great success in recent years because of the increase in the computational power at our disposal and because of the high-end research in these fields.
openaire +2 more sources
SOFT MEASUREMENTS AND COMPUTING, 2022
The article is devoted to the issues of quantum computing and algorithms aimed at implementing quantum machine learning both on separate quantum processors and in hybrid circuits using TPU and CPU.
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The article is devoted to the issues of quantum computing and algorithms aimed at implementing quantum machine learning both on separate quantum processors and in hybrid circuits using TPU and CPU.
openaire +2 more sources
Distributed secure quantum machine learning
Science Bulletin, 2017Distributed secure quantum machine learning (DSQML) enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server ...
Yu-Bo, Sheng, Lan, Zhou
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Machine learning and quantum physics
Science, 2017Many-Body Physics Elucidating the behavior of quantum interacting systems of many particles remains one of the biggest challenges in physics. Traditional numerical methods often work well, but some of the most interesting problems leave them stumped.
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Machine learning for quantum physics
Science, 2017An artificial neural network can discover the ground state of a quantum many-body ...
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Quantum guidelines for solid-state spin defects
Nature Reviews Materials, 2021Gary Wolfowicz +2 more
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