Results 81 to 90 of about 154,173 (259)
, 2023 Physical learning machines, be they classical or quantum, are necessarily dissipative systems. The rate of energy dissipation decreases as the learning error rate decreases linking thermodynamic efficiency and learning efficiency. In the classical case the energy is dissipated as heat.
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Quantum Chemistry Meets Machine Learning
CHIMIA, 2019 In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous ...
Fabrizio, Alberto +3 more
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Advanced Functional Materials, EarlyView.A pixelation‐free, monolithic iontronic pressure sensor enables simultaneous pressure and position sensing over large areas. AC‐driven ion release generates spatially varying impedance pathways depending on the pressure. Machine learning algorithms effectively decouple overlapping pressure–position signals from the multichannel outputs, achieving high ...
Juhui Kim +10 more
wiley +1 more source
Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets
IEEE AccessThe 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 +6 more
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Potential and limitations of random Fourier features for dequantizing quantum machine learning [PDF]
QuantumQuantum 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 +6 more
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QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
, 2018 Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery to advancing cybersecurity.
Kalantre, Sandesh S. +4 more
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Accelerated Discovery of High Performance Ni3S4/Ni3Mo HER Catalysts via Bayesian Optimization
Advanced Functional Materials, EarlyView.Integrated workflow accelerates the catalyst discovery of hydrogen evolution reaction via Bayesian optimization. An experiment‐trained surrogate model proposes synthesis conditions, guiding iterative refinement using electrochemical performance metrics.
Namuersaihan Namuersaihan +9 more
wiley +1 more source
Representation Learning via Quantum Neural Tangent Kernels
PRX Quantum, 2022 Variational quantum circuits are used in quantum machine learning and variational quantum simulation tasks. Designing good variational circuits or predicting how well they perform for given learning or optimization tasks is still unclear. Here we discuss
Junyu Liu +4 more
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Frontier Advances of Emerging High‐Entropy Anodes in Alkali Metal‐Ion Batteries
Advanced Functional Materials, EarlyView.Recent advances in microscopic morphology control of high‐entropy anode materials for alkali metal‐ion batteries. Abstract With the growing demand for sustainable energy, portable energy storage systems have become increasingly critical. Among them, the development of rechargeable batteries is primarily driven by breakthroughs in electrode materials ...
Liang Du +14 more
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Physical Review ResearchThe 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 +3 more
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