• Suche: Quantum Machine Learning with Python

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161

HYPE-MAD: high-yield python environment for molecular automation and data generation.
Peivaste, Iman ; Makradi, Ahmed ; Mercuri, Francesco ; et al.
Acta Mechanica. Apr2025, p1-9.

PYTHON programming langu... COMPUTER-assisted molecu... MACHINE learning DATA structures HIGH throughput screenin... RENEWABLE energy sources …
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162

QMLearn - Quantum Machine Learning
Shao, Xuecheng ; orcid:0000-0003-2215- ; Paetow, Lukas ; et al.

QMLearn Python
E-Ressource
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163

QMetric: Benchmarking Quantum Neural Networks Across Circuits, Features, and Training Dimensions
Illésová, Silvie ; Rybotycki, Tomasz ; Beseda, Martin
QualITA 2025: The Fourth Conference on System and Service Quality co-located with 39th International Conference on Modelling and Simulation (ECMS 2025), https://ceur-ws.org/Vol-4080/

Quantum Physics Computational Physics
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164

A novel approach to reduce derivative costs in variational quantum algorithms
G Minuto ; D Melegari ; S Caletti ; et al.
Journal of Physics A: Mathematical and Theoretical, 58 (18)

quantum chemistry gradient Quantum Physics quantum machine learning FOS: Physical sciences quantum variational algo... …
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165

Python at Warp Speed
Schreiber, Andreas

Verteilte Systeme und Ko... distributed computing Institut für Simulations... machine learning Data Science HPC …
Konferenz
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166

Python in Chemistry: Physicochemical Tools.
Ryzhkov, Fedor V. ; Ryzhkova, Yuliya E. ; Elinson, Michail N.
Processes; Oct2023, Vol. 11 Issue 10, p2897, 24p

PYTHON programming langu... PYTHONS QUANTUM chemistry COMPUTATIONAL chemistry PHYSICAL & theoretical c... QUANTUM wells
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167

Correction to: Meso-Scale Electrodynamic Coupling of the Earth Magnetosphere-Ionosphere System
Yiqun Yu ; Jinbin Cao ; Z. Y. Pu ; et al.
Space Science Reviews. 219

Artificial intelligence Construct (python librar... FOS: Political science FOS: Law Node (physics) Quantum mechanics …
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169

Machine-learning accelerated geometry optimization in molecular simulation.
Yang, Yilin ; Jiménez-Negrón, Omar A. ; Kitchin, John R.
Journal of Chemical Physics; 6/21/2021, Vol. 154 Issue 23, p1-9, 9p

MOLECULAR shapes NUCLEAR reactions MATERIALS science MOLECULAR crystals MOLECULAR structure PYTHON programming langu...
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170

New cognitive computational strategy for optimizing brain tumour classification using magnetic resonance imaging Data
Kanna, R. Kishore ; Salau, Ayodeji Olalekan
In Intelligence-Based Medicine 2025 11

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171

MLQD: A package for machine learning-based quantum dissipative dynamics.
Ullah, Arif ; Dral, Pavlo O.
Computer Physics Communications. Jan2024, Vol. 294, pN.PAG-N.PAG. 1p.

QUANTUM theory ARTIFICIAL intelligence PYTHON programming langu... MACHINE learning CONVOLUTIONAL neural net... QUANTUM wells
Zeitschrift
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172

Source code repository for the preprint: 'Training the parametric interactions in an analog bosonic quantum neural network with Fock basis measurement'
Dudas, Julien ; orcid:0009-0006-4223- ; Marković, Danijela ; et al.

Quantum computers Quantum physics Machine learning Supervised Machine Learn...
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173

PYSEQM 2.0: Accelerated Semiempirical Excited-State Calculations on Graphical Processing Units
Athavale, Vishikh ; Fedik, Nikita ; Colglazier, William ; et al.
Journal of Chemical Theory and Computation. 21(19):9498-9510

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174

Classification of Pixel Tracks to Improve Track Reconstruction from Proton-Proton Collisions
Fantahun, Kebur ; Joseph, Jobin ; Purdom, Halle ; et al.
SMU Data Science Review

Particle Collision Data Science Neural Network Deep Learning Classification Machine Learning …
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175

MLQD: A package for machine learning-based quantum dissipative dynamics
Arif Ullah ; Pavlo O. Dral
Computer Physics Communications. 294:108940

Chemical Physics (physic... Physics - Chemical Physi... 0103 physical sciences FOS: Physical sciences 01 natural sciences
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176

QDataSet, quantum datasets for machine learning.
Perrier, Elija ; Youssry, Akram ; Ferrie, Chris
Scientific Data; 9/23/2022, Vol. 9 Issue 1, p1-22, 22p

MACHINE learning QUANTUM computing QUANTUM computers PROBLEM solving QUBITS
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177

dxtb—An efficient and fully differentiable framework for extended tight-binding.
Friede, Marvin ; Hölzer, Christian ; Ehlert, Sebastian ; et al.
Journal of Chemical Physics; 8/14/2024, Vol. 161 Issue 6, p1-17, 17p

LARGE scale systems AUTOMATIC differentiatio... QUANTUM chemistry RAPID prototyping MACHINE parts
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178

PennyLane: Automatic differentiation of hybrid quantum-classical computations
Bergholm, Ville ; Izaac, Josh ; Schuld, Maria ; et al.

Quantum Physics Computer Science - Emerg... Computer Science - Machi... Physics - Computational...
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179

Geometrical versus time-series representation of data in quantum control learning
Ostaszewski, M. ; Miszczak, J. A. ; Sadowski, P.
Journal of Physics A: Mathematical and Theoretical, Volume 53, Number 19 (2020)

Quantum Physics Computer Science - Machi...
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180

QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics
Parisi, L. ; Neagu, D. ; Ma, R. ; et al.

Computer Science - Compu... Computer Science - Machi... Computer Science - Neura... Electrical Engineering a... 68T07, 68T10, 68T45, 68U... I.2.1 …
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