Quantum Machine Learning

256 papers for year 2021

Showing 133-144 of 256

Nuwa: A Quantum Circuit Transpiler Based on a Finite-Horizon Heuristic for Placement and Routing

Shengru Ren, KaWai Chen, Navid Ghadermarzy, Brandon Nguyen, Yanhao Huang, Pooya Ronagh

2021 arXiv arXiv preprint

On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification

Alessandro Sebastianelli, Daniela A. Zaidenberg, Dario Spiller, Bertrand Le Saux, Silvia Liberata Ullo

2021 arXiv arXiv preprint

On Feynman's Discussion of Classical Physics Failing at Specific Heat

Blake C. Stacey

2021 arXiv arXiv preprint

On nonlinear transformations in quantum computation

Zoë Holmes, Nolan Coble, Andrew T. Sornborger, Yiğit Subaşı

2021 arXiv arXiv preprint

On the challenges of using D-Wave computers to sample Boltzmann Random Variables

Thomas Pochart, Paulin Jacquot, Joseph Mikael

2021 arXiv arXiv preprint

On the effects of biased quantum random numbers on the initialization of artificial neural networks

Raoul Heese, Moritz Wolter, Sascha Mücke, Lukas Franken, Nico Piatkowski

2021 arXiv arXiv preprint

Online quantum time series processing with random oscillator networks

Johannes Nokkala

2021 arXiv arXiv preprint

Open Problems Related to Quantum Query Complexity

Scott Aaronson

2021 arXiv arXiv preprint

Optimal control of quantum thermal machines using machine learning

Ilia Khait, Juan Carrasquilla, Dvira Segal

2021 arXiv arXiv preprint

Optimization of Sources of Circulating Cell-Free DNA Variability for Downstream Molecular Analysis.

Till JE, Black TA, Gentile C, Abdalla A, Wang Z, Sangha HK, Roth JJ, Sussman R, Yee SS, O'Hara MH, Thompson JC, Aggarwal C, Hwang WT, Elenitoba-Johnson KSJ, Carpenter EL.

2021 Europe PMC J Mol Diagn

Optimized Machine Learning: Training and Classification Performance Using Quantum Computing

Mukta Nivelkar, S. G. Bhirud

2021 Crossref 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA)

Optimizing Quantum Variational Circuits with Deep Reinforcement Learning

Owen Lockwood

2021 arXiv arXiv preprint