Publikacje
Found 15 results
Filters: Autor is Mateusz Ostaszewski  [Clear All Filters]
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2024.  Enhancing quantum variational state diagonalization using reinforcement learning techniques. New Journal of Physics. 26
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2023.  The Effectiveness of World Models for Continual Reinforcement Learning. Conference on Lifelong Learning Agents. 
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2021.  Reinforcement learning for optimization of variational quantum circuit architectures. NeurIPS. 
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2021.  Structure optimization for parameterized quantum circuits. Quantum. 5
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2020.  Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling. Remote Sensing. 12(16)
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2020.  Geometrical versus time-series representation of data in quantum control learning. Journal of Physics A: Mathematical and Theoretical. 53(19)
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2019.  Approximation of quantum control correction scheme using deep neural networks. Quantum Information Processing. 18
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2019.  An initialization strategy for addressing barren plateaus in parametrized quantum circuits. Quantum. 3
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2019.  QSWalk. jl: Julia package for quantum stochastic walks analysis. Computer Physics Communications. 235
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2019.  Unsupervised deep learning approach to hyperspectral anomaly detection. PP-RAI'2019. 
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2018.  Limiting properties of stochastic quantum walks on directed graphs. Journal of Physics A: Mathematical and Theoretical. 51(3)
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2018.  Properties of quantum stochastic walks from the asymptotic scaling exponent. Quantum Information and Computation. 18(3&4)
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2017.  Superdiffusive quantum stochastic walk definable on arbitrary directed graph. Quantum Information & Computation. 17(11&12)
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2016.  Lively quantum walks on cycles. J. Phys. A: Math. Theor.. 49:375302.
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2015.  Quantum image classification using principal component analysis. Theoretical and Applied Informatics. 27(1)
