22 citations · 36 across the 4 of their papers we have counts for
4 papers
Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction
Daniel Fry, Amol Deshmukh, Samuel Yen-Chi Chen +2
Quantum reservoir computing is strongly emerging for sequential and time series data prediction in quantum machine learning. We make advancements to the quantum noise-induced reser…
Implementation and Learning of Quantum Hidden Markov Models
Vanio Markov, Vladimir Rastunkov, Amol Deshmukh +2
In this article, we use the theory of quantum channels and open quantum systems to provide an efficient unitary characterization of a class of stochastic generators known as quantu…
Reservoir Computing via Quantum Recurrent Neural Networks
Samuel Yen-Chi Chen, Daniel Fry, Amol Deshmukh +2
Recent developments in quantum computing and machine learning have propelled the interdisciplinary study of quantum machine learning. Sequential modeling is an important task with…
Boosting Method for Automated Feature Space Discovery in Supervised Quantum Machine Learning Models
Vladimir Rastunkov, Jae-Eun Park, Abhijit Mitra +5
Quantum Support Vector Machines (QSVM) have become an important tool in research and applications of quantum kernel methods. In this work we propose a boosting approach for buildin…