no code implementations • 18 Jul 2023 • Victor Yon, Frédéric Marcotte, Pierre-Antoine Mouny, Gebremedhin A. Dagnew, Bohdan Kulchytskyy, Sophie Rochette, Yann Beilliard, Dominique Drouin, Pooya Ronagh
In simulations supported by experimental measurements, we investigate the impact of TiOx-based memristive devices' non-idealities on decoding fidelity.
2 code implementations • 17 May 2021 • Elizabeth R. Bennewitz, Florian Hopfmueller, Bohdan Kulchytskyy, Juan Carrasquilla, Pooya Ronagh
Near-term quantum computers provide a promising platform for finding ground states of quantum systems, which is an essential task in physics, chemistry, and materials science.
no code implementations • 2 Dec 2020 • Matija Medvidovic, Juan Carrasquilla, Lauren E. Hayward, Bohdan Kulchytskyy
We explore a self-learning Markov chain Monte Carlo method based on the Adversarial Non-linear Independent Components Estimation Monte Carlo, which utilizes generative models and artificial neural networks.
Disordered Systems and Neural Networks Statistical Mechanics Computational Physics
1 code implementation • 21 Dec 2018 • Matthew J. S. Beach, Isaac De Vlugt, Anna Golubeva, Patrick Huembeli, Bohdan Kulchytskyy, Xiuzhe Luo, Roger G. Melko, Ejaaz Merali, Giacomo Torlai
As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter, and devices.
Quantum Physics Strongly Correlated Electrons
no code implementations • 8 Jan 2016 • Mohammad H. Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, Roger Melko
Inspired by the success of Boltzmann Machines based on classical Boltzmann distribution, we propose a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Hamiltonian.
Quantum Physics