Search Results for author: Bohdan Kulchytskyy

Found 5 papers, 2 papers with code

A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction

no code implementations18 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.

Decoder

Neural Error Mitigation of Near-Term Quantum Simulations

2 code implementations17 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.

Generative models for sampling of lattice field theories

no code implementations2 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

QuCumber: wavefunction reconstruction with neural networks

1 code implementation21 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

Quantum Boltzmann Machine

no code implementations8 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

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