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

11/30/2021
by   Thomas Pochart, et al.
0

Sampling random variables following a Boltzmann distribution is an NP-hard problem involved in various applications such as training of Boltzmann machines, a specific kind of neural network. Several attempts have been made to use a D-Wave quantum computer to sample such a distribution, as this could lead to significant speedup in these applications. Yet, at present, several challenges remain to efficiently perform such sampling. We detail the various obstacles and explain the remaining difficulties in solving the sampling problem on a D-wave machine.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/17/2016

Reinforcement Learning Using Quantum Boltzmann Machines

We investigate whether quantum annealers with select chip layouts can ou...
research
02/03/2020

Machine learning in quantum computers via general Boltzmann Machines: Generative and Discriminative training through annealing

We present a Hybrid-Quantum-classical method for learning Boltzmann mach...
research
03/15/2021

Assessment of image generation by quantum annealer

Quantum annealing was originally proposed as an approach for solving com...
research
02/17/2022

Full-Span Log-Linear Model and Fast Learning Algorithm

The full-span log-linear(FSLL) model introduced in this paper is conside...
research
12/18/2013

On the Challenges of Physical Implementations of RBMs

Restricted Boltzmann machines (RBMs) are powerful machine learning model...
research
06/14/2022

Automatic compile-time synthesis of entropy-optimal Boltzmann samplers

We present a famework for the automatic compilation of multi-parametric ...
research
12/21/2020

Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines

We provide a robust defence to adversarial attacks on discriminative alg...

Please sign up or login with your details

Forgot password? Click here to reset