Perdomo-Ortiz, Alejandro

NASA NTRS2016

Applying Simulated Annealing to Problems in Model-Based Diagnosis

Generating all diagnoses is computationally intractable. Therefore, many of the state-of-the-art approaches are incomplete. Quantum computers may however offer a solution. The first commercially available quantum computer is being used to minimize polynomials that are difficult for classical simulated annealing but easy for quantum annealing. All problems in Model-based Diagnosis (MBD) can be transformed into a polynomial minimization problem, allowing one to apply a quantum algorithm called quantum annealing to solve MBD problems. To better understand the need for this quantum approach, we designed two simulated annealingdiagnostic algorithms tailored to run on a polynomial representation of MBD. These algorithms differ on their policy for random neighborhood variable selection. In addition, enhanced metrics were devised to provide more diagnostic coverage. Finally, these two simulated annealing algorithms were analyzed and empirically evaluated and compared against state-of-the-art probabilistic methods for MBD such as SAFARI using ISCAS-85.

Simulated annealing↗

NASA NTRS2016

A Quantum-Assisted Algorithm for Sampling Applications in Machine Learning

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact in deep learning and other machine learning applications. Recently, quantum annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggests it will do so with an instance-dependent effective temperature, different from the physical temperature of the device. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this talk, we present a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a kind of restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep learning architectures. We also provide a comparison to k-step contrastive divergence (CD-k) with k up to 100. Although assuming a suitable fixed effective temperature also allows to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature we find a performance close to that of CD-100 for the case studied here. We discuss generalizations of the algorithm to other more expressive generative models, beyond restricted Boltzmann machines.

Perdomo-Ortiz, Alejandro↗