# Smooth Structured Prediction Using Quantum and Classical Gibbs Samplers

By Behrooz Sepehry, Ehsan Iranmanesh, Michael P. Friedlander, & Pooya Ronagh
We introduce a quantum algorithm for solving structured prediction problems with a runtime that scales with the square root of the size of the label space, but scales in $$\widetilde O\left(\epsilon^{-3.5}\right)$$ with respect to the precision, $$\epsilon$$, of the solution. In doing so, we analyze a stochastic gradient algorithm for convex optimization in the presence of an additive error in the calculation of the gradients, and show that its convergence rate does not deteriorate if the additive errors are of the order $$O(\sqrt\epsilon)$$. Our algorithm uses quantum Gibbs sampling at temperature $$\Omega (\epsilon)$$ as a subroutine. Based on these theoretical observations, we propose a method for using quantum Gibbs samplers to combine feedforward neural networks with probabilistic graphical models for quantum machine learning. Numerical results using Monte Carlo simulations on an image tagging task demonstrate the benefit of the approach.

## Scaling Overhead of Locality Reduction in Binary Optimization Problems

By Elisabetta Valiante, Maritza Hernandez, Amin Barzegar, & Helmut G. Katzgraber Recently, there has been considerable interest in solving optimization problems by mapping these onto a binary representation, sparked mostly by the use of quantum annealing machines....

## Quantum Multiple Kernel Learning

By Seyed Shakib Vedaie, Moslem Noori, Jaspreet S. Oberoi, Barry C. Sanders, & Ehsan Zahedinejad Kernel methods play an important role in machine learning applications due to their conceptual simplicity and superior performance on numerous machine learning tasks....

## Quantum Annealing Approaches to the Phase-Unwrapping Problem in Synthetic-Aperture Radar Imaging

By Khaled A. Helal Kelany, Nikitas Dimopoulos, Clemens P. J. Adolphs, Bardia Barabadi, & Amirali Baniasadi The focus of this work is to explore the use of quantum annealing solvers for the problem of phase unwrapping of synthetic aperture radar (SAR) images....

## Finding the Ground State of Spin Hamiltonians with Reinforcement Learning

By Kyle Mills, Pooya Ronagh, & Isaac Tamblyn Reinforcement learning (RL) has become a proven method for optimizing a procedure for which success has been defined, but the specific actions needed to achieve it have not. Using a method we call "controlled online...

## Variationally Scheduled Quantum Simulation

By Shunji Matsuura, Samantha Buck, Valentin Senicourt, & Arman Zaribafiyan Eigenstate preparation is ubiquitous in quantum computing, and a standard approach for generating the lowest-energy states of a given system is by employing adiabatic state preparation...