Adiabatic Quantum Linear Regression

Abstract

A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to excel at solving optimization problems, and therefore, we believe, present a promising alternative to improve machine learning training times. In this paper, we present an adiabatic quantum computing approach for training a linear regression model. In order to do this, we formulate the regression problem as a quadratic unconstrained binary optimization (QUBO) problem. We analyze our quantum approach theoretically, test it on the D-Wave 2000Q adiabatic quantum computer and compare its performance to a classical approach that uses the Scikit-learn library in Python. Our analysis shows that the quantum approach attains up to 2.8x speedup over the classical approach on larger datasets, and performs at par with the classical approach on the regression error metric.

References in corpus (8)

• Quantum Computing
• Quantum algorithm for solving linear systems of equations
• Multivariable Optimization: Quantum Annealing & Computation
• Training a Binary Classifier with the Quantum Adiabatic Algorithm
• Pegasus: The second connectivity graph for large-scale quantum annealing hardware
• Next-Generation Topology of D-Wave Quantum Processors
• Algorithm engineering for a quantum annealing platform
• Efficient Combinatorial Optimization Using Quantum Annealing

Cited by in corpus (11)

• QUBO Formulations for Training Machine Learning Models
• Training a quantum annealing based restricted Boltzmann machine on cybersecurity data
• Quantum Gaussian process model of potential energy surface for a polyatomic molecule
• Controller-based Energy-Aware Wireless Sensor Network Routing using Quantum Algorithms
• Balanced k-Means Clustering on an Adiabatic Quantum Computer
• How to experimentally evaluate the adiabatic condition for quantum annealing
• Quantum-Assisted Support Vector Regression
• Quantum Discriminator for Binary Classification
• Quantum Annealing Formulation for Binary Neural Networks
• Calculating Nash Equilibrium on Quantum Annealers
• Range dependent Hamiltonian Algorithm for numerical QUBO formulation