论文标题
绝热量子线性回归
Adiabatic Quantum Linear Regression
论文作者
论文摘要
机器学习的主要挑战是培训这些模型的计算费用。模型培训可以视为一种优化形式，用于将机器学习模型适合一组数据，这可能会在古典计算机上花费大量时间。绝热量子计算机已被证明在解决优化问题方面表现出色，因此，我们认为，为改善机器学习训练时间提供了有希望的替代方法。在本文中，我们提出了一种训练线性回归模型的绝热量子计算方法。为了做到这一点，我们将回归问题作为二次无约束的二进制优化（QUBO）问题提出。我们从理论上分析量子方法，在D-WAVE 2000Q绝热量子计算机上进行测试，并将其性能与使用Python的Scikit-Learn库的经典方法进行比较。我们的分析表明，量子方法在较大数据集上的经典方法上达到了高达2.8倍的速度，并且在回归误差度量方面的经典方法表现出色。
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.
