Solving machine learning optimization problems using quantum computers
Classical optimization algorithms in machine learning often take a long time to compute when applied to a multi-dimensional problem and require a huge amount of CPU and GPU resource. Quantum parallelism has a potential to speed up machine learning algorithms. We describe a generic mathematical model to leverage quantum parallelism to speed-up machine learning algorithms. We also apply quantum machine learning and quantum parallelism applied to a $3$-dimensional image that vary with time.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningCPUGPUQuantum Machine LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Using quantum computers in control: interval matrix properties
Quantum computing provides a powerful framework for tackling computational problems that are classically intractable. The goal of this paper is to explore the use of quantum computers for solving relevant problems in sys…
An Introduction to Quantum Computing for Statisticians and Data Scientists
Quantum computers promise to surpass the most powerful classical supercomputers when it comes to solving many critically important practical problems, such as pharmaceutical and fertilizer design, supply chain and traffi…
Adiabatic Quantum Linear Regression
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…
BIG-bench Machine LearningregressionQUBO Formulations for Training Machine Learning Models
Training machine learning models on classical computers is usually a time and compute intensive process. With Moore's law coming to an end and ever increasing demand for large-scale data analysis using machine learning, …
BIG-bench Machine LearningClusteringregressionHierarchical Quantum Control Gates for Functional MRI Understanding
Quantum computing has emerged as a powerful tool for solving complex problems intractable for classical computers, particularly in popular fields such as cryptography, optimization, and neurocomputing. In this paper, we …