Quadratic unconstrained binary optimization (QUBO), also known as unconstrained binary quadratic programming (UBQP), is a combinatorial optimization problem with a wide range of applications from finance and economics to machine learning. [1] QUBO is an NP hard problem, and for many classical problems from theoretical computer science, like ...

2021年5月11日 · We formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems so that they can be...

2018年11月13日 · This tutorial discloses the basic features of the QUBO model that give it the power and flexibility to encompass the range of applications that have thrust it onto center stage of the optimization field.

2023年4月13日 · 本文介绍了量子退火法作为量子计算机的一种算法，用于解决二次无约束二值优化问题。 通过将问题转换为哈密顿算符和QUBO矩阵，量子退火法能高效找到最优解。 文中以Python模拟退火算法为例，展示了如何利用QUBO解决二次多项式问题，并提到量子退火机在大规模问题上的优势。 量子退火法能解决什么问题？ 量子计算机是利用“量子叠加”，“纠缠”等 量子力学 现象实现 并行 计算的计算机。 传统计算机需要大量时间才能得出答案的问题，量子计算机可 …

Optimization (QUBO) model unifies a wide variety of combinatorial optimization problems, and moreover is the foundation of adiabatic quantum computing and a subject of study in neuromorphic computing. Through these connections, QUBO models lie at the heart of experimentation carried out with quantum computers developed by D-Wave Systems and

2024年8月24日 · 相干伊辛机(Coherent Ising Machine, 简称CIM), 是目前玻色量子重点研发的一项光量子计算机技术，CIM是一种基于简并光学参量振荡器(DOPO)的光量子计算机，在数学实践中, 我们可以将其抽象为优化Ising模型的专用计算机。

2020年8月5日 · In this paper, we formulate the training problems of three machine learning models---linear regression, support vector machine (SVM) and equal-sized k-means clustering---as QUBO problems so that they can be trained on adiabatic quantum computers efficiently.

In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k‐means clustering—as QUBO problems, making...

2022年4月7日 · Quantum Bridge Analytics relates generally to methods and systems for hybrid classical-quantum computing, and more particularly is devoted to developing tools for bridging classical and quantum computing to gain the benefits of their alliance in the present and enable enhanced practical application of quantum computing in the future.

2021年11月9日 · 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...