PyQUBO: Python Library for Mapping Combinatorial Optimization Problems to QUBO Form

Solving Sensor Placement Problems In Real Water Distribution Networks Using Adiabatic Quantum Computation

Quantum annealing has emerged in the last few years as a promising quantum computing approach to solving large-scale combinatorial optimization problems. In this paper, we formulate the problem of correctly placing pressure sensors on a Water Distribution Network (WDN) as a combinatorial optimization problem in the form of a Quadratic Unconstrained Binary Optimization (QUBO) or Ising model. Optimal sensor placement is indeed key to detect and isolate fault events. We outline the QUBO and Ising formulations for the sensor placement problem starting from the network topology and few other featur…

QUBO formulations for numerical quantum computing

With the advent of quantum computers, many quantum computing algorithms are being developed. Solving linear systems is one of the most fundamental problems in almost all science and engineering. The Harrow-Hassidim-Lloyd algorithm, a monumental quantum algorithm for solving linear systems on gate model quantum computers, was invented and several advanced variations have been developed. For a given n by n matrix A and a vector b, we will find unconstrained binary optimization (QUBO) models for a vector x that satisfies Ax=b. To formulate QUBO models for a linear system solving problem, we make …

Quantum Annealing Learning Search Implementations

This paper presents the details and testing of two implementations (in C++ and Python) of the hybrid quantum-classical algorithm Quantum Annealing Learning Search (QALS) on a D-Wave quantum annealer. QALS was proposed in 2019 as a novel technique to solve general QUBO problems that cannot be directly represented into the hardware architecture of a D-Wave machine. Repeated calls to the quantum machine within a classical iterative structure and a related convergence proof originate a learning mechanism to find an encoding of a given problem into the quantum architecture. The present work conside…

一种用于构建量子计算路径规划问题的框架

A Framework to Formulate Pathfinding Problems for Quantum Computing

针对量子优化问题QUBO形式构建难度大的问题，提出可自动生成路径规划问题QUBO表述的框架，支持多编码方案，配套开源工具与网页界面，大幅降低量子计算应用门槛。

Omnisolver：面向伊辛自旋玻璃与QUBO求解器的可扩展接口——添加分布式GPU暴力搜索插件

Omnisolver: An extensible interface to Ising spin-glass and QUBO solvers: adding a distributed GPU brute-force plugin

Omnisolver新增分布式GPU暴力搜索插件，可在CUDA GPU上精确穷举求解QUBO与伊辛实例，在8个H100 GPU上N=60时求解约3.15天，为经典启发式算法提供真值 oracle。

通过高阶编码实现资源高效的量子优化

Resource-Efficient Quantum Optimization via Higher-Order Encoding

本文提出利用高阶无约束二元优化（HUBO）替代传统的二次无约束二元优化（QUBO），在门分配（GAP）、最大k-着色子图（MkCS）和整数规划（IP）问题中显著减少量子比特需求和CNOT门数量，展示了HUBO在近期量子设备上的实用性。