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QUBO.jl: A Julia Ecosystem for Quadratic Unconstrained Binary Optimization

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arxiv 2307.02577 v3 pith:ZQTUGXVF submitted 2023-07-05 math.OC quant-ph

classification math.OCquant-ph
keywords qubooptimizationquantumjuliamethodsannealingbinaryjump
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We present QUBO.jl, an end-to-end Julia package for working with QUBO (Quadratic Unconstrained Binary Optimization) instances. This tool aims to convert a broad range of optimization problems in JuMP, Julia's mathematical programming package, for straightforward application in many physics and physics-inspired solution methods whose standard model form is equivalent to QUBO. These methods include quantum annealing, quantum gate-circuit optimization algorithms (Quantum Optimization Alternating Ansatz, Variational Quantum Eigensolver), other hardware-accelerated platforms, such as Coherent Ising Machines and Simulated Bifurcation Machines, and more traditional methods such as simulated annealing. In addition to working with reformulations, QUBO.jl allows its users to interface with the aforementioned hardware, sending QUBO models to these devices and retrieving results for subsequent analysis. QUBO.jl was written as a JuMP / MathOptInterface (MOI) layer that automatically maps between the input and output frames, thus providing a smooth modeling experience.

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  1. Hybrid Quantum Branch-and-Bound Method for Quadratic Unconstrained Binary Optimization

    math.OC 2025-09 conditional novelty 4.0 of 10

    A hybrid quantum-classical branch-and-bound solver for QUBO shows that a classical degree-based branching rule delivers the largest speedups (11% time, 17% nodes), while D-Wave warm starts contribute only a few percen...

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