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Two-Step QAOA: Enhancing Quantum Optimization by Decomposing K-hot Constraints in QUBO Formulations
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Two-Step QAOA: Enhancing Quantum Optimization by Decomposing K-hot Constraints in QUBO Formulations
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The Quantum Approximate Optimization Algorithm (QAOA) has shown promise in solving combinatorial optimization problems by leveraging quantum computational power. We propose a simple approach, the Two-Step QAOA, which aims to improve the effectiveness of QAOA by decomposing problems with k-hot encoding QUBO (Quadratic Unconstrained Binary Optimization) formulations. By identifying and separating the problem into two stages, we transform soft constraints into hard constraints, simplifying the generation of initial conditions and enabling more efficient optimization. The method is particularly beneficial for tackling complex societal problems that often involve intricate constraint structures.
Forward citations
Cited by 3 Pith papers
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Decomposition-Based QAOA for Maximum Coverage Location Problem in Satellite Constellation Design
Decomposition-based QAOA with spectral graph cuts and GSR merging solves large satellite MCLP instances with competitive coverage and bounded qubit use where standard QAOA is infeasible.
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Towards High Performance Quantum Computing (HPQ): Parallelisation of the Hamiltonian Auto Decomposition Optimisation Framework (HADOF)
Parallel HADOF execution on up to four IBM QPUs achieves 3-4x wall-clock speedup for combinatorial QUBO problems versus sequential runs, with comparable quality and validation on genome assembly instances.
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QTIS: A QAOA-Based Quantum Time Interval Scheduler
QTIS-QAOA separates the objective and overlap-penalty parts of the scheduling Hamiltonian with distinct QAOA angles, yielding a small measured improvement on toy instances.
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