{"ID":2895666,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.08489","arxiv_id":"2507.08489","title":"Towards solving large QUBO problems using quantum algorithms: improving the LogQ scheme","abstract":"The LogQ algorithm encodes Quadratic Unconstrained Binary Optimization (QUBO) problems with exponentially fewer qubits than the Quantum Approximate Optimization Algorithm (QAOA). The advantages of conventional LogQ are accompanied by a challenge related to the optimization of its free parameters, which requires the usage of resource intensive evolutionary or even global optimization algorithms. We propose a new LogQ parameterization that can be optimized with a gradient-inspired method, which is less resource-intensive and thus strengthens the advantage of LogQ over QAOA for large/industrial problems. We illustrate the features of our method on an analytical model and present larger scale numerical results on MaxCut problems.","short_abstract":"The LogQ algorithm encodes Quadratic Unconstrained Binary Optimization (QUBO) problems with exponentially fewer qubits than the Quantum Approximate Optimization Algorithm (QAOA). The advantages of conventional LogQ are accompanied by a challenge related to the optimization of its free parameters, which requires the usa...","url_abs":"https://arxiv.org/abs/2507.08489","url_pdf":"https://arxiv.org/pdf/2507.08489v1","authors":"[\"Yagnik Chatterjee\",\"Jérémie Messud\"]","published":"2025-07-11T11:07:56Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"math.OC\"]","methods":"[]","has_code":false}