Solving Constrained Combinatorial Optimization Problems with Variational Quantum Imaginary Time Evolution

• URL: http://arxiv.org/abs/2504.12607v1
• Date: Thu, 17 Apr 2025 03:09:37 GMT
• Title: Solving Constrained Combinatorial Optimization Problems with Variational Quantum Imaginary Time Evolution
• Authors: Xin Wei Lee, Hoong Chuin Lau,
• Abstract summary: We show that VarQITE achieves significantly lower mean optimality gaps compared to other conventional methods.<n>We demonstrate that scaling the Hamiltonian can further reduce optimization costs and accelerate convergence.
• Score: 4.266376725904727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Solving combinatorial optimization problems using variational quantum algorithms (VQAs) has emerged as a promising research direction. Since the introduction of the Quantum Approximate Optimization Algorithm (QAOA), numerous variants have been proposed to enhance its performance. QAOA was later extended to the Quantum Alternating Operator Ansatz (QAOA+), which generalizes the initial state, phase-separation operator, and mixer to address constrained problems without relying on the standard Quadratic Unconstrained Binary Optimization (QUBO) formulation. However, QAOA+ often requires additional ancilla qubits and a large number of multi-controlled Toffoli gates to prepare the superposition of feasible states, resulting in deep circuits that are challenging for near-term quantum devices. Furthermore, VQAs are generally hindered by issues such as barren plateaus and suboptimal local minima. Recently, Quantum Imaginary Time Evolution (QITE), a ground-state preparation algorithm, has been explored as an alternative to QAOA and its variants. QITE has demonstrated improved performance in quantum chemistry problems and has been applied to unconstrained combinatorial problems such as Max-Cut. In this work, we apply the variational form of QITE (VarQITE) to solve the Multiple Knapsack Problem (MKP), a constrained problem, using a Max-Cut-tailored ansatz. To the best of our knowledge, this is the first attempt to address constrained optimization using VarQITE. We show that VarQITE achieves significantly lower mean optimality gaps compared to QAOA and other conventional methods. Moreover, we demonstrate that scaling the Hamiltonian coefficients can further reduce optimization costs and accelerate convergence.

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