Modeling Linear Inequality Constraints in Quadratic Binary Optimization for Variational Quantum Eigensolver

Quinones, Miguel Paredes; Junqueira, Catarina

doi:10.48550/arxiv.2007.13245

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Overview Of the Problem-specific Pqc1

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2023

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“…Quinones et al [18] and Perelshtein et al [19] consider constructions for different types of constraints than those proposed in this paper. Although our approach differs in terms of the specific constraints and optimization problems considered, we also propose further ideas to improve problem-specific PQCs by restricting the achievable quantum state space and reducing cost for noisy quantum devices.…”

Section: Overview Of the Problem-specific Pqcmentioning

confidence: 98%

Enhancing VQE Convergence for Optimization Problems with Problem-Specific Parameterized Quantum Circuits

MATSUO,

SUZUKI,

HAMAMURA

et al. 2023

IEICE Trans. Inf. & Syst.

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The Variational Quantum Eigensolver (VQE) algorithm is gaining interest for its potential use in near-term quantum devices. In the VQE algorithm, parameterized quantum circuits (PQCs) are employed to prepare quantum states, which are then utilized to compute the expectation value of a given Hamiltonian. Designing efficient PQCs is crucial for improving convergence speed. In this study, we introduce problem-specific PQCs tailored for optimization problems by dynamically generating PQCs that incorporate problem constraints. This approach reduces a search space by focusing on unitary transformations that benefit the VQE algorithm, and accelerate convergence. Our experimental results demonstrate that the convergence speed of our proposed PQCs outperforms state-of-the-art PQCs, highlighting the potential of problem-specific PQCs in optimization problems.

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Section: Overview Of the Problem-specific Pqcmentioning

confidence: 98%