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Title: Hybrid Quantum-Classical Simulation for Quantum Field Theory

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

Quantum field theory (QFT) encompasses a vast and complex landscape of physical phenomena, making accurate simulation a significant challenge. Classical computers struggle to handle the exponential scaling of the computational cost with increasing system size, limiting the exploration of these theories. Hybrid quantum-classical simulation offers a promising path toward overcoming these limitations by intelligently leveraging the strengths of both paradigms. This research investigates a novel hybrid solver, the 'Quantum-Classical Hybrid Solver,' designed to efficiently simulate complex QFTs. We demonstrate the method's ability to surpass classical simulation capabilities in certain regimes while maintaining computational efficiency, offering a significant advancement in the field. The core mechanism centers around a dynamic switching between classical and quantum techniques, tailored to the specific problem at hand. This paper details the design, implementation, and preliminary results of the Hybrid Quantum-Classical Solver, highlighting its potential for accelerating QFT research.

DOI
10.5281/zenodo.22185120

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Quantum simulation offers a revolutionary approach to understanding complex dynamical systems, enabling insights into emergent phenomena previously inaccessible through classical methods. This paper explores a novel hybrid quantum-classical algorithm designed to efficiently simulate these systems, combining the strengths of both paradigms. We propose a method for intelligently distributing computational tasks between quantum and classical components, optimizing resource utilization and accelerating the simulation process. The core claim is that this approach can overcome limitations in current quantum simulation techniques, facilitating the study of systems exhibiting complex dynamics and emergent behavior. This work presents a framework for seamless integration of quantum and classical algorithms, demonstrating its potential for significant advancements in the modeling and analysis of diverse dynamical systems.
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