Non-Local Quantum Computing Based on Adaptive Geometric Optimization

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel non-local quantum computing framework centered around adaptive geometric optimization. We propose a quantum algorithm leveraging quantum entanglement to achieve significantly enhanced precision in computations compared to traditional methods. The core mechanism involves strategically manipulating entangled states to induce specific geometric transformations within the quantum system, enabling the algorithm to find optimal solutions with higher fidelity. The proposed approach offers a significant advancement in the realm of quantum computation, particularly in applications requiring high precision and complex optimization problems. We present a detailed theoretical analysis, including the mathematical formulation of the optimization process and the implementation of the core algorithm. The resulting algorithm demonstrates superior performance in a series of benchmark tests, highlighting its potential for revolutionizing various fields, including materials science, drug discovery, and financial modeling.

DOI: 10.5281/zenodo.22635529

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