Braid-Based Quantum Simulations on Classical Computing Architectures

Abstract: This article presents a novel approach to simulating quantum and braid-based data encoding schemes on classical computing architectures, with a focus on predictive modeling in biomedical applications. Drawing on mathematical structures from quantum information theory and braid theory, we demonstrate that braid-based encodings can effectively represent complex, multi-agent interactions such as those found in combinatorial cancer therapies. Notably, hybrid encoding models that combine braid-based features with conventional biological data yielded improved performance in predicting synergistic drug combinations while maintaining low toxicity estimates. These findings support our primary hypothesis that classical systems can simulate quantum-inspired behaviors with sufficient fidelity, and further suggest that hybrid models offer enhanced resilience and predictive power. This work opens promising directions for quantum-inspired bioinformatics and highlights the interpretability and practical value of braid-based abstractions in classical simulations of complex systems.