Hyper-Dimensional Encoding of Causal Quantum State Networks for Robust NISQ Device Simulation

**Abstract:** The limitations of current Noise Intermediate-Scale Quantum (NISQ) devices necessitate advanced simulation techniques to validate quantum algorithms and extract meaningful insights. This paper introduces a novel approach, Hyper-Dimensional Encoding of Causal Quantum State Networks (HD-CQSN), that leverages hyperdimensional computing and causal inference to create compact, scalable, and robust simulations of NISQ devices. HD-CQSN represents quantum states and their evolution through noisy channels in high-dimensional hypervectors, allowing for efficient manipulation and the rapid evaluation of algorithm performance under various noise models. We demonstrate the efficacy of this approach through simulations of the Variational Quantum Eigensolver (VQE) algorithm applied to a simplified hydrogen molecule (H₂), showcasing a 10x improvement in simulation speed and a demonstrable increase in robustness to noise compared to traditional density matrix-based methods. This framework facilitates more accurate algorithm design and optimization efforts directly applicable to extant and near-term NISQ devices, paving the path for faster and more reliable quantum computation.