Quantum-Inspired Algorithms for Next-Generation Intelligent Computing

Abstract

Quantum computing promises exponential computational advantages for specific problem classes, yet practical quantum hardware remains
constrained by qubit decoherence, gate error rates, and limited circuit depth. Quantum-inspired algorithms — classical methods that emulate
quantum mechanical principles such as superposition, entanglement-like correlations, and quantum interference — offer a pragmatic bridge,
delivering quantum-analogous computational advantages on classical hardware for optimisation, machine learning, and sampling tasks. This study
proposes QuIntel, a unified quantum-inspired algorithmic framework integrating quantum-inspired evolutionary optimisation (QIEO), tensor network
machine learning (TNML), and quantum-inspired Monte Carlo sampling (QIMC) for next-generation intelligent computing applications. QuIntel was
benchmarked across four domains: combinatorial optimisation (Max-Cut on graphs up to n = 5,000 nodes), deep neural network training acceleration
(ResNet-50 on ImageNet), probabilistic inference (Bayesian network with 500 variables), and portfolio optimisation (FTSE 350 asset universe).
QuIntel achieved solution quality improvements of 18.7% over classical genetic algorithms, training speed-up of 2.3× over SGD, inference accuracy
improvements of 14.2% over standard MCMC, and portfolio Sharpe ratio improvements of 0.41 over classical mean-variance optimisation. These
results establish QuIntel as a versatile and practically deployable quantum-inspired framework for intelligent computing without requiring quantum
hardware.