Qured: A Quantum-Inspired Symbolic Platform for Portfolio Optimization through High-Dimensional QUBO Modeling

This work introduces Qured, a Quantum-Inspired Computing 2.0 (QIC 2.0) platform that reconstructs quantum informational geometry symbolically rather than stochastically. By applying Qured’s Symbolic State-Vector Framework (SSVF) to a 200-variable Quadratic Unconstrained Binary Optimization (QUBO) problem in portfolio management, we demonstrate that quantum-class performance can be achieved entirely on classical infrastructure. The study benchmarks Qured’s symbolic solver against classical Sequential Least Squares Quadratic Programming (SLSQP) and recent quantum-inspired QUBO formulations (e.g., Lu et al., Quantum-Inspired Portfolio Optimization in the QUBO Framework, arXiv:2410.05932v1). The results show near-perfect alignment between Qured’s symbolic frontier and the classical efficient frontier, with a correlation of 0.986 and a normalized RMSE of 1.8×10−31.8\times10^{-3}1.8×10−3, all achieved without stochastic sampling or quantum hardware. This benchmark establishes Qured as a practical and accessible bridge between symbolic computation and quantum-class optimization—delivering deterministic convergence, high scalability, and hardware-independent performance. The findings highlight the emergence of symbolic quantum computation as a new paradigm for achieving quantum advantage in finance and other large-scale combinatorial optimization domains.