Bridging Classical and Quantum Intelligence: Quantum-Inspired Computing in Financial Big Data Systems

The convergence of quantum principles with classical artificial intelligence is reshaping computational paradigms in financial analytics. This paper presents a comprehensive framework for quantum-inspired computing, a class of algorithms that emulate quantum mechanics behaviors such as superposition, tunneling, and entanglement on classical hardware to address high-dimensional optimization and streaming challenges in finance. By reformulating problems like portfolio optimization, risk scoring, and derivative pricing within Quadratic Unconstrained Binary Optimization (QUBO) and tensor-network formulations, these methods achieve near-quantum performance using high-performance classical architectures. The proposed hybrid reference architecture integrates quantum-inspired solvers with real-time data ingestion, feature engineering, and governance layers, supporting transparent, adaptive, and auditable decision systems. We demonstrate that quantum-inspired algorithms can substantially reduce computation latency, improve convergence in dynamic markets, and enhance explainability in regulated environments. While challenges remain in scalability, benchmarking, and interpretability, these algorithms provide a pragmatic bridge toward future quantum-classical ecosystems, enabling financial institutions to operationalize quantum-era intelligence within current computational infrastructures.