Quantum Supremacy in Real-Time Optimization: Benchmarking Performance Against Classical High-Performance Computing Systems

Abstract

Quantum supremacy refers to the point at which quantum computers outperform classical high-performance computing (HPC) systems in solving computations that are infeasible for conventional systems. Real-time optimization problems—such as traffic flow, financial modeling, logistics, portfolio balancing, cryptographic simulations, and supply chain design—require massive computation and near-instant decision-making, presenting a strong testbed for demonstrating true quantum advantage. This research evaluates quantum optimization performance using Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), comparing them against classical HPC techniques such as simulated annealing and gradient descent optimization. Benchmarking was conducted using real-time optimization datasets via IBM Quantum processors (127-qubit systems) and NVIDIA A100-based HPC clusters. Results indicate that quantum systems reduced computational complexity and required 54% less time to reach near-optimal solutions in large combinatorial problems involving more than 10¹⁷ possible permutations. Classical systems exhibited latency bottlenecks and exponential time growth, while quantum execution achieved polynomial-time convergence. This study validates quantum supremacy in selective optimization workloads and provides a roadmap for industrial adoption.