Advancing quantum-classical hybrid workflows : algorithm selection, circuit generation, and optimization for NISQ processors (要旨)

マオ, イーカイ, Mao, Yikai, 毛, 翊愷

マオ, イーカイ, Mao, Yikai, 毛, 翊愷

Xiaozhen Ge, Re-Bing Wu, HERSCHEL A. RABITZ

Uman Khalid, Usama Inam Paracha, Syed Muhammad Abuzar Rizvi +1 more

Complex optimization problems, such as traffic routing and electric vehicle (EV) charging scheduling, are becoming increasingly challenging for intelligent transportation systems (ITSs), in particular as computational resources are limited and network conditions evolve frequently. This paper explores a quantum computing approach to address these issues by proposing a hybrid quantum-classical (HQC) workflow that leverages the variational quantum eigensolver (VQE), an algorithm particularly well suited for execution on noisy intermediate-scale quantum (NISQ) hardware. To this end, the EV charging scheduling and traffic routing problems are both reformulated as binary optimization problems and then encoded into Ising Hamiltonians. Within each VQE iteration, a parametrized quantum circuit (PQC) is prepared and measured on the quantum processor to evaluate the Hamiltonian’s expectation value, while a classical optimizer—such as COBYLA, SPSA, Adam, or RMSProp—updates the circuit parameters until convergence. In order to find optimal or nearly optimal solutions, VQE uses PQCs in combination with classical optimization algorithms to iteratively minimize the problem Hamiltonian. Simulation results exhibit that the VQE-based method increases the efficiency of EV charging coordination and improves route selection performance. These results demonstrate how quantum computing will potentially advance optimization algorithms for next-generation ITSs, representing a practical step toward quantum-assisted mobility solutions.

LAU WEI ZHONG JONATHAN

Akshat Verma, Raj Basnet, Bina Kotiyal +2 more

This paper presents a hybrid computational framework that integrates classical and quantum optimization techniques to improve the efficiency of Huffman encoding and string-matching algorithms.On the classical side, performance is enhanced through SIMD (Single Instruction, Multiple Data) parallelism, bitlevel operations, and cache-aware data structures.On the quantum side, we investigate Grover-based string search using amplitude amplification, approximate reflection operators, and entropy-aware circuit design tailored for Noisy Intermediate-Scale Quantum (NISQ) constraints.The primary contribution of this work is a hybrid visualization framework that synchronizes classical algorithmic flowcharts with real-time quantum circuit representations, enabling unified debugging, execution tracing, and performance analysis across both domains.Experimental evaluations conducted using optimized classical implementations and quantum simulators with noise models demonstrate up to a 65% reduction in runtime for quantum-enhanced string search and over a 30% speedup in classical SIMD-accelerated Huffman encoding.The proposed quantum optimizations maintain an accuracy above 97% while reducing circuit depth, indicating practical feasibility under current NISQ limitations.By improving both algorithmic performance and developer interpretability, this hybrid framework serves as an effective tool for algorithm design, analysis, and education.The results highlight the potential of tightly coupled classical-quantum workflows and motivate further research into scalable, NISQ-compatible hybrid algorithm development.