Enhancing Classical and Quantum Algorithms through Circuit Optimization and Hybrid Visualization Framework

DOI

10.2991/978-94-6239-789-7_7How to use a DOI?

Keywords

Huffman encoding; quantum string matching; SIMD optimization; Grover’s algorithm; hybrid visualization framework; NISQ computing

Abstract

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, bit-level 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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Akshat Verma
AU  - Raj Basnet
AU  - Bina Kotiyal
AU  - Amrita Tiwari
AU  - Ruchira Rawat
PY  - 2026
DA  - 2026/10/08
TI  - Enhancing Classical and Quantum Algorithms through Circuit Optimization and Hybrid Visualization Framework
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 80
EP  - 94
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_7
DO  - 10.2991/978-94-6239-789-7_7
ID  - Verma2026
ER  -