International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Novel Quantum Computing Architectures for Enhanced Computational Efficiency: A Hybrid Classical-Quantum Approach |
|---|---|
| ABSTRACT | Quantum computing offers the potential for exponential or polynomial speedups in problems such as factoring, unstructured search, quantum chemistry, and combinatorial optimization, but present devices operate in the noisy intermediate-scale quantum (NISQ) regime and are constrained by decoherence, gate infidelities, and limited qubit connectivity. Hybrid variational algorithms, such as the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA), have emerged as promising candidates for near-term advantage, yet their practical performance is highly sensitive to circuit depth, qubit mapping strategies, and hardware noise. This paper proposes a novel hybrid classical–quantum architecture that combines algorithm-oriented qubit mapping, hardware-aware variational ansätze, and layered error-mitigation techniques tailored to a 127-qubit superconducting processor. The architecture derives a weighted interaction graph from the problem Hamiltonian, embeds it into low-error sub topologies of the physical device, and co-optimizes circuit design with a noise-aware classical optimization loop and measurement, zero-noise extrapolation, and probabilistic error-cancellation mitigation. Numerical and hardware-backed evaluations on molecular electronic-structure and Max-Cut benchmarks demonstrate depth reductions of approximately 30–50%, effective two-qubit gate-fidelity improvements of around 30–35%, and error reductions on expectation values on the order of 60–65%, enabling chemically accurate VQE results and improved QAOA approximation ratios on NISQ hardware. These findings indicate that co-design of topology-aware compilation, hybrid optimization, and mitigation can significantly extend the usable capability of current quantum processors and provide a blueprint for scalable near-term architectures. |
| AUTHOR | Y. ASHA CHANDRA, ABBADASARI SANGEETA Lecturer, Department of ECE, Sir C R Reddy Polytechnic College, Eluru, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401032 |
| pdf/32_Novel Quantum Computing Architectures for Enhanced Computational Efficiency A Hybrid Classical-Quantum Approach.pdf | |
| KEYWORDS | |
| References | 1. Nielsen, M. A., & Chuang, I. L. Quantum Computation and Quantum Information. Cambridge University Press. [Foundational concepts and models of quantum computation]. 2. Shor, P. W. “Algorithms for quantum computation: discrete logarithms and factoring.” Proceedings 35th Annual Symposium on Foundations of Computer Science (FOCS), 1994. [Seminal quantum factoring algorithm]. 3. Preskill, J. “Quantum Computing in the NISQ era and beyond.” Quantum 2, 79 (2018). [Defines NISQ and its challenges]. 4. Ji, Y., Chen, X., Polian, I., & Ban, Y. “Algorithm-oriented qubit mapping for variational quantum algorithms.” Physical Review Applied 23, 034022 (2025). [AOQMAP; depth and success-probability improvements]. 5. Lubinski, T. et al. “Advancing hybrid quantum–classical computation with real devices.” Frontiers in Physics 10, 940293 (2022). [Hybrid architectures and embedded classical control]. 6. Schmale, T. et al. “Real-time hybrid quantum–classical computations for near-term devices.” arXiv:2303.01282 (2023). [Real-time hybrid control concepts]. 7. Cerezo, M. et al. “Variational Quantum Algorithms.” Nature Reviews Physics (reviewed in VQE survey) and “The Variational Quantum Eigensolver: a review of methods and best practices.” arXiv:2111.05176 (2021). [Comprehensive VQE/VQA review]. 8. Miao, J. et al. “Benchmarking variational quantum eigensolvers for entanglement detection in many-body Hamiltonian ground states.” AVS Quantum Science 7, 023802 (2025). [Benchmarking VQE circuits and ansätze]. 9. Peruzzo, A. et al. “A variational eigenvalue solver on a photonic quantum processor.” Nature Communications 5, 4213 (2014). [Original VQE demonstration; summarized by later reviews]. 10. Schuch, N. et al. “Quantum computing: foundations, algorithms, and applications.” Frontiers in Quantum Science and Technology (2025). [Recent overview of VQAs and applications]. |
