Compiler-assisted code generation for quantum computing: leveraging the unique properties of quantum architectures

G. G. James; A. P.  Ekong; A. U.  Unyime; A. Akpanobong; J. A. Odey; D. O.  Egete; S. Inyang; I.  Ohaeri; C. M.  Orazulume; E. Etuk; P. Okafor

doi:10.46481/jnsps.2025.2615

Authors

G. G. James
[email protected]

Department of Computing, Topfaith University, Mkpatak, Nigeria
A. P. Ekong
Department of Computer Science, Akwa Ibom State University, Ikot Akpaden, Nigeria
A. U. Unyime
Department of Computer and Robotic Education, University of Uyo, Nigeria
A. Akpanobong
Department of Computer Networks, Faculty of Science and Information Technology, UPM, Serdang, Malaysia
J. A. Odey
Department of Computer Science, University of Calabar, Calabar, Nigeria
D. O. Egete
Department of Computer Science, University of Calabar, Calabar, Nigeria
S. Inyang
Department of Computing, Topfaith University, Mkpatak, Nigeria
I. Ohaeri
Department of Computing, Topfaith University, Mkpatak, Nigeria
C. M. Orazulume
Department of Electrical Electronics Engineering, Topfaith University, Mkpatak, Nigeria
E. Etuk
Department of Computer Science, Michael Okpara University of Agriculture, Umudike, Nigeria
P. Okafor
Department of State Service, Ebonyi State Command, Abakaliki, Nigeria

Keywords:

Neuroprotection, Quantum computing, quantum architectures, quantum circuits design, quantum code optimization

Abstract

Quantum computing holds transformative potential, but its adoption is hindered by the complexity of generating efficient, hardware-specific code. This work presents a modular, extensible compiler framework that bridges high-level quantum languages with diverse hardware architectures. The framework consists of three modules: a front-end for parsing quantum code into a hardware-agnostic intermediate representation (IR), an optimization module for enhancing quantum circuits through gate synthesis, qubit routing, and error mitigation, and a back-end for generating hardware-specific instructions. Major contributions include a hardware-agnostic IR for cross-platform compatibility, optimization techniques to reduce gate complexity and noise, and hardware-specific adaptations to improve execution fidelity. A practical demonstration optimizes quantum circuits, highlighting the impact of hardware constraints. Comparative analysis of IBM Quantum and IonQ platforms underscores the role of qubit connectivity and noise resilience in algorithmic performance. This scalable framework enhances quantum software development and efficient hardware utilization.

Dimensions

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