Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 17 | 02 | Page :
    By

    Bibhu Prasad Ganthia,

  1. Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India

Abstract

Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and thermal hotspot mitigation during the design process. A thermal prediction engine continuously monitors temperature distribution across the chip and provides feedback to the learning agent for adaptive decision-making. The quantum-classical optimization layer enhances exploration capabilities, enabling faster convergence toward optimal design configurations while minimizing computational overhead. Experimental evaluations on benchmark VLSI circuits demonstrate significant improvements in power efficiency, thermal stability, and resource utilization compared with conventional EDA approaches. The framework achieves reduced peak temperature, lower leakage power, improved routing efficiency, and shorter optimization time, thereby enhancing overall chip reliability and lifespan. The proposed methodology offers a scalable and intelligent solution for future semiconductor technologies, where energy constraints and thermal management are critical design considerations. The results indicate that the integration of quantum-inspired learning with thermal-aware design automation can substantially improve the efficiency and sustainability of advanced VLSI architectures.

Keywords: Hybrid Quantum-Classical Computing; Reinforcement Learning; Electronic Design Automation (EDA); Thermal-Aware Optimization; VLSI Systems; Energy Efficiency; Chip Thermal Management; Quantum-Inspired Optimization; Semiconductor Design; Intelligent CAD Systems.

How to cite this article:
Bibhu Prasad Ganthia. Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications. Journal of Electronic Design Technology. 2026; 17(02):-.
How to cite this URL:
Bibhu Prasad Ganthia. Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications. Journal of Electronic Design Technology. 2026; 17(02):-. Available from: https://journals.stmjournals.com/joedt/article=2026/view=247327


References

  1. Antolini A, Lico A, Zavalloni F, Scarselli EF, Gnudi A, Torres ML, Canegallo R, Pasotti M. A readout scheme for PCM-based analog in-memory computing with drift compensation through reference conductance tracking. IEEE Open Journal of the Solid-State Circuits Society. 2024 Jul 25;4:69-82.
  2. Pistolesi L, Ravelli L, Glukhov A, de Gracia Herranz A, Lopez-Vallejo M, Carissimi M, Pasotti M, Rolandi PL, Redaelli A, Martín IM, Bianchi S. Differential phase change memory (PCM) cell for drift- compensated in-memory computing. IEEE Transactions on Electron Devices. 2024 Oct 25;71(12):7447-53.
  3. Yang X, Lei Y, Yu Q, Wang Q, Chen H, Song Z. Phase change memory programming circuit with improved speed. Moore and More. 2025 Jan 15;2(1):3.
  4. Syed GS, Le Gallo M, Sebastian A. Phase-change memory for in-memory computing. Chemical reviews. 2025 May 22;125(11):5163-94.
  5. Zhou W, Shen X, Yang X, Wang J, Zhang W. Fabrication and integration of photonic devices for phase- change memory and neuromorphic computing. International Journal of Extreme Manufacturing. 2024 Apr 1;6(2):022001.
  6. Viollet V, Prenat G, Al Muktash A, Popon C, Anghel L, Lecoq X. Design of a Low-Power High- Density Analog in-Memory Computing Accelerator Using 18 Nm FD-SOI Phase-Change Memory. In2026 IEEE 27th Latin American Test Symposium (LATS) 2026 Mar 17 (pp. 1-6). IEEE.
  7. Le Gallo M, Khaddam-Aljameh R, Stanisavljevic M, Vasilopoulos A, Kersting B, Dazzi M, Karunaratne G, Brändli M, Singh A, Mueller SM, Büchel J. A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference. Nature Electronics. 2023 Sep;6(9):680-93.
  8. Wu C, Deng H, Huang YS, Yu H, Takeuchi I, Ríos Ocampo CA, Li M. Freeform direct-write and rewritable photonic integrated circuits in phase-change thin films. Science Advances. 2024 Jan 5;10(1):eadk1361.
  9. Miller F, Chen R, Fröch JE, Rarick H, Geiger S, Majumdar A. Rewritable photonic integrated circuits using dielectric-assisted phase-change material waveguides. Optics Letters. 2023 Apr 26;48(9):2385-8.
  10. Wu C, Yu H, Lee S, Peng R, Takeuchi I, Li M. Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network. Nature communications. 2021 Jan 4;12(1):96.
  11. Xie H, Wang Y, Gao Z, Ganthia BP, Truong CV. Research on frequency parameter detection of frequency shifted track circuit based on nonlinear algorithm. Nonlinear Engineering. 2021 Jan 1;10(1):592-9.
  12. Gu J, Wang W, Yin R, Truong CV, Ganthia BP. Complex circuit simulation and nonlinear characteristics analysis of GaN power switching device. Nonlinear Engineering. 2021 Jan 1;10(1):555- 62.
  13. Rubavathy SJ, Venkatasubramanian R, Kumar MM, Ganthia BP, Kumar JS, Hemachandu P, Ramkumar MS. Smart grid based multiagent system in transmission sector. In2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) 2021 Sep 2 (pp. 1-5). IEEE.
  14. Priyadarshini L, Kundu S, Maharana MK, Ganthia BP. Controller design for the pitch control of an autonomous underwater vehicle. Engineering, Technology & Applied Science Research. 2022 Aug 7;12(4):8967-71.
  15. Zheng W, Mehbodniya A, Neware R, Wawale SG, Ganthia BP, Shabaz M. Modular unmanned aerial vehicle platform design: Multi-objective evolutionary system method. Computers and Electrical Engineering. 2022 Apr 1;99:107838.
  16. Ranjan S, Jaiswal S, Latif A, Das DC, Sinha N, Hussain SS, Ustun TS. Isolated and interconnected multi-area hybrid power systems: A review on control strategies. Energies. 2021 Dec 8;14(24):8276.
  17. Pellis S. Golden Fractals in Fluid Dynamics and Turbulence. Available at SSRN 5543579. 2025 Sep 28.
  18. Prasad J, Kumari MS, Srikanth G, Gopi A. Reconfigurable FPGA-Based Framework for Power- Efficient Signal Processing in AI-Assisted IoT Gateways. In2025 International Conference on Innovations and Emerging Technologies In AI & Communication Systems (IETACS) 2025 Nov 6 (pp. 1292-1297). IEEE.
  19. Baraa SM, Desa H, Mohammed KS, Al-Malaisi TA, Hussain AS, Majdi HS. Selective harmonic elimination in reduced-switch multilevel inverters for PV systems using the sparrow search algorithm. Journal of Robotics and Control (JRC). 2025 Feb 15;6(1):385-95.
Ahead of Print Subscription Review Article
Volume 17
02
Received 19/06/2026
Accepted 20/06/2026
Published 23/06/2026
Publication Time 4 Days
1

Login


My IP

PlumX Metrics