Bridging Brain-Inspired Learning and Quantum Reasoning for Future AGI Systems

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 | 01 | Page :
    By

    Reshika Gupta,

  • Rajnish kumar,

  • Sachin Yadav,

  • TinuAnand Kumar,

  1. Student, Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali, Punjab, India
  2. Student, Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali, Punjab, India
  3. Student, Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali, Punjab, India
  4. Student, Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali, Punjab, India

Abstract

This research paper presents a novel neuromorphic–quantum hybrid computing framework envisioned to advance intelligent systems toward artificial general intelligence. The architecture integrates brain-inspired spiking networks for adaptive, energy-efficient learning with quantum processors for non-classical optimization and reasoning. A shared synaptic–quantum memory layer enables dual information representation, while neuromorphic adaptive controllers provide real-time stabilization of noisy quantum circuits. While quantum processors offer features like superposition- enabled exploration and entanglement-based correlations that are unavailable to classical systems, neuromorphic components offer event-driven processing, continuous learning, and resilience to uncertainty. Cross-domain learning, state transfer, and hybrid memory consolidation are supported by the introduction of a shared synaptic–quantum memory layer, which allows dual information representation across spikes and qubits. Neuromorphic adaptive controllers are used for real-time quantum circuit monitoring, feedback, and stabilization in order to mitigate the intrinsic noise and instability of near-term quantum hardware. At the algorithmic level, the study proposes spiking–quantum hybrid models that integrate asynchronous sensory encoding with quantum-enhanced reasoning and feedback-driven learning dynamics, enabling efficient interaction between perception, cognition, and decision-making At the algorithmic level, spiking–quantum hybrid models are proposed, combining event-driven sensory encoding with quantum-enhanced reasoning and feedback-driven learning. On the system scale, the framework introduces an edge–cloud integration strategy, allowing local neuromorphic preprocessing and global quantum inference to operate in synergy. This multi-level innovation establishes a forward-looking pathway where spikes and qubits converge to form scalable, resilient, and human-like intelligence. The proposed vision positions neuromorphic–quantum convergence as a foundational step toward future AGI architectures.

Keywords: Neuromorphic Computing, Quantum Computing, Hybrid Intelligence, Spiking–Quantum Algorithms, Edge–Cloud Integration, Artificial General Intelligence.

How to cite this article:
Reshika Gupta, Rajnish kumar, Sachin Yadav, TinuAnand Kumar. Bridging Brain-Inspired Learning and Quantum Reasoning for Future AGI Systems. Current Trends in Signal Processing. 2026; 17(01):-.
How to cite this URL:
Reshika Gupta, Rajnish kumar, Sachin Yadav, TinuAnand Kumar. Bridging Brain-Inspired Learning and Quantum Reasoning for Future AGI Systems. Current Trends in Signal Processing. 2026; 17(01):-. Available from: https://journals.stmjournals.com/ctsp/article=2026/view=238963


References

[1] Mead C. Neuromorphic electronic systems. Proceedings of the IEEE. 2002 Aug 6;78(10):1629-36.

[2] Davies M, Srinivasa N, Lin TH, Chinya G, Cao Y, Choday SH, Dimou G, Joshi P, Imam N, Jain S, Liao Y. Loihi: A neuromorphic manycore processor with on-chip learning. Ieee Micro. 2018 Jan 16;38(1):82-99.

[3] Nielsen MA, Chuang IL. Quantum computation and quantum information. Cambridge university press; 2010 Dec 9.

[4] Arute F, Arya K, Babbush R, Bacon D, Bardin JC, Barends R, Biswas R, Boixo S, Brandao FG, Buell DA, Burkett B. Quantum supremacy using a programmable superconducting processor. Nature. 2019 Oct 24;574(7779):505-10.

[5] Furber S. Large-scale neuromorphic computing systems. Journal of neural engineering. 2016 Aug 16;13(5):051001.

[6] Preskill J. Quantum computing in the NISQ era and beyond. Quantum. 2018 Aug 6;2:79.

[7] Du W, Liu S, Zhang X. A quatum inspired neural network for geometric modeling. arXiv preprint arXiv:2401.01801. 2024 Jan 3.

[8] Indiveri G, Liu SC. Memory and information processing in neuromorphic systems. Proceedings of the IEEE. 2015 Jul 15;103(8):1379-97.

[9] Merolla, P. A. et al. (2014). A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, Vol. 345, No. 6197, pp. 668-673. DOI: 10.1126/science.1254642

[10] Quantum IB. IBM quantum roadmap. Retrieved September. 2023;20:2023.

[11] Wright K, Beck KM, Debnath S, Amini JM, Nam Y, Grzesiak N, Chen JS, Pisenti NC, Chmielewski M, Collins C, Hudek KM. Benchmarking an 11-qubit quantum computer. Nature communications. 2019 Nov 29;10(1):5464.

[12] Bharti K, Cervera-Lierta A, Kyaw TH, Haug T, Alperin-Lea S, Anand A, Degroote M, Heimonen H, Kottmann JS, Menke T, Mok WK. Noisy intermediate-scale quantum algorithms. Reviews of Modern Physics. 2022 Jan 1;94(1):015004.

[13] Backus J. Can programming be liberated from the von Neumann style? A functional style and its algebra of programs. Communications of the ACM. 1978 Aug 1;21(8):613-41.

[14] Hennessy JL, Patterson DA. Computer architecture: a quantitative approach. Elsevier; 2011 Oct 7.

[15] Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S. Quantum machine learning. Nature. 2017 Sep 14;549(7671):195-202.

[16] Pospieszynski PF. Modeling Intelligence as Trajectories in Complex Space: A Quantum-Inspired Approach to AGI. InInternational Conference on Artificial General Intelligence 2025 Aug 7 (pp. 109-124). Cham: Springer Nature Switzerland.

Ahead of Print Subscription Original Research
Volume 17
01
Received 06/11/2025
Accepted 21/11/2025
Published 20/03/2026
Publication Time 134 Days
61

Login


My IP

PlumX Metrics