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Current Trends in Signal Processing Original Research

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

  1. Reshika Gupta Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali
  2. Rajnish Kumar Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali
  3. Sachin Yadav Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali
  4. TinuAnand Kumar Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali

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

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