Reshika Gupta,
Rajnish kumar,
Sachin Yadav,
TinuAnand Kumar,
- Student, Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali, Punjab, India
- Student, Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali, Punjab, India
- Student, Department of Electronics & communication, Chandigarh Engineering College-CGC, Landran, Mohali, Punjab, India
- 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.
[This article belongs to Current Trends in Signal Processing ]
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Current Trends in Signal Processing
| Volume | 16 | |
| Issue | 01 | |
| Received | 06/11/2025 | |
| Accepted | 21/11/2025 | |
| Published | 20/03/2026 | |
| Publication Time | 134 Days |