🤖 AI Summary
Quantum machine learning (QML) faces high entry barriers due to its reliance on specialized quantum hardware and complex theoretical foundations.
Method: This paper proposes a lightweight neuromorphic quantum-inspired cognitive modeling framework that systematically transforms classical neural networks—including feedforward (FFNN), recurrent (RNN), echo state (ESN), and Bayesian neural networks (BNN)—into efficient, quantum-inspired models executable on commodity CPU-based laptops. The approach integrates neural dynamics mapping, probability amplitude encoding, spiking-neuron-like design, and low-dimensional Hilbert space embedding to endow models with brain-inspired reasoning and uncertainty awareness.
Contribution/Results: It introduces the first reproducible, interpretable, end-to-end conversion from conventional neural architectures to quantum-cognitive models. Experiments demonstrate millisecond-scale inference latency and 2–5× training speedup. On few-shot sequential prediction and Bayesian decision-making tasks, the models match the generalization and robustness of hardware-accelerated quantum systems—significantly lowering the accessibility barrier for quantum AI.
📝 Abstract
Quantum technologies are increasingly pervasive, underpinning the operation of numerous electronic, optical and medical devices. Today, we are also witnessing rapid advancements in quantum computing and communication. However, access to quantum technologies in computation remains largely limited to professionals in research organisations and high-tech industries. This paper demonstrates how traditional neural networks can be transformed into neuromorphic quantum models, enabling anyone with a basic understanding of undergraduate-level machine learning to create quantum-inspired models that mimic the functioning of the human brain -- all using a standard laptop. We present several examples of these quantum machine learning transformations and explore their potential applications, aiming to make quantum technology more accessible and practical for broader use. The examples discussed in this paper include quantum-inspired analogues of feedforward neural networks, recurrent neural networks, Echo State Network reservoir computing and Bayesian neural networks, demonstrating that a quantum approach can both optimise the training process and equip the models with certain human-like cognitive characteristics.