Quantum Cognition and Quantum Zeno Effect for Neuromorphic Reservoir Computing

Luke C. G. Govia, William D. Kalfus, Guilhem J. Ribeill +3 more

Ivan S. Maksymov

H. Fearn, Willis E. Lamb

Maksimovic, Milan, Ivan S. Maksymov

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.

Christoph Räth, Joel Steinegger, Fabian Fischbach

The application of AI-based forecasting methods has led to great progress in the prediction of complex systems. Among the AI-methods being used reservoir computing (RC) turns out to be so far the most promising approach as it combines superior prediction results with little CPU-needs for training. In Quantum Reservoir Computing (QRC) the reservoir that is commonly a random network, is replaced by a system of entangled qubits. Using a spin-network as ad hoc quantum reservoir it was demonstrated that a NARMA process can be well predicted using as few as 10 qubits [1]. Our recent first results suggest that even a 4-6 qubit system may be sufficient to predict the Lorenz-system as well as with “conventional” RC [2]. In the forthcoming research project “Neuromorphic Quantum Computing (NeMoQC)” within the framework of the DLR Quantum Computing Initiative (QCI) we will now investigate systematically which quantum reservoirs are best suited for prediction and optimization tasks. In a software/hardware codesign we will seek for hardware realizations of QRC. Further, these newly developed QC-based forecasting and optimization methods are to be utilized in real world applications. Industrial partners for both the hardware design and the applications in real world use cases are welcome to join our project. [1] R. Martinez-Pena et al., PRL, 127, 100502 (2021) [2] J. Steinegger, Masterthesis LMU (ongoing)

Vladimir G. Ivancevic, Tijana T. Ivancevic