Synaptic weighting in single flux quantum neuromorphic computing

Schneider, Michael L.; Donnelly, Christine A.; Haygood, Ian W.; Wynn, Alex; Russek, Stephen E.; Castellanos-Beltran, Manuel; Dresselhaus, Paul D.; Hopkins, P. F.; Pufall, Matthew R.; Rippard, William H.

doi:10.1038/s41598-020-57892-0

Cited by 27 publications

(17 citation statements)

References 22 publications

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“…. Superconductor technology is a promising platform for such a solution since both superconducting quantum machine learning circuits [16][17][18][19][20][21][22] and superconducting ANNs [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] are rapidly developed nowadays.…”

Section: Figure 1amentioning

confidence: 99%

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A superconducting adiabatic neuron in a quantum regime

Bastrakova¹,

Pashin²,

Rybin³

et al. 2022

Beilstein J. Nanotechnol.

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We explore the dynamics of an adiabatic neural cell of a perceptron artificial neural network in a quantum regime. This mode of cell operation is assumed for a hybrid system of a classical neural network whose configuration is dynamically adjusted by a quantum co-processor. Analytical and numerical studies take into account non-adiabatic processes as well as dissipation, which leads to smoothing of quantum coherent oscillations. The obtained results indicate the conditions under which the neuron possesses the required sigmoid activation function.

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Section: Figure 1amentioning

confidence: 99%

“…We will use only the Born-Markov approximation and neglect the Lamb shift. Hence, the generalized master equation [50] for the density matrix in terms of the instantaneous eigenbasis in the Schrödinger picture can be written as follows: (26)…”

Section: Appendixmentioning

confidence: 99%

A superconducting adiabatic neuron in a quantum regime

Bastrakova¹,

Pashin²,

Rybin³

et al. 2022

Beilstein J. Nanotechnol.

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show abstract

“…An alternative to quantum machine learning is quantum spike-activated neural networks (SNNs)-a bio-inspired neuromorphic computation model with threshold-triggered activation similar to the natural neural firing of the brain [54]. Exemplar quantum SNN projects use Josephson junctions to study emergent behavior [55] and accelerated matrix processing via synaptic weighting [56] and superposition modeling [57].…”

Section: Quantum Eegmentioning

confidence: 99%

Quantum Neurobiology

2022

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Quantum neurobiology is concerned with potential quantum effects operating in the brain and the application of quantum information science to neuroscience problems, the latter of which is the main focus of the current paper. The human brain is fundamentally a multiscalar problem, with complex behavior spanning nine orders of magnitude-scale tiers from the atomic and cellular level to brain networks and the central nervous system. In this review, we discuss a new generation of bio-inspired quantum technologies in the emerging field of quantum neurobiology and present a novel physics-inspired theory of neural signaling (AdS/Brain (anti-de Sitter space)). Three tiers of quantum information science-directed neurobiology applications can be identified. First are those that interpret empirical data from neural imaging modalities (EEG, MRI, CT, PET scans), protein folding, and genomics with wavefunctions and quantum machine learning. Second are those that develop neural dynamics as a broad approach to quantum neurobiology, consisting of superpositioned data modeling evaluated with quantum probability, neural field theories, filamentary signaling, and quantum nanoscience. Third is neuroscience physics interpretations of foundational physics findings in the context of neurobiology. The benefit of this work is the possibility of an improved understanding of the resolution of neuropathologies such as Alzheimer’s disease.

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“…The relaxation oscillations and spiking characteristics of Josephson junctions have been of interest in different artificial neural networks ( 16 , 21 , 34 , 35 ). Furthermore, alternative devices to generate spiking neuron functionality using superconducting nanowire-based devices have also been proposed ( 18 , 24 ).…”

Section: Quantum Materials Platformsmentioning

confidence: 99%

Low-temperature emergent neuromorphic networks with correlated oxide devices

Goteti

Zaluzhnyy

Ramanathan

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Neuromorphic computing—which aims to mimic the collective and emergent behavior of the brain’s neurons, synapses, axons, and dendrites—offers an intriguing, potentially disruptive solution to society’s ever-growing computational needs. Although much progress has been made in designing circuit elements that mimic the behavior of neurons and synapses, challenges remain in designing networks of elements that feature a collective response behavior. We present simulations of networks of circuits and devices based on superconducting and Mott-insulating oxides that display a multiplicity of emergent states that depend on the spatial configuration of the network. Our proposed network designs are based on experimentally known ways of tuning the properties of these oxides using light ions. We show how neuronal and synaptic behavior can be achieved with arrays of superconducting Josephson junction loops, all within the same device. We also show how a multiplicity of synaptic states could be achieved by designing arrays of devices based on hydrogenated rare earth nickelates. Together, our results demonstrate a research platform that utilizes the collective macroscopic properties of quantum materials to mimic the emergent behavior found in biological systems.

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Synaptic weighting in single flux quantum neuromorphic computing

Cited by 27 publications

References 22 publications

A superconducting adiabatic neuron in a quantum regime

A superconducting adiabatic neuron in a quantum regime

Quantum Neurobiology

Low-temperature emergent neuromorphic networks with correlated oxide devices

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