Quantum-classical neurons upgraded with optical spin qubits for advanced computing and networking architectures

Abstract

Hardware neurons incorporating built-in memory components are critical technologies for implementing large scale neural networks that exhibit adaptive-itinerant behavior and produce biological-inspired spiking patterns that match neuroscience known operations (1). Moreover, neurons capable of full quantum information processing through their qubit states and quantum trajectory provides capability for hybrid quantum-classical operations through the level of coherence/entanglement and non-Markovianity (2,3). These attributes further contribute to solving complex quantum mechanical problems by providing a rich and diverse group for expressing quantum neural states. Upgrading these hardware neurons now with optical spin qubits excitable with pulsed laser excitation in the near infrared and designed from negatively charged silicon vacancies in semiconductor silicon carbide (4) and Neodymium rare earth ions in the superconducting Niobium/Niobium oxide film (5) that form the memory provides access to the unique spin Hamiltonian that considers electronic and nuclear interactions. In this manuscript, we experimentally measure opto-electronic quantum-classical neurons integrated with a optical spin qubit and examine how tailored neuronal spiking sequences drive the photoluminescence oscillations and modulate the quantum spin transitions. We derive a quantum model for the system that considers the interaction between the neuron and optical qubit spin and perform calculations to project how quantum computing operations are expandable across the state space in the quantum-classical neuron and spin qubit system. For example, the resulting impact on the neuron quantum states where the strength is adjustable due to dynamical memory changes. These results are critical for realizing hardware neurons with quantum optical capabilities and inspired by the existence in biology of biophotons.