Neuromorphic Computing and Quantum Machine Learning: Emerging Architectures for Next-Generation Artificial Intelligence
Abstract
ABSTRACT
Traditional Von Neumann computing architectures are experiencing severe
performance bottlenecks due to the physical limits of silicon scaling, memory
wall bandwidth constraints, and exponential power dissipation in modern
artificial intelligence (AI) workloads. In response, Neuromorphic Computing
(NC) and Quantum Machine Learning (QML) have emerged as two leading
non-Von Neumann paradigms designed to power next-generation intelligent
systems.
Neuromorphic computing mimics the biological brain's
spatiotemporal event-driven processing and synaptic plasticity, providing
ultra-low-power
execution
and
real-time
processing
capabilities.
Concurrently, Quantum Machine Learning leverages quantum mechanical
phenomena—such as superposition, entanglement, and quantum
interference—to perform complex matrix transformations and high
dimensional vector space operations with potential exponential speedups. This
review paper provides a rigorous, comprehensive analysis of NC and QML
architectures, highlighting their foundational mathematical principles,
hardware implementations, algorithmic frameworks, and convergence
opportunities. We systematically examine biological spiking neural networks
(SNNs), memristive devices, variational quantum circuits (VQCs), and
quantum neural networks (QNNs). Furthermore, we explore the emerging
paradigm of Hybrid Neuromorphic-Quantum AI (NQ-AI) systems, which
combine event-based energy efficiency with quantum computational speedups.
Finally, we establish a structured taxonomy, evaluate key performance
benchmarks, identify critical technical gaps, and outline future research
directions required to transition these computational paradigms from
theoretical models to large-scale engineering deployments.
KEYWORDS: Neuromorphic Computing, Quantum Machine Learning,
Spiking Neural Networks, Variational Quantum Circuits, Memristors, Hybrid
AI Architectures, Next-Generation Computing.
Traditional Von Neumann computing architectures are experiencing severe
performance bottlenecks due to the physical limits of silicon scaling, memory
wall bandwidth constraints, and exponential power dissipation in modern
artificial intelligence (AI) workloads. In response, Neuromorphic Computing
(NC) and Quantum Machine Learning (QML) have emerged as two leading
non-Von Neumann paradigms designed to power next-generation intelligent
systems.
Neuromorphic computing mimics the biological brain's
spatiotemporal event-driven processing and synaptic plasticity, providing
ultra-low-power
execution
and
real-time
processing
capabilities.
Concurrently, Quantum Machine Learning leverages quantum mechanical
phenomena—such as superposition, entanglement, and quantum
interference—to perform complex matrix transformations and high
dimensional vector space operations with potential exponential speedups. This
review paper provides a rigorous, comprehensive analysis of NC and QML
architectures, highlighting their foundational mathematical principles,
hardware implementations, algorithmic frameworks, and convergence
opportunities. We systematically examine biological spiking neural networks
(SNNs), memristive devices, variational quantum circuits (VQCs), and
quantum neural networks (QNNs). Furthermore, we explore the emerging
paradigm of Hybrid Neuromorphic-Quantum AI (NQ-AI) systems, which
combine event-based energy efficiency with quantum computational speedups.
Finally, we establish a structured taxonomy, evaluate key performance
benchmarks, identify critical technical gaps, and outline future research
directions required to transition these computational paradigms from
theoretical models to large-scale engineering deployments.
KEYWORDS: Neuromorphic Computing, Quantum Machine Learning,
Spiking Neural Networks, Variational Quantum Circuits, Memristors, Hybrid
AI Architectures, Next-Generation Computing.