stochastic-quantum-spiking-neural-networks

name	stochastic-quantum-spiking-neural-networks
title	Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
description	Novel stochastic quantum spiking (SQS) neuron model with multi-qubit quantum circuits for internal quantum memory, enabling event-driven probabilistic spike generation and hardware-friendly local learning without backpropagation.
authors	["Jiechen Chen","Bipin Rajendran","Osvaldo Simeone"]
arxiv_id	2506.21324
categories	["quantum-computing","neuromorphic-computing","spiking-neural-networks","quantum-machine-learning"]
date	2026-07-23
version	1.0

Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning

Overview

This methodology proposes a novel stochastic quantum spiking (SQS) neuron model that addresses key limitations of existing quantum spiking models. The SQS neuron uses multi-qubit quantum circuits to realize spiking units with internal quantum memory, enabling event-driven probabilistic spike generation in a single shot during inference. Networks of SQS neurons (SQSNN) can be trained via hardware-friendly local learning rules, eliminating the need for global classical backpropagation.

Core Methodology

SQS Neuron Architecture

• Multi-qubit quantum circuits: Realize internal quantum memory mechanisms beyond single-qubit classical memory
• Single-shot inference: Event-driven probabilistic spike generation without repeated measurements
• Quantum memory integration: Internal state preservation through quantum coherence and entanglement
• Hardware-friendly design: Compatible with neuromorphic integrated sensing and communications (N-ISAC)

SQS Neural Networks (SQSNN)

• Local learning rules: Hardware-friendly training without global backpropagation
• Event-driven computation: Energy consumption only upon input events
• Tensor-product composition: Exponential state space growth with qubit count
• Superposition and entanglement: Quantum states across basis states and subsystems

Training Methodology

• Local learning rule: Eliminates need for conventional backpropagation
• Parameter efficiency: Improved performance when fixing total trainable parameters
• Hardware compatibility: Designed for implementation on quantum neuromorphic platforms

Applications

Neuromorphic Integrated Sensing and Communications (N-ISAC)

• Event-driven applications requiring real-time processing
• Low-power quantum neuromorphic systems
• Hybrid quantum-classical edge computing

Time Series Processing

• Efficient temporal data processing through sparse, event-driven computation
• Quantum-enhanced pattern recognition in time series
• Real-time decision making with quantum speedup

Quantum Machine Learning

• Parameter-efficient quantum neural networks
• Hardware-native quantum ML architectures