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[2506.21324] Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
Summary
This paper presents a novel stochastic quantum spiking neuron model that integrates quantum memory and local learning, enhancing the efficiency of neural networks in processing time series data.
Why It Matters
The integration of neuromorphic and quantum computing could revolutionize artificial intelligence by improving computational efficiency and energy consumption. This research addresses limitations in existing quantum spiking models, potentially leading to more effective AI systems.
Key Takeaways
- Introduces a stochastic quantum spiking (SQS) neuron model with internal quantum memory.
- Eliminates the need for global classical backpropagation in training neural networks.
- Demonstrates improved performance over previous quantum spiking models and classical counterparts.
- Utilizes event-driven probabilistic spike generation for efficient inference.
- Addresses key challenges in hybrid quantum-neuromorphic computing.
Computer Science > Neural and Evolutionary Computing arXiv:2506.21324 (cs) [Submitted on 26 Jun 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning Authors:Jiechen Chen, Bipin Rajendran, Osvaldo Simeone View a PDF of the paper titled Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning, by Jiechen Chen and 2 other authors View PDF HTML (experimental) Abstract:Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at processing time series data efficiently through sparse, event-driven computation, consuming energy only upon input events. Quantum computing, on the other hand, operates on state spaces that grow exponentially in dimension with the number of qubits -- as a consequence of tensor-product composition -- with quantum states admitting superposition across basis states and entanglement between subsystems. Hybrid approaches combining these paradigms have begun to show potential, but existing quantum spiking models have important limitations. Notably, they implement classical memory mechanisms on single qubits, requiring repeated measurements to estimate firing probabilities, while relying on conventional backpropagation for training. In this paper, we propose a novel stochastic quantum spiking (SQ...
