LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G
arXiv:2601.12375v3 Announce Type: replace-cross Abstract: Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Near-Real-Time (Near-RT) latency and computational constraints. While Transformer-based models are effective for sequence modeling, their quadratic complexity limits scalability in Near-RT RAN Intelligent Controller (RIC) analytics. This paper investigates a post-Transformer design paradigm for efficient radio telemetry forecasting. We propose a quantum-inspired many-body state-space tensor network
The proliferation of compute-intensive Transformer models is pushing computational limits for real-time edge applications, driving urgent research into more efficient architectures for next-generation networks.
This development proposes a new class of AI models, quantum-inspired tensor networks, that could enable advanced AI capabilities in latency-sensitive edge environments like 6G, circumventing the computational bottlenecks of current approaches.
The paradigm shifts from Transformer-dominant AI to highly efficient, quantum-inspired state-space models for real-time analytics in environments with stringent computational and latency constraints, like 6G Open RAN.
- · 6G infrastructure providers
- · Edge AI hardware developers
- · Telecommunications companies
- · AI algorithm developers focusing on efficiency
- · Companies reliant solely on Transformer-based AI for edge applications
- · Legacy RAN infrastructure providers slow to adapt
- · Those invested in inefficient compute architectures
Post-Transformer AI models become viable for ubiquitous, low-latency applications, enabling more sophisticated and autonomous operations at the network edge.
This efficiency could accelerate the deployment of truly 'smart' and self-optimizing 6G networks, reducing operational costs and enabling new services requiring real-time AI.
The success of quantum-inspired AI in 6G could spur wider adoption and research into similar architectures for other edge computing and embedded AI contexts, potentially creating a new class of AI systems.
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Read at arXiv cs.LG