Research Context & Prior Literature

Q-TensorFormer stands on the shoulders of foundational research in tensor network decompositions, dynamic neural computation, and KV-cache compression without claiming unearned priority over individual mathematical primitives.

1. Related Literature & Attribution

Tensor-Train Decomposition: Introduced by Oseledets (2011) (Tensor-Train Decomposition, SIAM J. Sci. Comput.) and first applied to neural network parameter compression by Novikov et al. (2015) (Tensorizing Neural Networks, NeurIPS).
Adaptive Computation in Neural Networks: Pioneered by Graves (2016) (Adaptive Computation Time for Recurrent Neural Networks), Schuster et al. (2022) (Confident Adaptive Language Modeling (CALM), NeurIPS), and Raposo et al. (2024) (Mixture-of-Depths, arXiv).
Attention Sinks & KV-Cache Pruning: Xiao et al. (2024) (Efficient Streaming Language Models with Attention Sinks, ICLR), Zhang et al. (2023) (H2O: Heavy-Hitter Oracle for Efficient Generative Inference, NeurIPS), and Liu et al. (2024) (KIVI: A Tuning-Free 2-bit KV Cache).
Quantum Feature Maps & Entanglement Metrics: Schuld & Killoran (2019) (Quantum Machine Learning in Feature Hilbert Spaces, Phys. Rev. Lett.), Meyer & Wallach (2002) (Global Entanglement in Multiparticle Systems), and Sim et al. (2019) (Expressibility and Entangling Capability of Parameterized Quantum Circuits).

2. What Q-TensorFormer Contributes

Prior works evaluate tensor network factorization, adaptive depth, attention sinks, and control loops in isolation. Q-TensorFormer's unique contribution is integrating these disparate techniques into a unified, closed-loop inference architecture:

Couples an 8D token complexity vector (z_t) directly to dynamic Tensor-Train rank slicing without runtime SVD.
Stabilizes dynamic rank selection using a dual-subgradient PID loop paired with a hysteresis deadband (τ = 0.15) to eliminate routing chattering.
Integrates multi-precision KV caching (FP16 → INT8 → INT4) with sink token preservation under a single unified memory ceiling.
Provides an open-source, empirically measured benchmark suite that candidly evaluates where tensor networks win and where they face hardware overheads.

3. Citation Record

If you use Q-TensorFormer in your research or benchmarks, please cite the project as follows:

@software{yadav2026qtensorformer,
  author       = {Premchand Yadav},
  title        = {Q-TensorFormer: Information-Value-Driven Adaptive Tensor-Network Transformer for Constrained and Edge Inference},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/Premchan369/Q-TensorFormer}
}