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[2602.22255] Deep Sequence Modeling with Quantum Dynamics: Language as a Wave Function
Summary
This paper presents a novel sequence modeling framework using quantum dynamics, where language is treated as a wave function evolving under a learned Hamiltonian, offering advantages over traditional recurrent architectures.
Why It Matters
The integration of quantum dynamics into sequence modeling could significantly enhance the capabilities of natural language processing (NLP) systems. By leveraging quantum interference, the proposed framework may provide more efficient and accurate disambiguation in language tasks, representing a potential breakthrough in AI and machine learning methodologies.
Key Takeaways
- Introduces a quantum dynamics framework for sequence modeling.
- Utilizes quantum interference to enhance language processing.
- Demonstrates a theoretical advantage over traditional models in disambiguation tasks.
- Establishes a continuity equation for latent probability mass.
- Accesses pairwise phase correlations that improve model performance.
Computer Science > Machine Learning arXiv:2602.22255 (cs) [Submitted on 24 Feb 2026] Title:Deep Sequence Modeling with Quantum Dynamics: Language as a Wave Function Authors:Ahmed Nebli, Hadi Saadatdoorabi, Kevin Yam View a PDF of the paper titled Deep Sequence Modeling with Quantum Dynamics: Language as a Wave Function, by Ahmed Nebli and 2 other authors View PDF HTML (experimental) Abstract:We introduce a sequence modeling framework in which the latent state is a complex-valued wave function evolving on a finite-dimensional Hilbert space under a learned, time-dependent Hamiltonian. Unlike standard recurrent architectures that rely on gating mechanisms to suppress competing hypotheses, our framework utilizes quantum interference: the Hamiltonian steers the phases of complex amplitudes so that conflicting interpretations cancel while compatible ones reinforce. The dynamics are strictly unitary, ensuring that the state norm is preserved exactly at every time step via a Cayley (Crank--Nicolson) discretization. Token probabilities are extracted using the Born rule, a quadratic measurement operator that couples magnitudes and relative phases. Our primary theoretical contribution is a separation theorem characterizing the representational advantage of this readout: we define a family of disambiguation tasks that a complex unitary model of dimension $N$ solves exactly, but which requires a state dimension of $\Omega(N^2)$ for any real-valued orthogonal model equipped with a stand...
