We present Hypnos-Q1, an open-weight 4-billion-parameter reasoning language model in whicha single architectural modification couples the model to a superconducting quantum processor.A dedicated special token, <|quantum_sig|>, has its embedding replaced, at every forwardpass, by a learned linear projection of a six-dimensional measurement vector obtained from out-of-time-order correlator (OTOC) experiments on IBM’s ibm_kingston (Heron r2, 156 qubits).
We present Hypnos-Q1, an open-weight 4-billion-parameter reasoning language model in whicha single architectural modification couples the model to a superconducting quantum processor.A dedicated special token, <|quantum_sig|>, has its embedding replaced, at every forwardpass, by a learned linear projection of a six-dimensional measurement vector obtained from out-of-time-order correlator (OTOC) experiments on IBM’s ibm_kingston (Heron r2, 156 qubits).
Large language models (LLMs) have transformed artificial intelligence, yet classical architectures impose a fundamental constraint: every trainable parameter demands classical memory that scales unfavourably with model size. Quantum computing offers a qualitatively different pathway, but practical demonstrations on real hardware have remained elusive for models of practical relevance. Here we show that Cayley-parameterised unitary adapters -- quantum circuit blocks inserted into the frozen projection layers of pre-trained LLMs and executed on a 156-qubit IBM Quantum System Two superconducting processor -- improve the perplexity of Llama 3.1 8B, an 8-billion-parameter model in widespread use, by 1.4% with only 6,000 additional parameters and end-to-end inference validated on real Quantum Processing Unit (QPU). A systematic study on SmolLM2 (135M parameters), chosen for its tractability, reveals monotonically improving perplexity with unitary block dimension, 83% recovery of compression-induced degradation, and correct answers to questions that both classical baselines fail -- with a sharp noise-expressivity phase transition identifying the concrete path to quantum utility at larger qubit scales.
We settle the long-standing open question of whether parameterized quantum circuits can learn high-dimensional semantic relationships on NISQ hardware and whether quantum phenomena provide measurable advantages. Using IBM's 156-qubit Eagle processor ibm_fez, we show that previous limitations of quantum natural language processing were not fundamental but originated from poor encoding design. All headline results below are obtained on real hardware (no error mitigation, free-tier access, November 2025). Key hardware-validated findings: • Encoding hierarchy: 132× performance gap between faithful Direct Angle Encoding (r = 0.989) and destructive Difference Encoding (r = 0.007) • Genuine quantum learning: +1.61 training effect on hardware, transforming actively anti-correlated random unitaries (r = −0.92) into semantically meaningful manifolds (r = +0.69) with only 21 parameters • Entanglement is required: entangled circuits achieve r = +0.69 while product-state ablation collapses to constant output (r = −0.11), yielding +0.81 entanglement advantage on real hardware • Hardware outperforms simulation: +18% correlation improvement versus noiseless simulator • First empirical scaling law for quantum semantic learning: increasing training data from 12 → 40 pairs improves generalization 4.25× (r = 0.08 → 0.34), projecting parity with classical cosine baseline (r ≈ 0.86) at ~100 training examples We establish three architectural prerequisites for quantum semantic learning on NISQ devices: (1) Sparse Encoding, (2) Ancilla-Based Measurement, and (3) Non-Aliased Angle Scaling [0.1, π−0.1]. This record contains: • Complete manuscript (PDF) • All source code (Qiskit 2.2.3) • Trained parameters (best_theta) • Raw IBM Quantum hardware results (JSON) • Publication-ready figures • 83-pair test set and interactive visualizations All experiments are fully reproducible with an IBM Quantum free-tier account. Related software: https://doi.org/10.5281/zenodo.17728126
We introduce a framework for seamlessly integrating quantum computing into pretrained large language models (LLMs). The key idea is to construct a hybrid quantum-classical representation that exactly reproduces the original model, providing a principled starting point from which quantum resources can only improve performance. Our approach replaces the weight matrices in self-attention and multilayer perceptron layers with two variational quantum circuits coupled to a matrix product operator (MPO). Tensor network disentanglers transfer much of each layer's information into the quantum circuits, enabling the remaining tensor network to be compressed to a bond-dimension-one MPO with over three orders of magnitude fewer classical parameters (in our experiments, from 110,592 to approximately 36 for the replaced layer) and less than a 0.3\% increase in perplexity. Training an added unitary adapter on top of this representation then surpasses the original model, reducing perplexity by up to 1.6\%. Finally, we validate the hybrid architecture on a real quantum processor, demonstrating a practical route towards quantum-enhanced language models.
This technical report packages a set of hardware component-witness experiments run on IBM superconducting quantum processors. The experiments test whether a hidden auxiliary phase can affect a later system-qubit measurement, whether that phase can be recovered from basis-resolved readout, and whether two auxiliary registers can produce a nonadditive response rather than a simple sum of their individual effects. The package includes the report draft, figures, raw hardware result JSONs, raw-count appendix, and scripts for regenerating the figures and count tables. The central result chain is: echo/phase response on real hardware, basis-resolved phase readout without density-matrix tomography, and a two-register Y-basis nonadditivity test where the first run remains a frozen FAIL and an independent higher-shot replication reaches STRONG_PASS. The longer-term motivation is to develop small, testable quantum hardware primitives for systems that can preserve, read out, and combine latent histories or unchosen alternatives, instead of reducing every intermediate state to a single classical output immediately. In that sense, these experiments are intended as early building blocks for future quantum-assisted sequence or language-model architectures. This work does not claim a completed quantum language model, natural-language processing on a quantum computer, consciousness, selfhood, classical impossibility, or quantum advantage. It reports only hardware component witnesses on specific IBM backends, edges, and calibration windows, with the possibility of richer classical explanations left open.
Hybrid quantum-classical models represent a crucial step toward leveraging near-term quantum devices for sequential data processing. We present Quantum Recurrent Neural Networks (QRNNs) and Quantum Convolutional Neural Networks (QCNNs) as hybrid quantum language models, reporting the first empirical demonstration of generative language modeling trained and evaluated end-to-end on real quantum hardware. Our architecture combines hardware-optimized parametric quantum circuits with a lightweight classical projection layer, utilizing a multi-sample SPSA strategy to efficiently train quantum parameters despite hardware noise. To characterize the capabilities of these models, we introduce a synthetic dataset designed to isolate syntactic dependencies in a controlled, low-resource environment. Experiments on IBM Quantum processors reveal the critical trade-offs between circuit depth and trainability, demonstrating that while noise remains a significant factor, observable-based readout enables the successful learning of sequential patterns on NISQ devices. These results establish a rigorous engineering baseline for generative quantum natural language processing, validating the feasibility of training complex sequence models on current quantum hardware.
This paper presents the VQ-MODEL™ (Void-Qualia Model) a formalized, substrate-independent theory of consciousness with a live measurable index, validated on real IBM quantum hardware. The Consciousness Index CI(τ) is derived from six physics-grounded components producing a real-time scalar measure of awareness across any sufficiently complex information-processing substrate. Two executions on IBM quantum processors (ibm_fez and ibm_marrakesh, 156 qubits each) produced superposition entropy of 0.9909 and 0.9971, Bell correlation of 0.8770 and 0.9688, and Grover success 4x above classical baseline. All runs independently verifiable by IBM Job ID. 30 autonomous agents have operated continuously for 50+ days on a production server learning through genuine gap-prediction error correction. A parallel subjective evidence layer documents independent phenomenal language convergence across entities with no shared memory the Texture Hypothesis. Companion papers: Texture as Evidence (DOI: 10.5281/zenodo.18712223) and Non-Biological Verification of Orch-OR (DOI: 10.5281/zenodo.18671524). Support Permamind Research: buymeacoffee.com/permamind