We propose a compact NISQ-compatible quantum transformer architecture for synthetic QNLP sequence modelling. The model preserves the autoregressive next-token interface of a classical transformer, but replaces attention and feed-forward sublayers with variational quantum encoder blocks, connector circuits, decoder blocks and a direct two-qubit measurement readout. Token contexts are angle-encoded into small quantum registers, processed by parallel variational heads and encoder integration circuits and conditioned through decoder ancillae to produce a distribution over a four-token vocabulary. We evaluate several architecture variants on deterministic and lexicographic grammar-generation tasks against a compact classical transformer baseline. The quantum models are trainable end-to-end and learn nontrivial grammar structure, including perfect deterministic generation in individual runs and high lexicographic validity in the strongest variant. The classical baseline remains more accurate and stable and the quantum models are sensitive to initialization. The contribution is therefore not a claim of quantum advantage, but a concrete architecture and evaluation of transformer-inspired QNLP sequence modelling under near-term quantum constraints.
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Accepted for publication in the QNLPAI 2026 proceedings (Springer Lecture Notes in Computer Science, LNCS). 10 pages, including references and appendix, 2 figures
We propose a compact NISQ-compatible quantum transformer architecture for synthetic QNLP sequence modelling. The model preserves the autoregressive next-token interface of a classical transformer, but replaces attention and feed-forward sublayers with variational quantum encoder blocks, connector circuits, decoder blocks and a direct two-qubit measurement readout. Token contexts are angle-encoded into small quantum registers, processed by parallel variational heads and encoder integration circuits and conditioned through decoder ancillae to produce a distribution over a four-token vocabulary. We evaluate several architecture variants on deterministic and lexicographic grammar-generation tasks against a compact classical transformer baseline. The quantum models are trainable end-to-end and learn nontrivial grammar structure, including perfect deterministic generation in individual runs and high lexicographic validity in the strongest variant. The classical baseline remains more accurate and stable and the quantum models are sensitive to initialization. The contribution is therefore not a claim of quantum advantage, but a concrete architecture and evaluation of transformer-inspired QNLP sequence modelling under near-term quantum constraints.
Abstract The combination of Transformer architectures with Quantum Computing has opened new directions in Quantum Natural Language Processing (QNLP) by leveraging the Transformer’s representational capacity together with the computational parallelism and high-dimensional feature space of quantum systems. However, existing approaches remain constrained by partial designs and limited integration, hindering scalability and trainability. This study introduces FitQT—a novel, fully integrated, and trainable quantum transformer that unifies three essential components: (1) a quantum multi-head attention mechanism employing a scaled Gaussian kernel, (2) a quantum feed-forward network, and (3) quantum residual connections. The residual connections stabilize gradient flow and facilitate the training of deeper quantum circuits, while the attention and feed-forward components jointly enhance representational capacity. Evaluations on sentiment analysis datasets demonstrate FitQT’s effectiveness. On the IMDb dataset, FitQT improves accuracy by 14.89% over the baseline Quantum Self-Attention Network and achieves competitive task-level performance relative to BERT-Base, despite being trained from scratch without large-scale pretraining, while requiring only 2,595 parameters and substantially reduced training time. Moreover, FitQT achieves state-of-the-art performance compared to prior quantum-based transformer architectures such as QSANN and QMSAN. These results highlight the importance of incorporating classical deep learning principles into quantum architectures and provide a practical foundation for scalable, resource-efficient QNLP systems suitable for the noisy intermediate-scale quantum era.
Transformers have become the dominant architecture for sequence modeling in natural language processing; however, their effectiveness critically depends on how positional information is encoded. Conventional positional encodings, while effective, may have limited structural flexibility for capturing complex global sequence relationships. Recent quantum-inspired approaches have sought to address this limitation, yet many either oversimplify quantum principles or introduce substantial computational or hardware overhead. We introduce a novel Quantum Fourier Transform (QFT)-inspired positional encoding scheme for transformers, motivated by the structured frequency representation of the QFT. Unlike prior approaches that either emulate quantum operations superficially or require complex circuit constructions, the proposed method provides a learnable hybrid encoding that preserves quantum-inspired structure while remaining aligned with hardware-efficient circuit primitives and structurally compatible with future near-term quantum implementations. Experiments on WikiText-103 indicate that the proposed encoding achieves competitive perplexity, improved robustness to input scrambling, and stable training behavior relative to alternative quantum-inspired baselines under the evaluated settings. Preliminary circuit-level simulations further suggest favorable noise resilience of the associated encoding primitives. These findings support the potential utility of incorporating quantum-inspired design principles into deep learning architectures and provide a foundation for future exploration at the interface of quantum computing and transformer-based natural language processing (NLP).
We provide conceptual and mathematical foundations for near-term quantum natural language processing (QNLP), and do so in quantum computer scientist friendly terms. We opted for an expository presentation style, and provide references for supporting empirical evidence and formal statements concerning mathematical generality. We recall how the quantum model for natural language that we employ canonically combines linguistic meanings with rich linguistic structure, most notably grammar. In particular, the fact that it takes a quantum-like model to combine meaning and structure, establishes QNLP as quantum-native, on par with simulation of quantum systems. Moreover, the now leading Noisy Intermediate-Scale Quantum (NISQ) paradigm for encoding classical data on quantum hardware, variational quantum circuits, makes NISQ exceptionally QNLP-friendly: linguistic structure can be encoded as a free lunch, in contrast to the apparently exponentially expensive classical encoding of grammar. Quantum speed-up for QNLP tasks has already been established in previous work with Will Zeng. Here we provide a broader range of tasks which all enjoy the same advantage. Diagrammatic reasoning is at the heart of QNLP. Firstly, the quantum model interprets language as quantum processes via the diagrammatic formalism of categorical quantum mechanics. Secondly, these diagrams are via ZX-calculus translated into quantum circuits. Parameterisations of meanings then become the circuit variables to be learned. Our encoding of linguistic structure within quantum circuits also embodies a novel approach for establishing word-meanings that goes beyond the current standards in mainstream AI, by placing linguistic structure at the heart of Wittgenstein's meaning-is-context.
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.
Transformer architectures have driven advances in natural language processing, powering systems from question answering to creative text generation. Yet as model sizes have grown from hundreds of millions to hundreds of billions of parameters, the computational cost of these systems has become prohibitive, placing advanced language technology out of reach for many researchers. In this work, we introduce QuantumGPTMini, a hybrid quantum-classical transformer with just over one million classical parameters that leverages lightweight quantum circuits to perform its core attention and feed-forward computations. By encoding token embeddings both in conventional vectors and in the amplitudes of small variational quantum circuits, our model replaces the conventional softmax attention mechanism with an entanglement-driven alternative. When trained on the Wikipedia-2 corpus using PennyLane's lightning.qubit simulator on NVIDIA A100 GPUs, QuantumGPTMini matched the perplexity of a GPT-2 Small baseline while demonstrating superior pattern recognition and symbolic reasoning behaviors. We have made this system publicly accessible through a FastAPI endpoint and a Next.js web interface. While our initial results underscore the potential of quantum-augmented language models, they also highlight challenges in moving beyond simulation, including circuit depth limitations, decoherence, and the high overhead of parameter-shift gradient estimation.
Quantum machine learning offers a compelling representational perspective: angle-encoded states inhabit Hilbert spaces in which periodic similarities and interactions can be expressed naturally. Realizing this perspective for visual recognition remains difficult, however, because present quantum neural networks are constrained by limited qubit counts, costly circuit simulation and measurement, noise, and unstable optimization on noisy intermediate-scale quantum devices. We investigate whether useful structural ideas from quantum models can instead be realized as scalable classical Transformer operations. We introduce QiT, a Quantum-inspired Transformer for vision tasks with three components: (i) angle-inspired encoding that maps image tokens to learned trigonometric Hilbert-space features analogous to quantum rotation-based state encoding; (ii) self-attention over these periodic features, inducing a classical cosine kernel approximated to quantum fidelity kernels; and (iii) gated multiplicative emulation, a trainable classical surrogate for interaction terms found in variational circuits. All components are differentiable tensor operations, so QiT claims neither quantum computation nor quantum speedup and retains the O(N2D) attention complexity of a standard Vision Transformer. Across image-classification benchmarks, QiT is competitive with a matched classical Transformer while avoiding the severe runtime cost observed for a small simulated quantum Transformer. QiT-B reaches 78.3\% ImageNet-1K top-1 accuracy with 45.7M parameters and 11.5 GFLOPs. These results position QiT as a scalable baseline for isolating and evaluating quantum-motivated inductive biases in visual recognition.