GPT attention measures token compatibility through dot-product similarity. This mechanism is simple, effective, and memory-efficient. But it does not explicitly model whether strong token features should reinforce or suppress one another. We introduce Q-Interference, a fully classical quantum-inspired attention mechanism for autoregressive language modeling that augments each query and key feature with an amplitude and a learned phase. The resulting attention score is phase-aware which aligned phases contribute constructively while conflicting phases contribute destructively. Although Q-Interference yields a richer interaction rule than similarity alone, a naive implementation of Q-Interference requires a large token-pair-feature interaction tensor, making it memory-intensive and often impractical. To address this limitation, we propose an exact trigonometric factorization that computes the same score using two standard matrix multiplications avoiding materialization of the large intermediate tensor. Q-Interference fits directly into a Transformer block in GPT and leaves the remainder of the model architecture and next-token prediction objective unchanged. Experiments on public benchmark datasets and baseline models show that the proposed reformulation trains stably in a controlled GPT-style setting and provides a consistent memory advantage over naive phase-aware interference attention. These results support the specific contribution of this work: an exact memory-efficient reformulation that makes phase-aware interference attention practical within a standard GPT pipeline.
GPT attention measures token compatibility through dot-product similarity. This mechanism is simple, effective, and memory-efficient. But it does not explicitly model whether strong token features should reinforce or suppress one another. We introduce Q-Interference, a fully classical quantum-inspired attention mechanism for autoregressive language modeling that augments each query and key feature with an amplitude and a learned phase. The resulting attention score is phase-aware which aligned phases contribute constructively while conflicting phases contribute destructively. Although Q-Interference yields a richer interaction rule than similarity alone, a naive implementation of Q-Interference requires a large token-pair-feature interaction tensor, making it memory-intensive and often impractical. To address this limitation, we propose an exact trigonometric factorization that computes the same score using two standard matrix multiplications avoiding materialization of the large intermediate tensor. Q-Interference fits directly into a Transformer block in GPT and leaves the remainder of the model architecture and next-token prediction objective unchanged. Experiments on public benchmark datasets and baseline models show that the proposed reformulation trains stably in a controlled GPT-style setting and provides a consistent memory advantage over naive phase-aware interference attention. These results support the specific contribution of this work: an exact memory-efficient reformulation that makes phase-aware interference attention practical within a standard GPT pipeline.
Recent advances in Natural Language Processing have been predominantly driven by transformer-based architectures, which rely heavily on self-attention mechanisms to model relationships between tokens in a sequence. Similarly, the field of Quantum Natural Language Processing, which seeks to leverage quantum principles to address challenges in language understanding and generation tasks, has seen the recent development of quantum self-attention mechanisms. We propose a classical quantum-inspired self-attention (QISA) mechanism and integrate it into the full autoregressive language modeling pipeline of GPT-1. To the best of our knowledge, this is the first integration of this kind, as previous quantum self-attention mechanisms have been primarily tested on text classification. In our experiments, QISA achieves better performance when compared to standard self-attention on the metrics character error rate (15.5× better), word error rate ($4.7 \times $) and cross-entropy loss (13×). This is achieved while only requiring a $ 2.6\times$ longer inference time.
Recent advances in quantum computing have opened new pathways for enhancing deep learning architectures, particularly in domains characterized by high-dimensional and context-rich data such as natural language processing (NLP). In this work, we present a hybrid classical-quantum Transformer model that integrates a quantum-enhanced attention mechanism into the standard classical architecture. By embedding token representations into a quantum Hilbert space via parameterized variational circuits and exploiting entanglement-aware kernel similarities, the model captures complex semantic relationships beyond the reach of conventional dot-product attention. We demonstrate the effectiveness of this approach across diverse NLP benchmarks, showing improvements in both efficiency and representational capacity. The results section reveal that the quantum attention layer yields globally coherent attention maps and more separable latent features, while requiring comparatively fewer parameters than classical counterparts. These findings highlight the potential of quantum-classical hybrid models to serve as a powerful and resource-efficient alternative to existing attention mechanisms in NLP.
This working paper introduces the Quantum Interference Transformer (QIT), a sequence model in which token relationships are resolved by constructive and destructive interference in a parameterised quantum circuit, rather than by classical dot-product attention. We present QIT-0, a minimal PennyLane/PyTorch prototype. Tokens are encoded as rotation angles, cross-token correlations are seeded by a ring CNOT ladder, a learned strongly-entangling unitary acts as the attention operator, and Pauli-Z expectation values are decoded by a linear layer. On a 4-bit parity memorisation benchmark (16 inputs; 5 random seeds), QIT-0 (78 parameters) reaches 100% accuracy in 6.2 ± 1.7 epochs. Classical baselines with more parameters converge more slowly or fail within the epoch budget. Additional experiments include ablations, a first-token negative control, and related structured tasks. This is an independent technical report, not a peer-reviewed publication. We do not claim general quantum advantage for machine learning. Code: https://github.com/MihailStancescu/qit
The success of the self-attention mechanism in classical machine learning models has inspired the development of quantum analogs aimed at reducing the computational overhead. Self-attention integrates learnable query and key matrices to calculate attention scores between all pairs of tokens in a sequence. These scores are then multiplied by a learnable value matrix to obtain the output self-attention matrix, enabling the model to effectively capture long-range dependencies within the input sequence. Here, we propose a hybrid quantum-classical self-attention mechanism as part of a transformer decoder, the architecture underlying large language models (LLMs). To demonstrate its utility in chemistry, we train this model on the QM9 dataset for conditional generation, using SMILES strings as input, each labeled with a set of physicochemical properties that serve as conditions during inference. Our theoretical analysis shows that the time complexity of the query-key dot product is reduced from O ( n 2 d ) in a classical model to O ( n 2 log d ) in our quantum model, where n and d represent the sequence length and the embedding dimension, respectively. We perform simulations using NVIDIA’s CUDA-Q platform, which is designed for efficient GPU scalability. This work provides a promising avenue for quantum-enhanced natural language processing (NLP).
Quantum-Inspired Transformers are a new class of approaches for natural language processing (NLP) that use principles inspired by physical quantum mechanics to encode and process linguistic information. Paper aims to improve both scalability and efficiency for large-scale NLP tasks in the next generation of artificial intelligence (AI). However, traditional transformers can struggle to encode complex semantic relationships across long sequences and can require high levels of compute resources, hence limiting scalability for large datasets and multilingual tasks. In addition, traditional embeddings do not represent multiple contextual meanings simultaneously, which decreases model expressiveness. To address these challenges and maximize the exploration of potential quantum effects in AI, this paper presents a Quantum State Embedding Attention (QSEA) framework that encodes tokens as quantum inspired states with complex-valued amplitudes. Attention, which is based on entangled states, is meant to efficiently capture long-range dependencies while reducing compute and increasing scalability, all without losing a rich understanding of context. Whereas multilingual translation needs to encode both the source similarity and the target language within a smaller semantic representation space. Our experimentation shows that QSEA consistently provides increased accuracy in decoding, measures attention across long sequences, and yields enduring scalability over large multilingual datasets. The evidence demonstrates the potential for QSEA to be the new layer in the future of AI systems, a layer that is scalable and a highperforming NLP application.
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.