The introduction of the transformer architecture in 2017 marked the most striking advancement in natural language processing. The transformer is a model architecture relying entirely on an attention mechanism to draw global dependencies between input and output. However, we believe there is a gap in our theoretical understanding of what the transformer is, and how it works physically. From a physical perspective on modern chips, such as those chips under 28nm, modern intelligent machines should be regarded as open quantum systems beyond conventional statistical systems. Thereby, in this paper, we construct physical models realizing large language models based on a transformer architecture as open quantum systems in the Fock space over the Hilbert space of tokens. Our physical models underlie the transformer architecture for large language models.
The introduction of the transformer architecture in 2017 marked the most striking advancement in natural language processing. The transformer is a model architecture relying entirely on an attention mechanism to draw global dependencies between input and output. However, we believe there is a gap in our theoretical understanding of what the transformer is, and how it works physically. From a physical perspective on modern chips, such as those chips under 28nm, modern intelligent machines should be regarded as open quantum systems beyond conventional statistical systems. Thereby, in this paper, we construct physical models realizing large language models based on a transformer architecture as open quantum systems in the Fock space over the Hilbert space of tokens. Our physical models underlie the transformer architecture for large language models.
This paper presents a mathematical formalism for generative artificial intelligence (GAI). Our starting point is an observation that a “histories” approach to physical systems agrees with the compositional nature of deep neural networks. Mathematically, we define a GAI system as a family of sequential joint probabilities associated with input texts and temporal sequences of tokens (as physical event histories). From a physical perspective on modern chips, we then construct physical models realizing GAI systems as open quantum systems. Finally, as an illustration, we construct physical models realizing large language models based on a transformer architecture as open quantum systems in the Fock space over the Hilbert space of tokens. Our physical models underlie the transformer architecture for large language models.
The convergence of quantum computing and large language models (LLMs) represents a major milestone in computational intelligence, introducing novel paradigms for information processing, complexity modeling, and scientific advancement. This survey provides a comprehensive overview of the emerging interdisciplinary field that integrates quantum mechanics with natural language processing. We begin by presenting the foundational principles of quantum computing and LLMs, then examine their integration through automated quantum code generation, hybrid transformer architectures, and quantum natural language processing (QNLP) frameworks. State-of-the-art models, such as quantum-augmented attention mechanisms and circuit embedding strategies, are evaluated across domain-specific datasets and benchmarks. We also investigate the role of LLMs as quantum research assistants, educational tools, and simulation agents, particularly in resource-constrained environments. Furthermore, the survey explores challenges related to scalability, interpretability, and quantum data encoding, and outlines future research directions-especially the vision for fully quantum-native LLMs. Our goal is to guide researchers through the current landscape and to encourage continued exploration at the intersection of quantum technologies and advanced language models.
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.
Powerful generative artificial intelligence from large language models (LLMs) harnesses extensive computational resources for inference. In this work, we investigate the transformer architecture, a key component of these models, under the lens of fault-tolerant quantum computing. We develop quantum subroutines to construct the building blocks in the transformer, including the self-attention, residual connection with layer normalization, and feed-forward network. As an important subroutine, we show how to efficiently implement the Hadamard product and element-wise functions of matrices on quantum computers. Our algorithm prepares an amplitude encoding of the transformer output, which can be measured for prediction or use in the next layer. We find that the matrix norm of the input sequence plays a dominant role in the quantum complexity. With numerical experiments on open-source LLMs, including for bio-informatics applications, we demonstrate the potential of a quantum speedup for transformer inference in practical regimes.
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.
My PhD research focused on the Transformer architecture, a powerful deep neural network model that has emerged as a cornerstone for solving complex problems in natural language processing, image analysis, signal processing, and beyond. In particular, we studied the learning dynamics of this architecture, and its application to the representation of many-body wavefunctions, the so-called Neural Quantum States. Initially, we investigated the representational capabilities of Transformers by characterizing the statistical structures that a simplified Transformer layer, utilizing the so-called factored attention, is capable of learning. Building on these results, we utilized factored attention in deep Transformers to develop an accurate ansatz for approximating the ground states of quantum many-body Hamiltonians within the variational Monte Carlo framework. In this specific application, factored attention is crucial for achieving accurate results, demonstrating superior performance compared to the standard attention mechanism used in most of the other applications of the Transformers, and in particular in natural language processing. Alongside the development of an efficient optimization method for large-scale neural networks, we achieved state-of-the-art results on the most popular benchmark in Neural Quantum States and addressed complex physical problems that are subjects of ongoing debate. Finally, we developed a framework to train Foundation Neural Quantum States, which are versatile neural network models that approximate quantum wave functions of multiple systems simultaneously, enabling accurate estimates of challenging quantities such as disorder averages and fidelity susceptibility. We envision numerous future directions for this approach, including its extension to quantum dynamics by explicitly modeling time-dependent variational states, as well as its application to the design of novel materials in fermionic systems.