A Survey of Quantum Transformers: Technical Approaches, Challenges and Outlooks

• URL: http://arxiv.org/abs/2504.03192v3
• Date: Fri, 09 May 2025 08:34:51 GMT
• Title: A Survey of Quantum Transformers: Technical Approaches, Challenges and Outlooks
• Authors: Hui Zhang, Qinglin Zhao,
• Abstract summary: Quantum Transformers combine quantum computing with the powerful Transformer model, offering new possibilities for machine learning.<n>This paper presents the first comprehensive, systematic survey of quantum Transformer models.
• Score: 2.5871385953824855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum Transformers combine quantum computing with the powerful Transformer model, offering new possibilities for machine learning by leveraging quantum features like superposition and entanglement. The surge in research since 2022, marked by diverse approaches and evolving trajectories, underscores the need for a cohesive synthesis of this interdisciplinary field. This paper presents the first comprehensive, systematic survey of quantum Transformer models. We define the research scope, focusing on quantum enhancements to Transformer components, and introduce key concepts in classical Transformers and quantum machine learning. Existing studies are categorized into Parameterized Quantum Circuits (PQC)-based and Quantum Linear Algebra (QLA)-based paradigms. PQC-based approaches are further divided into QKV-only Quantum Mapping, Quantum Pairwise Attention, Quantum Global Attention, and Quantum-assisted Acceleration, with analysis of core concepts and techniques. We evaluate computational complexity and performance, highlighting challenges such as scalability, generalization, and the barren plateau problem in PQC-based methods, and parameter trainability in QLA-based ones. Finally, we suggest future directions including low-complexity quantum Transformer variants, enhanced scalability, standardized evaluation, robust PQC architectures, and hybrid PQC-QLA designs. This survey should help researchers and practitioners quickly grasp the overall landscape of current quantum Transformer research and promote future developments in this emerging field.

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