Quantum attention mechanisms integrate parameterised quantum circuits or quantum linear algebra primitives into transformer architectures, drawing on superposition and entanglement in the attention computation. The field has produced more than two dozen proposals between 2022 and 2026, spanning Parameterised Quantum Circuits (PQC) on near-term hardware, Quantum Linear Algebra (QLA) on fault-tolerant hardware, and quantum annealing. This survey contributes a refined taxonomy with an attention-semantics dimension distinguishing pairwise from holistic mechanisms; a resource-realism analysis that states the measurement-overhead cost of pairwise quantum attention in the Noisy Intermediate-Scale Quantum (NISQ) regime as a proposition under explicit assumptions, with the architecture classes falling outside those assumptions identified, organised within a four-axis design framework covering sequence and qubit scaling, circuit depth, encoding overhead, and hardware platform constraints, complementing the dedicated measurement-overhead analysis; and a critical synthesis of the empirical literature that finds parity or task-conditional modest gains rather than uniform quantum advantage across the five papers reporting parameter-matched classical baselines. Six open directions are identified for the next phase of research. The survey clarifies what the current literature does and does not establish about quantum attention’s practical advantage over efficient classical alternatives.
Quantum attention mechanisms integrate parameterised quantum circuits or quantum linear algebra primitives into transformer architectures, drawing on superposition and entanglement in the attention computation. The field has produced more than two dozen proposals between 2022 and 2026, spanning Parameterised Quantum Circuits (PQC) on near-term hardware, Quantum Linear Algebra (QLA) on fault-tolerant hardware, and quantum annealing. This survey contributes a refined taxonomy with an attention-semantics dimension distinguishing pairwise from holistic mechanisms; a resource-realism analysis that states the measurement-overhead cost of pairwise quantum attention in the Noisy Intermediate-Scale Quantum (NISQ) regime as a proposition under explicit assumptions, with the architecture classes falling outside those assumptions identified, organised within a four-axis design framework covering sequence and qubit scaling, circuit depth, encoding overhead, and hardware platform constraints, complementing the dedicated measurement-overhead analysis; and a critical synthesis of the empirical literature that finds parity or task-conditional modest gains rather than uniform quantum advantage across the five papers reporting parameter-matched classical baselines. Six open directions are identified for the next phase of research. The survey clarifies what the current literature does and does not establish about quantum attention’s practical advantage over efficient classical alternatives.
Quantum Artificial Intelligence (QAI) has emerged at the nexus of quantum computing and AI, promising to redefine computational frontiers. This survey critically synthesizes the state-of-the- art through 2024, elucidating the profound bidirectional synergy between these fields. We analyze how classical machine learning is accelerating quantum hardware control, circuit optimization, and error correction. Conversely, we assess the potential quantum advantage of algorithms, including variational and kernel-based methods, across domains such as drug discovery, financial modeling, and cybersecurity. Our analysis reveals a critical trade-of between the utility of near-term Noisy Intermediate-Scale Quantum (NISQ) devices and the long-term promise of fault-tolerant architectures. We identify fundamental obstacles to QAI’s advancement, including hardware decoherence, algorithmic barren plateaus, and data-encoding bottlenecks. While QAI’s potential is transformative, achieving practical quantum advantage requires a concerted effort to overcome these core challenges at the hardware-software interface. This work provides a roadmap for navigating the current landscape and prioritizing future research in this rapidly evolving discipline. • We introduce a bi-directional Quantum-AI framework, AI-for-QC and QC-for-AI, and provide a practical roadmap for integrating both paradigms across the full stack. • We quantify AI-enabled gains: calibration time drops from 8 hours to 2 hours, circuit depth falls by up to 42%, and ML decoders cut logical error rates by about half. • We synthesize comparative guidance on VQAs, quantum kernels, and hybrid/quantum Transformers, clarifying trainability limits and criteria for credible quantum advantage. • We deliver a clinically grounded playbook for radiology, covering small-data use cases, plug-in hybrid modules, OOD validation, fairness assessments, and TRIPOD/CONSORT-AI reporting.
Quantum Transformers integrate the representational power of classical Transformers with the computational advantages of quantum computing. Since 2022, research in this area has rapidly expanded, giving rise to diverse technical paradigms and early applications. To address the growing need for consolidation, this paper presents the first comprehensive, systematic, and in-depth survey of quantum Transformer models. First, we delineate the research scope, focusing on improving Transformer parts with quantum methods, and introduce foundational concepts in classical Transformers and quantum machine learning. Then we organize existing studies into two main paradigms: PQC-based and QLA-based, with PQC-based paradigm further divided into QKV-only Quantum Mapping, Quantum Pairwise Attention, Quantum Holistic Attention. and Quantum-Assisted Optimization, analyzing their core mechanisms and architectural traits. We also summarize empirical results that demonstrate preliminary quantum advantages, especially on small-scale tasks or resource-constrained settings. Following this, we examine key technical challenges, such as complexity-resource trade-offs, scalability and generalization limitations, and trainability issues including barren plateaus, and provide potential solutions, including quantumizing classical transformer variants with lower complexity, hybrid designs, and improved optimization strategies. Finally, we propose several promising future directions, e.g., scaling quantum modules into large architectures, applying quantum Transformers to domains with inherently quantum data (e.g., physics, chemistry), and developing theory-driven designs grounded in quantum information science. This survey will help researchers and practitioners quickly grasp the overall landscape of current quantum Transformer research and promote future developments in this emerging field.
Abstract Quantum machine learning (QML) has attracted growing interest because quantum states and circuits may provide new representational primitives for learning, yet progress remains constrained by noisy intermediate-scale quantum (NISQ) hardware and heterogeneous evaluation practices. This article presents a critical review of recent QML architectures and representative empirical and theoretical studies, with emphasis on work published since 2022 alongside selected seminal references. To improve comparability, “quantum advantage” is assessed in three distinct senses: computational scaling under stated input-access assumptions, empirical predictive performance in the benchmark setting considered, and resource efficiency at a fixed budget accounting for both classical compute and quantum execution, including shot costs. Organized by architecture family and application domain, the review analyzes how baseline strength, hyperparameter optimization, preprocessing, and budget reporting shape conclusions. Overall, quantum feature maps and kernels, as well as variational (often hybrid) pipelines, can be competitive on selected small- to medium-scale benchmarks, but robust end-to-end evidence for a general practical advantage remains limited. Reported gains are often sensitive to budget matching and may be dominated by measurement overheads, dataset and preprocessing effects, and training instabilities. The review concludes that credible progress requires standardized benchmarking, transparent end-to-end resource accounting, and hardware-centered evaluations that test scalability and robustness beyond toy regimes.
Quantum computing has progressed from early demonstrations across superconducting, trapped ion, photonic, and spin-based platforms to increasingly integrated systems. However, transitioning from the noisy intermediate-scale quantum (NISQ) era to robust, fault tolerant implementations remain a central challenge. This paper presents a systematic review and synthesis of practical quantum computing, encompassing experimental advances, integrated photonics, error correction breakthroughs, software stack evolution, benchmarking practices, algorithmic readiness, and socio-technical dimensions. We critically assess current limitations and highlight emerging directions, including integrated photonic modules with high fidelity interconnects, real time decoders for surface code logical qubits, and hardware aware compiler strategies. Our analysis is grounded in peer-reviewed experimental data and model-based evaluations. Key findings include demonstrated logical error suppression via increased code distance, scalable photonic module prototypes, and refined resource estimates for cryptanalytic and simulation tasks. This paper concludes with a structured research agenda for 2025–2030, proposing four priority thrusts such as hardware QEC co design, reproducible cross platform benchmarking, modular/distributed architectures, and quantum networking with governance frameworks. This evidence based roadmap aims to align researchers and practitioners toward actionable milestones on the path to practical quantum computing.
Transformer now underpins modern AI as its core infrastructure. Its defining capability-dynamically focusing on the most relevant information in complex inputs-is bounded above by the self-attention scoring function. Quantum computing, with its superposition, entanglement, and probabilistic outputs, offers a fundamentally distinct computational framework for exploring beyond the design constraints of classical scoring functions. While quantum attention mechanisms have shown initial promise, existing works remain largely confined to redefining feature similarity measures, leaving the systematic use of parameterized quantum circuits (PQCs) as scoring functions largely unexplored; a substantial portion of existing schemes further rely on purely quantum architectures, precluding effective encoding of high-dimensional image inputs in the Noisy Intermediate-Scale Quantum era. We propose the Quantum Parameterized Self-Attention Network (QPSAN), implementing the self-attention scoring function via PQCs with only 5 trainable quantum parameters per layer. QPSAN computes query-key attention scores through quantum state encoding and joint measurement, yielding naturally bounded outputs without the explicit scaling of classical dot-product attention. We further establish a theoretical framework of the mathematical properties of this scoring function, demonstrating its potential to capture complex nonlinear query-key interactions, and quantifying the structural constraints of the encoding layer via effective degrees of freedom analysis. Experiments on four vision datasets show that QPSAN significantly outperforms the Vision Transformer (ViT) baseline, with the quantum representational advantage amplifying as data complexity increases. Ablation studies indicate that the performance gains may stem from the structural inductive bias of the quantum circuit rather than from parameter scale.
Abstract We formalize Quantum Hybrid Modules (QHM) to evaluate the insertion of quantum subroutines into classical AI pipelines with explicit assumptions about data loading, noise, and measurement overhead. The framework comprises three components: (1) a Hadamard Quantum Self-Attention (HQSA) module computing sparse attention via Hadamard-test inner products in O(nn/k⋅logd/ϵ) time assuming quantum random access memory (qRAM); (2) a Quantum Approximate Optimization Algorithm (QAOA) module for combinatorial optimization sub-problems; and (3) a Grover-accelerated neurosymbolic verification loop for quadratically faster constraint checking. We evaluate QHM through 22 experiments spanning statevector and GPU simulation up to 30 qubits, cloud simulators, and five quantum processing unit (QPU) experiments on Rigetti Cepheus and IonQ Forte, including 8--100-qubit superconducting and 8--30-qubit trapped-ion QAOA. Simulations confirm the intended mechanisms: QAOA exceeds the Goemans--Williamson bound on selected 3-regular instances, Hadamard-test attention reproduces classical softmax, and Grover verification reaches a 20,862× query reduction at 30 qubits. Classical, dequantized, and qRAM-free baselines expose current limits: small quantum kernels lag tuned classical baselines, hybrid variational quantum circuit layers add training cost without consistent accuracy gains, and shot-based noisy intermediate-scale quantum (NISQ) attention is limited by systematic bias. Hardware results are depth- and platform-dependent: shallow circuits retain measurable signal, Rigetti QAOA depolarizes to the random baseline and is not recovered by post-hoc mitigation, and IonQ Forte preserves QAOA signal but remains below noiseless performance. We do not claim near-term quantum advantage; we characterize the gap between asymptotic theory and present-day hardware.