Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum Synergy in Retrieval-Augmented Generation for Contextual Enhancement

K. Adithi, R. K. Kapilavani

Published May 8, 2025• 1 citation
Publisher

Abstract

Abstract Retrieval-Augmented Generation (RAG) has emerged as a powerful framework in natural language processing (NLP), integrating retrieval mechanisms with generative models to enhance information accuracy and contextual relevance. However, classical retrieval techniques face scalability bottlenecks and computational inefficiencies when handling large datasets. In this work, we introduce GroQ-Enhanced RAG (QRAG), a hybrid quantum-classical framework that leverages Grover’s search algorithm and GroQ-Rank (QAOA-based ranking) to enhance retrieval efficiency and optimization. QRAG employs Grover’s algorithm to accelerate query processing and utilizes GroQ for combinatorial ranking optimization, significantly reducing computational overhead. Empirical evaluations demonstrate that QRAG reduces retrieval latency by 40–50% compared to traditional RAG while improving response accuracy and scalability. By integrating quantum search and optimization techniques, GroQ-powered QRAG sets a new benchmark for efficient, high-fidelity information retrieval in NLP. While this study applies QRAG to RAG-based architectures, the proposed framework can be extended to other AI-driven retrieval-intensive applications, highlighting the transformative potential of quantum computing in large-scale language processing and information retrieval tasks.

DOI
10.21203/rs.3.rs-6216441/v1

Related papers

Quantum-Enhanced Retrieval-Augmented Generation for Hallucination Reduction in Large Language Models

Praveenkumar Seepana

Although the performance of LLMs on a wide variety of natural language processing problems demonstrates remarkable ability, hallucinated responses are introduced as one of the key weaknesses of LLMs, especially in knowledge-intensive applications, where fidelity to facts is paramount. While effective in reducing hallucinations, the context retrieved during the RAG operations is still very often suboptimal with respect to the external documents used in the retrieval stage, and being based on cosine similarity and nearest-neighbour search, these models typically do not return optimal context to support factual generation. The authors propose a five-stage solution, called Quantum-Enhanced Retrieval-Augmented Generation (QERAG), which combines the use of a quantum-inspired probability amplitude document ranking, a Context Utility Score (CUS) optimisation engine, and a Hallucination Verification Agent (HVA) based on a formal Hallucination Reduction Index (HRI). The Quantum Relevance Score (QRS) uses interference based, Amplitude Encoding on candidate Document Sets to yield a normalised relevance distribution that emphasises the relatively higher value generated in the discriminative context as compared to other locally similar passages in a document set. QERAG outperforms Standard LLM and Traditional RAG models on all five Natural benchmark across response accuracy, with a score of 92.8% vs. 74.3%, and hallucination rate, 4.3% vs. 18.2%, respectively. They achieve a significant overall improvement by using quantum-inspired retrieval layer, which achieves a 3.5-percentage-point reduction in hallucination rate when compared to cosine-similarity RAG, with this single layer showing more reduction than the rest of the pipeline layers. The results of an ablation study show the individual contribution of every pipeline stage, and the quantum-inspired retrieval layer exhibits the greatest reduction of a 3.5 percentage points over cosine-similarity RAG.

International Journal of Computational Science and Engineering Research•Aug 3, 2026•0 citations

Quantum Driven Vector Similarity Search

Asoke Nath, Sanchayita Ghosh, Bhaskar Joyti Mitra +1 more

Retrieval-Augmented Generation (RAG) systems rely on high-dimensional vector similarity search to retrieve relevant documents for large language models. However, classical nearest-neighbor methods face scalability limits dueto the "curse of dimensionality" and the computational cost of comparing massive vector sets.The authors propose a hybrid quantum-classical pipeline that leverages quantum computing for similarity evaluation.Experimental results on synthetic data demonstrate that the swap-test similarity estimates closely match classical dot products, and that clustering dramatically reduces search overhead.The present studies show the feasibility of quantum-enhanced retrieval: the quantum similarity scores approximate classical results within statistical error.We discuss accuracy, runtime trade-offs, and the challenges of state preparation in the Noisy Intermediate-Scale Quantum (NISQ) era.

INTERNATIONAL JOURNAL OF ADVANCE RESEARCH IN COMPUTER SCIENCE AND MANAGEMENT STUDIES•Jun 1, 2025•0 citations

Quantum-enhanced generative artificial intelligence: a critical review of classical limitations, complexity barriers, and hybrid quantum–classical architectures

Navneet Pratap Singh, J. Omana, Smrithy Girijakumari Sreekantan Nair

The rapid commercialization of generative artificial intelligence (AI), along with the maturation of quantum technologies has raised a question: can quantum-powered neural networks become the next major shift in large language model (LLM) technology? This naturally leads to another misconception that quantum systems will replace classical LLMs. In this study, both architectures are compared in a contrastive manner in terms of mathematics. The data reveals that identical dynamics that help classical systems learn natural language distributions constrain its ability to use efficient sampling of quantum-mechanical spaces. Performing complexity-theoretic separations (i.e., the widely believed but unproven conjecture that BPP ⊆ BQP) and a 2025 preprint reporting experimental demonstrations of quantum advantage for generative tasks we conclude that quantum utility is unlikely to lie in tasks involving natural language processing under current architectures, but rather in certain computational subroutines. We then suggest a hybrid quantum-classical architecture as the best direction to take in the future, as it has the advantages of both paradigms. This is done by studying a case study that optimizes retrieval-augmented generation (RAG) pipelines with Grover's search algorithm.

Frontiers in Artificial Intelligence•Jul 21, 2026•0 citations

GroverAttention: Quantum-Enhanced Attention Mechanism for Effcient Transformers

Belkacem Chikhaoui

Abstract Transformer-based models have revolutionized natural language processing (NLP), but their computational complexity remains a bottleneck. This paper proposes a novel approach to integrating Quantum Attention into Transformers, leveraging Grover’s algorithm to optimize query-key matching. By replacing classical attention mechanisms with quantum-enhanced token selection, we achieve reduced computational cost and improved inference speed. Experimental results on real-world datasets demonstrate the effectiveness of our hybrid Quantum-Classical Transformer, showing an improvement in accuracy with reduced training overhead. Our findings suggest that quantum-assisted attention has the potential to significantly optimize deep learning architectures for NLP applications.

•Oct 31, 2025•0 citations

Quantum Techniques for Optimizing Algorithms in Natural Language Processing

Dalydas Karanath, Udai Shankar

This research paper explores how Natural Language Processing (NLP) algorithms can be enhanced through the use of quantum computing. Since NLP applications such as sentiment analysis, text classification, and machine translation analyze huge volumes of data, the amount of resourceconsuming work for traditional computing architectures, including large scale language models, becomes excessive. There is an opportunity for significant improvements in speed and efficiency because of the quantum properties of superposition and entanglement. This research explores the use of quantum algorithms such as Grover's search, quantum machine learning algorithms, and quantum-inspired tensor networks for alleviating computational NLP bottlenecks. The comparative study of the quantum approach against the classical method shows notable speedup in theory with an explanation of the implementation of quantum algorithms on Noisy IntermediateScale Quantum (NISQ) devices. The most promising NLP algorithm enhancement will most likely come from hybrid quantum-classical sophisticated architectures as discovered through this study, where quantum processing is applied to some components and classical processing is used for the rest of the quantum-classical NLP algorithms. This is the first research study in Quantum Natural Language Processing (QNLP) for real world NLP applications and the author is commended for their efforts.

International Conference on Visual Analytics and Data Visualization (ICVADV)•Feb 24, 2026•0 citations

Quantum-Rag: Retrieval-Augmented Generation Framework for Quantum Circuit Code Generation

Nicholas Stapleton, Sharmin Afrose, Ethan Seefried +3 more

Generating correct and executable quantum circuit code remains a significant barrier for both newcomers and experienced practitioners in quantum computing. While Large Language Models (LLMs) have shown promise in classical code generation, their application to quantum programming is hampered by limited domain-specific training data and the rapidly evolving nature of quantum software frameworks. In this work, we present Quantum-RAG, an extended Retrieval-Augmented Generation framework that couples a domain-specialized language model with a curated, multi-source knowledge corpus to produce Qiskit-based quantum circuit code. Our three-stage pipeline ingests documentation, educational notebooks, and research literature into a dense retrieval index and augments the generation model with the most relevant context at inference time. Evaluated on the Qiskit HumanEval benchmark, QuantumRAG achieves a Pass@1 of 0.4132, a Pass@3 of 0.6291, and a Pass@5 of 0.7020, surpassing the domain-adapted GraniteQiskit baseline by 101%, 72%, and 47 % at each respective threshold. These results demonstrate that retrieval-augmented approaches can substantially improve quantum code generation without expensive model retraining.

IEEE Dallas Circuits and Systems Conference (DCAS)•Apr 10, 2026•0 citations
Abstract Retrieval-Augmented Generation (RAG) has emerged as a powerful framework in natural language processing (NLP), integrating retrieval mechanisms with generative models to enhance information accuracy and contextual relevance. However, classical retrieval techniques face scalability bottlenecks and computational inefficiencies when handling large datasets. In this work, we introduce GroQ-Enhanced RAG (QRAG), a hybrid quantum-classical framework that leverages Grover’s search algorithm and GroQ-Rank (QAOA-based ranking) to enhance retrieval efficiency and optimization. QRAG employs Grover’s algorithm to accelerate query processing and utilizes GroQ for combinatorial ranking optimization, significantly reducing computational overhead. Empirical evaluations demonstrate that QRAG reduces retrieval latency by 40–50% compared to traditional RAG while improving response accuracy and scalability. By integrating quantum search and optimization techniques, GroQ-powered QRAG sets a new benchmark for efficient, high-fidelity information retrieval in NLP. While this study applies QRAG to RAG-based architectures, the proposed framework can be extended to other AI-driven retrieval-intensive applications, highlighting the transformative potential of quantum computing in large-scale language processing and information retrieval tasks.
Although the performance of LLMs on a wide variety of natural language processing problems demonstrates remarkable ability, hallucinated responses are introduced as one of the key weaknesses of LLMs, especially in knowledge-intensive applications, where fidelity to facts is paramount. While effective in reducing hallucinations, the context retrieved during the RAG operations is still very often suboptimal with respect to the external documents used in the retrieval stage, and being based on cosine similarity and nearest-neighbour search, these models typically do not return optimal context to support factual generation. The authors propose a five-stage solution, called Quantum-Enhanced Retrieval-Augmented Generation (QERAG), which combines the use of a quantum-inspired probability amplitude document ranking, a Context Utility Score (CUS) optimisation engine, and a Hallucination Verification Agent (HVA) based on a formal Hallucination Reduction Index (HRI). The Quantum Relevance Score (QRS) uses interference based, Amplitude Encoding on candidate Document Sets to yield a normalised relevance distribution that emphasises the relatively higher value generated in the discriminative context as compared to other locally similar passages in a document set. QERAG outperforms Standard LLM and Traditional RAG models on all five Natural benchmark across response accuracy, with a score of 92.8% vs. 74.3%, and hallucination rate, 4.3% vs. 18.2%, respectively. They achieve a significant overall improvement by using quantum-inspired retrieval layer, which achieves a 3.5-percentage-point reduction in hallucination rate when compared to cosine-similarity RAG, with this single layer showing more reduction than the rest of the pipeline layers. The results of an ablation study show the individual contribution of every pipeline stage, and the quantum-inspired retrieval layer exhibits the greatest reduction of a 3.5 percentage points over cosine-similarity RAG.
Retrieval-Augmented Generation (RAG) systems rely on high-dimensional vector similarity search to retrieve relevant documents for large language models. However, classical nearest-neighbor methods face scalability limits dueto the "curse of dimensionality" and the computational cost of comparing massive vector sets.The authors propose a hybrid quantum-classical pipeline that leverages quantum computing for similarity evaluation.Experimental results on synthetic data demonstrate that the swap-test similarity estimates closely match classical dot products, and that clustering dramatically reduces search overhead.The present studies show the feasibility of quantum-enhanced retrieval: the quantum similarity scores approximate classical results within statistical error.We discuss accuracy, runtime trade-offs, and the challenges of state preparation in the Noisy Intermediate-Scale Quantum (NISQ) era.
The rapid commercialization of generative artificial intelligence (AI), along with the maturation of quantum technologies has raised a question: can quantum-powered neural networks become the next major shift in large language model (LLM) technology? This naturally leads to another misconception that quantum systems will replace classical LLMs. In this study, both architectures are compared in a contrastive manner in terms of mathematics. The data reveals that identical dynamics that help classical systems learn natural language distributions constrain its ability to use efficient sampling of quantum-mechanical spaces. Performing complexity-theoretic separations (i.e., the widely believed but unproven conjecture that BPP ⊆ BQP) and a 2025 preprint reporting experimental demonstrations of quantum advantage for generative tasks we conclude that quantum utility is unlikely to lie in tasks involving natural language processing under current architectures, but rather in certain computational subroutines. We then suggest a hybrid quantum-classical architecture as the best direction to take in the future, as it has the advantages of both paradigms. This is done by studying a case study that optimizes retrieval-augmented generation (RAG) pipelines with Grover's search algorithm.
Abstract Transformer-based models have revolutionized natural language processing (NLP), but their computational complexity remains a bottleneck. This paper proposes a novel approach to integrating Quantum Attention into Transformers, leveraging Grover’s algorithm to optimize query-key matching. By replacing classical attention mechanisms with quantum-enhanced token selection, we achieve reduced computational cost and improved inference speed. Experimental results on real-world datasets demonstrate the effectiveness of our hybrid Quantum-Classical Transformer, showing an improvement in accuracy with reduced training overhead. Our findings suggest that quantum-assisted attention has the potential to significantly optimize deep learning architectures for NLP applications.
This research paper explores how Natural Language Processing (NLP) algorithms can be enhanced through the use of quantum computing. Since NLP applications such as sentiment analysis, text classification, and machine translation analyze huge volumes of data, the amount of resourceconsuming work for traditional computing architectures, including large scale language models, becomes excessive. There is an opportunity for significant improvements in speed and efficiency because of the quantum properties of superposition and entanglement. This research explores the use of quantum algorithms such as Grover's search, quantum machine learning algorithms, and quantum-inspired tensor networks for alleviating computational NLP bottlenecks. The comparative study of the quantum approach against the classical method shows notable speedup in theory with an explanation of the implementation of quantum algorithms on Noisy IntermediateScale Quantum (NISQ) devices. The most promising NLP algorithm enhancement will most likely come from hybrid quantum-classical sophisticated architectures as discovered through this study, where quantum processing is applied to some components and classical processing is used for the rest of the quantum-classical NLP algorithms. This is the first research study in Quantum Natural Language Processing (QNLP) for real world NLP applications and the author is commended for their efforts.
Generating correct and executable quantum circuit code remains a significant barrier for both newcomers and experienced practitioners in quantum computing. While Large Language Models (LLMs) have shown promise in classical code generation, their application to quantum programming is hampered by limited domain-specific training data and the rapidly evolving nature of quantum software frameworks. In this work, we present Quantum-RAG, an extended Retrieval-Augmented Generation framework that couples a domain-specialized language model with a curated, multi-source knowledge corpus to produce Qiskit-based quantum circuit code. Our three-stage pipeline ingests documentation, educational notebooks, and research literature into a dense retrieval index and augments the generation model with the most relevant context at inference time. Evaluated on the Qiskit HumanEval benchmark, QuantumRAG achieves a Pass@1 of 0.4132, a Pass@3 of 0.6291, and a Pass@5 of 0.7020, surpassing the domain-adapted GraniteQiskit baseline by 101%, 72%, and 47 % at each respective threshold. These results demonstrate that retrieval-augmented approaches can substantially improve quantum code generation without expensive model retraining.