QHM: Unifying Superconducting and Topological Quantum Computing with Multimodal AI

  1. Vikram Karlex

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Artificial Intelligence (AI) has achieved remarkable successes, but it continues to face critical challenges—including inefficiency, limited interpretability, lack of robustness, alignment issues, and high energy consumption. Quantum computing offers a promising path to fundamentally accelerate and enhance AI by leveraging quantum parallelism and entanglement. This paper proposes QHM—Quantum Hybrid Multimodal, a unified framework that integrates superconducting and topological quantum computing into multimodal AI architectures. We establish a theoretical foundation for embedding quantum subroutines—such as a Quantum Self-Attention Neural Network (QSANN) and Quantum Enhanced Quantum Approximate Optimization Algorithm (QAOA)- QQAOA—within classical deep learning models. We survey key quantum algorithms, including Grover’s search, the HHL algorithm for solving linear systems, QAOA, and variational quantum circuits, evaluating their computational complexity and suitability for AI workloads. We also analyze cutting-edge quantum hardware platforms: superconducting qubit systems like Google’s 105-qubit Willow, IBM’s 1,121-qubit Condor, Amazon’s bosonic Ocelot, and Microsoft’s topological Majorana-1, discussing their potential for accelerating AI. The paper explores how quantum resources can enhance large language models, Transformers, mixture-of-experts architectures, and cross-modal learning via quantum-accelerated similarity search, attention mechanisms, and optimization techniques. We also examine practical engineering challenges, including cryogenic cooling, control electronics, qubit noise, quantum error correction, and data encoding overhead, offering a cost-benefit analysis. An implementation roadmap is outlined, progressing from classical simulations to hybrid quantum-classical prototypes, and ultimately to fully integrated systems. We propose benchmarking strategies to evaluate quantum-AI performance relative to classical baselines. Compared to conventional approaches, the QHM hybrid framework promises improved computational scaling and novel capabilities—such as faster search and more efficient training—while acknowledging current limitations in noise and infrastructure. We conclude by outlining future directions for developing quantum-enhanced AI systems that are more efficient, interpretable, and aligned with human values, and we discuss broader implications for AI safety and sustainability.

Article activity feed

  1. PREreview

    This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/15361176.

    General Evaluation

    This manuscript presents a detailed framework for integrating quantum computing technologies—specifically superconducting and topological platforms—into multimodal artificial intelligence systems. The proposed Quantum Hybrid Multimodal (QHM) architecture is well-developed, supported by theoretical models, current hardware specifications, and a structured implementation roadmap. It successfully connects recent advances in quantum computing with the evolving demands of high-complexity AI systems.

    Strengths

    1. Thorough Scope The work covers theoretical formulations, algorithmic applications, quantum hardware, and systems-level considerations in detail. The inclusion of specific quantum subroutines such as QSANN and QQAOA in AI tasks is well-motivated.

    2. Technical Rigor The complexity analysis of quantum attention mechanisms and optimization algorithms is mathematically sound. The formulation of hybrid functions combining classical and quantum modules is presented clearly and with precision.

    3. Structured Implementation Roadmap The multi-phase roadmap from simulation to full integration offers a realistic progression, considering both current hardware constraints and future advancements.

    4. Realistic Hardware Considerations Practical limitations such as cryogenic cooling, data encoding overhead, and noise mitigation are addressed thoroughly. The discussion is balanced and informed by current engineering standards.

    5. Relevance to Multimodal AI The proposed use of quantum circuits in large-scale models involving vision, language, and sensor data demonstrates the timeliness and applicability of the framework.

    Recommendations

    1. Condensation of Content The manuscript is extensive. Reducing redundancy and condensing repeated algorithm explanations will enhance clarity and improve accessibility for readers.

    2. Inclusion of Proof-of-Concept Demonstrations Including results from small-scale hybrid simulations or prototype circuits would support the theoretical claims and demonstrate feasibility.

    3. Neurosymbolic Integration Detail The discussion of neurosymbolic AI could benefit from more concrete examples or diagrams illustrating how quantum subroutines interact with symbolic components.

    4. Comparative Summary Table A table comparing classical, superconducting, and topological approaches across performance, scalability, and application domains would help summarize key insights.

    5. Expanded Discussion on Verification and Alignment Given the significance of model interpretability and alignment in advanced AI, the manuscript could include more on how quantum computing might enhance system verifiability.

    6. Stylistic Improvements Streamlining sentence structure and adding visual aids or subsections in dense areas would further improve the document's flow and readability.

    Conclusion

    This manuscript offers a substantial contribution to the field by proposing a unified model that combines the strengths of modern quantum technologies with multimodal AI. It is conceptually innovative and technically grounded. With moderate revisions focused on clarity, length, and practical validation, the paper would be well-positioned for publication in a reputable journal dedicated to quantum computing or machine learning research.

    Competing interests

    The author declares that they have no competing interests.

  2. Version published to 10.20944/preprints202504.0232.v2