Hybrid Quantum–Classical Algorithms to Accelerate AI

V Divakar Naidu, Dr. A.V.H. Sai Prasad Dr. A.V.H. Sai Prasad, Pentamala Prakash Babu +3 more

Syed Muntasir Mamun

Quantum-AI hybrid algorithms represent the most practical near-term pathway for leveraging quantum computing's probabilistic power alongside classical AI's scalability and optimization strengths. In the noisy intermediate-scale quantum (NISQ) era, these hybrids—primarily variational quantum algorithms (VQAs) such as the Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA)—delegate hard subproblems (e.g., high-dimensional sampling, optimization under quantum constraints, or feature extraction in entangled spaces) to quantum processors while classical optimizers or neural networks handle parameter tuning, error mitigation, and large-scale data processing (Ferguson et al., 2026; Acampora, 2026). This exploration builds on the 2025 NSF AI+MPS community paper by integrating accelerated timelines: mass deployment of utility-scale quantum computing by August 2027 and AGI substantially before 2030. Hybrid approaches become critical enablers for MPS grand challenges in Mamun, SM (2026)…………………………………………1 Quantum-AI Hybrid Algorithms: Bridging Classical Intelligence, Quantum Acceleration, and Scientific Discovery in the MPS Domains (Vol-I) chemistry (molecular ground states), materials (inverse design), physics (many-body simulation), and astronomy (anomaly detection under uncertainty). Also available on Academia.edu: https://www.academia.edu/attachments/132469868/download_file?s=portfolio

Syed Muntasir Mamun

The escalating global demand for data storage, projected to reach 1024 bits by 2040, is rapidly outpacing the capacity of classical silicon-based memory, exposing an infrastructural crisis. Synthetic Deoxyribonucleic Acid (DNA) is the premier ultra-high-density archival candidate, offering 1018 bits per cubic centimeter, but its enterprise-scale adoption is severely limited by biochemical (imperfect synthesis, PCR biases) and computational bottlenecks (intractable encoding combinatorics, the Data Loading Problem) inherent to classical architectures. This report details how the strategic integration of Quantum-AI hybrid algorithms—specifically Variational Quantum Eigensolvers (VQE), Quantum Neural Networks (QNNs), and Artificial General Intelligence (AGI) orchestration—provides the necessary computational leverage to overcome these historic limitations. Hybrid frameworks like Quantum-DNA Image Compression (Q-DIC) utilize NISQ-compatible VQEs to enforce hardware Mamun, SM (2026) …………………………….1 Quantum-AI Hybrid Algorithms: Bridging Classical Intelligence, Quantum Acceleration, and Biological Data Storage (Vol-II) K S C synthesis constraints and achieve significant compression, while the 2026 breakthrough of Quantum Oracle Sketching bypasses the need for Quantum Random Access Memory (QRAM) by processing petabyte-scale data streams "on the fly". Also available on Academia.edu: https://www.academia.edu/attachments/132470386/download_file?s=portfolio

Zeph Hardie

Matthias Klusch, Jörg Lässig, Frank K. Wilhelm

Vikram Karlex

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.