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AUTHOR(S): Jacob Nagler, Ofir Ben-Shabat
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TITLE Brief Review: Artificial Intelligence In the Age of Quantum Computing |
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ABSTRACT This paper presents a rigorous, evidence-driven synthesis of the technical and socio-technical landscape at the intersection of artificial intelligence (AI) and quantum computing (QC). Combining a structured literature synthesis with a transparent diagnostic scoring framework, the study evaluates ten principal subdomains, including quantum hardware and control, quantum error correction, hybrid classical-quantum architectures, quantum machine learning (QML), quantum neural networks (QNNs), optimization methods, data-encoding strategies, post-quantum cryptography, software/benchmarking, and ethics/governance along three axes: readiness, technical risk, and potential impact. Key findings identify quantum hardware and error correction as foundational enablers with the greatest trans-formative potential but minimal near-term readiness; by contrast, hybrid architectures and quantum-safe cryptographic measures exhibit higher maturity and represent the most practicable near-term routes to benefit from NISQ devices. QML and QNN approaches retain considerable theoretical promise but are constrained by data-encoding costs, training-landscape pathologies (e.g., barren plateaus), noise sensitivity, and dequantizing caveats. To move from theoretical potential to verifiable progress, the paper proposes a prioritized, technical agenda that emphasizes (i) standardized, reproducible bench-marking pipelines and open data; (ii) hybrid co-design experiments to disentangle expressive from noise; (iii) resource-aware error-mitigation and low-overhead QEC research; and (iv) explicit articulation of input/state-preparation models in all speedup claims. The combined technical survey, diagnostic dataset, and concrete experimental protocols aim to enable empirically testable claims of quantum-augmented AI and to guide coordinated research and engineering efforts toward fault-tolerant, socially responsible outcomes. |
KEYWORDS Quantum computing; Quantum machine learning; Variational algorithms; Hybrid architectures; Quantum error correction; Post-quantum cryptography; Benchmarking; NISQ |
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Cite this paper Jacob Nagler, Ofir Ben-Shabat. (2026) Brief Review: Artificial Intelligence In the Age of Quantum Computing. International Journal of Circuits and Electronics, 11, 10-31 |
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