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Quantum computing and artificial intelligence: status and perspectives

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arxiv 2505.23860 v3 pith:O47HPSRK submitted 2025-05-29 quant-ph cs.AIcs.LG

Quantum computing and artificial intelligence: status and perspectives

Giovanni Acampora , Andris Ambainis , Natalia Ares , Leonardo Banchi , Pallavi Bhardwaj , Daniele Binosi , G. Andrew D. Briggs , Tommaso Calarco
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Vedran Dunjko Jens Eisert Olivier Ezratty Paul Erker Federico Fedele Elies Gil-Fuster Martin G\"arttner Mats Granath Markus Heyl Iordanis Kerenidis Matthias Klusch Anton Frisk Kockum Richard Kueng Mario Krenn J\"org L\"assig Antonio Macaluso Sabrina Maniscalco Florian Marquardt Kristel Michielsen Gorka Mu\~noz-Gil Daniel M\"ussig Hendrik Poulsen Nautrup Sophie A. Neubauer Evert van Nieuwenburg Roman Orus J\"org Schmiedmayer Markus Schmitt Philipp Slusallek Filippo Vicentini Christof Weitenberg Frank K. Wilhelm
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classification quant-ph cs.AIcs.LG
keywords quantumcomputingartificialclassicaldevelopmentintelligenceresearchwhite
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could support the development of innovative AI solutions. It also examines use cases of classical AI that can empower research and development in quantum technologies, with a focus on quantum computing and quantum sensing. The purpose of this white paper is to provide a long-term research agenda aimed at addressing foundational questions about how AI and quantum computing interact and benefit one another. It concludes with a set of recommendations and challenges, including how to orchestrate the proposed theoretical work, align quantum AI developments with quantum hardware roadmaps, estimate both classical and quantum resources - especially with the goal of mitigating and optimizing energy consumption - advance this emerging hybrid software engineering discipline, and enhance European industrial competitiveness while considering societal implications.

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Cited by 9 Pith papers

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    Encoding the inference sum as a single observable and applying quantum amplitude estimation achieves optimal query complexity O(‖α‖₁/ε) with a matching lower bound for quantum kernel methods.

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    quant-ph 2026-04 unverdicted novelty 7.0

    Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.

  3. Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification

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    A hybrid classical-quantum scheme compresses and disentangles bottleneck layers of pre-trained neural networks into MPO form for execution on quantum devices, validated via proof-of-concept on MNIST and CIFAR-10 image...

  4. DeepQuantum: A PyTorch-based Software Platform for Quantum Machine Learning and Photonic Quantum Computing

    quant-ph 2025-12 accept novelty 6.0

    DeepQuantum is a PyTorch platform that unifies quantum circuits, photonic quantum circuits, and measurement-based quantum computing in one open-source framework for hybrid models and variational algorithms.

  5. Hybrid Quantum-Classical Neural Networks for Recognizing Quantum Phases

    quant-ph 2026-06 unverdicted novelty 5.0

    Hybrid quantum-classical neural network experimentally classifies topological ground states of surface-code lattices up to 4x4 sites from product states, achieving >85% single-shot and >99% averaged accuracy even unde...

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  7. Hybrid quantum-classical physics-informed neural networks for solving nonlinear PDEs: when and where hybridization is effective?

    quant-ph 2026-06 unverdicted novelty 5.0

    HQPINNs reduce relative L2 error by roughly fourfold on Burgers' equation and fivefold on Allen-Cahn equation versus classical PINNs, with smoother training and largest gains in stiff regimes.

  8. AML-QKD: Adaptive Machine Learning Framework for Real-time Parameter Tuning in QKD

    quant-ph 2026-03 conditional novelty 4.0

    ML-based TCN+PPO controller raises simulated QKD key rates by 14-25% and cuts QBER roughly in half across BB84, E91, and COW, with a separate exploratory QRL variant reporting a 29.2% E91 throughput gain.

  9. Quantum Integrated High-Performance Computing: Foundations, Architectural Elements and Future Directions

    quant-ph 2026-04 unverdicted novelty 3.0

    The authors describe a visionary layered architecture for unifying classical and quantum compute resources under a single job submission and scheduling interface.