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Quantum Neural Networks: Concepts, Applications, and Challenges

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arxiv 2108.01468 v1 pith:PLNAOIKO submitted 2021-08-02 quant-ph cs.LG

Quantum Neural Networks: Concepts, Applications, and Challenges

classification quant-ph cs.LG
keywords quantumdeeplearningresearchnetworksneuralchallengescomputing
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Quantum deep learning is a research field for the use of quantum computing techniques for training deep neural networks. The research topics and directions of deep learning and quantum computing have been separated for long time, however by discovering that quantum circuits can act like artificial neural networks, quantum deep learning research is widely adopted. This paper explains the backgrounds and basic principles of quantum deep learning and also introduces major achievements. After that, this paper discusses the challenges of quantum deep learning research in multiple perspectives. Lastly, this paper presents various future research directions and application fields of quantum deep learning.

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Cited by 1 Pith paper

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  1. Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization

    quant-ph 2026-07 conditional novelty 6.0

    A learning-to-rank model over feature-model-sampled Qiskit transpiler pass configurations reliably outperforms Qiskit's fixed optimization levels on two-qubit gate reduction.