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The quantum machine learning (QML) paradigms and their synergies with network slicing can be envisioned to be a disruptive technology on the cusp of entering to era of sixth-generation (6G), where the mobile communication systems are underpinned in the form of advanced tenancy-based digital use-cases to meet different service requirements.
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2021
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F. Rezazadeh, H. Chergui, L. Christofi, and C. Verikoukis, “Actor-Critic-Based Learning for Zero-touch Joint Resource and Energy Control in Network Slicing,” IEEE ICC , Jun. 2021
2021
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M. Schuld et al., “The effect of data encoding on the expressive power of variational quantum machine learning models,” American Physical Society , March 2021
2021
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Q. Wei, H. Ma, C. Chen, and D. Dong, “Deep Reinforcement Learning With Quantum-Inspired Experience Replay,” IEEE Transactions on Cybernetics , pp. 1–13, Feb. 2021
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L. Funcke et al., “Towards quantum simulations in particle physics and beyond on noisy intermediate-scale quantum devices,” Royal Society, Quantum technologies in particle physics , vol. 380, no. 2216, Feb. 2022
2022
Closest in time.
A. Skolik, S. Jerbi, and V. Dunjko, “Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning,” Quantum Journal , vol. 6, May 2022
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Closest in time.
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2021
Cited alongside, same era.
2022
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