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[22] Tobias Haug, Kishor Bharti, and M.S. Kim, "Capacity and Quantum Geometry of Parametrized Quantum Circuits", PRX Quantum 2 4, 040309 (2021).

[23] Jinfeng Zeng, Chenfeng Cao, Chao Zhang, Pengxiang Xu, and Bei Zeng, "A variational quantum algorithm for Hamiltonian diagonalization", Quantum Science and Technology 6 4, 045009 (2021).

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[50] Ju-Young Ryu, Eyuel Elala, and June-Koo Kevin Rhee, "Quantum Graph Neural Network Models for Materials Search", Materials 16 12, 4300 (2023).

[51] Tyler Volkoff, Zoë Holmes, and Andrew Sornborger, "Universal Compiling and (No-)Free-Lunch Theorems for Continuous-Variable Quantum Learning", PRX Quantum 2 4, 040327 (2021).

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[63] Sukin Sim, Jonathan Romero, Jérôme F Gonthier, and Alexander A Kunitsa, "Adaptive pruning-based optimization of parameterized quantum circuits", Quantum Science and Technology 6 2, 025019 (2021).

[64] Chen Zhao and Xiao-Shan Gao, "QDNN: deep neural networks with quantum layers", Quantum Machine Intelligence 3 1, 15 (2021).

[65] Dylan G Stone and Carlo Bradac, "Machine and quantum learning for diamond-based quantum applications", Materials for Quantum Technology 3 1, 012001 (2023).

[66] Barnaby van Straaten and Bálint Koczor, "Measurement Cost of Metric-Aware Variational Quantum Algorithms", PRX Quantum 2 3, 030324 (2021).

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[71] Peng-Fei Zhou, Rui Hong, and Shi-Ju Ran, "Automatically differentiable quantum circuit for many-qubit state preparation", Physical Review A 104 4, 042601 (2021).

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[73] Stefano Barison, Filippo Vicentini, and Giuseppe Carleo, "An efficient quantum algorithm for the time evolution of parameterized circuits", Quantum 5, 512 (2021).

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[90] Mo Kordzanganeh, Pavel Sekatski, Leonid Fedichkin, and Alexey Melnikov, "An exponentially-growing family of universal quantum circuits", Machine Learning: Science and Technology 4 3, 035036 (2023).

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[98] Chen Zhao and Xiao-Shan Gao, "Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus", Quantum 5, 466 (2021).

[99] Sergio Altares-López, Angela Ribeiro, and Juan José García-Ripoll, "Automatic design of quantum feature maps", Quantum Science and Technology 6 4, 045015 (2021).

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[105] Xin Wang, Zhixin Song, and Youle Wang, "Variational Quantum Singular Value Decomposition", Quantum 5, 483 (2021).

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[110] Kouhei Nakaji and Naoki Yamamoto, "Expressibility of the alternating layered ansatz for quantum computation", Quantum 5, 434 (2021).

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[116] Bobak Toussi Kiani, Giacomo De Palma, Milad Marvian, Zi-Wen Liu, and Seth Lloyd, "Learning quantum data with the quantum earth mover’s distance", Quantum Science and Technology 7 4, 045002 (2022).

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[119] Wenjie Liu, Jiaojiao Zhao, and Qingshan Wu, "A hybrid quantum-classical generative adversarial networks algorithm based on inherited layerwise learning with circle-connectivity circuit", Quantum Information Processing 21 11, 372 (2022).

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[121] Michael L. Wall and Giuseppe D'Aguanno, "Tree-tensor-network classifiers for machine learning: From quantum inspired to quantum assisted", Physical Review A 104 4, 042408 (2021).

[122] G. Xu, Y. B. Guo, X. Li, K. Wang, Z. Fan, Z. S. Zhou, H. J. Liao, and T. Xiang, "Concurrent quantum eigensolver for multiple low-energy eigenstates", Physical Review A 107 5, 052423 (2023).

[123] Stefano Barison, Filippo Vicentini, Ignacio Cirac, and Giuseppe Carleo, "Variational dynamics as a ground-state problem on a quantum computer", Physical Review Research 4 4, 043161 (2022).

[124] Andrew Arrasmith, M. Cerezo, Piotr Czarnik, Lukasz Cincio, and Patrick J. Coles, "Effect of barren plateaus on gradient-free optimization", Quantum 5, 558 (2021).

[125] Zhimin He, Chuangtao Chen, Lvzhou Li, Shenggen Zheng, and Haozhen Situ, "Quantum Architecture Search with Meta‐Learning", Advanced Quantum Technologies 5 8, 2100134 (2022).

[126] Nikolay V. Tkachenko, James Sud, Yu Zhang, Sergei Tretiak, Petr M. Anisimov, Andrew T. Arrasmith, Patrick J. Coles, Lukasz Cincio, and Pavel A. Dub, "Correlation-Informed Permutation of Qubits for Reducing Ansatz Depth in the Variational Quantum Eigensolver", PRX Quantum 2 2, 020337 (2021).

[127] Lirandë Pira and Chris Ferrie, "An invitation to distributed quantum neural networks", Quantum Machine Intelligence 5 2, 23 (2023).

[128] Brian Coyle, Maxwell Henderson, Justin Chan Jin Le, Niraj Kumar, Marco Paini, and Elham Kashefi, "Quantum versus classical generative modelling in finance", Quantum Science and Technology 6 2, 024013 (2021).

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[130] Charles Moussa, Yash J. Patel, Vedran Dunjko, Thomas Bäck, and Jan N. van Rijn, "Hyperparameter importance and optimization of quantum neural networks across small datasets", Machine Learning (2023).

[131] Tatiana A. Bespalova and Oleksandr Kyriienko, "Hamiltonian Operator Approximation for Energy Measurement and Ground-State Preparation", PRX Quantum 2 3, 030318 (2021).

[132] Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, Shan You, and Dacheng Tao, "Learnability of Quantum Neural Networks", PRX Quantum 2 4, 040337 (2021).

[133] Ze‐Tong Li, Fan‐Xu Meng, Han Zeng, Zhai‐Rui Gong, Zai‐Chen Zhang, and Xu‐Tao Yu, "A Gradient‐Cost Multiobjective Alternate Framework for Variational Quantum Eigensolver with Variable Ansatz", Advanced Quantum Technologies 6 5, 2200130 (2023).

[134] Alba Cervera-Lierta, Jakob S. Kottmann, and Alán Aspuru-Guzik, "Meta-Variational Quantum Eigensolver: Learning Energy Profiles of Parameterized Hamiltonians for Quantum Simulation", PRX Quantum 2 2, 020329 (2021).

[135] Hongxiang Chen, Max Nusspickel, Jules Tilly, and George H. Booth, "Variational quantum eigensolver for dynamic correlation functions", Physical Review A 104 3, 032405 (2021).

[136] Maxwell T West, Martin Sevior, and Muhammad Usman, "Reflection equivariant quantum neural networks for enhanced image classification", Machine Learning: Science and Technology 4 3, 035027 (2023).

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The above citations are from Crossref's cited-by service (last updated successfully 2023-09-28 01:49:11) and SAO/NASA ADS (last updated successfully 2023-09-28 01:49:12). The list may be incomplete as not all publishers provide suitable and complete citation data.