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[2] Andrew Jordan Siciliano and Zheng Wang, "Generating three-dimensional genome structures with a variational quantum algorithm", Briefings in Bioinformatics 26 6, bbaf663 (2025).

[3] Seongmin Im, Juhee Baek, and Keunhong Jeong, "Prediction of toxicity of chemicals using a novel quantum circuit with sign-alternated angle encoding", Digital Discovery 5 8, 3245 (2026).

[4] Chisomo Daka and Somnath Bhattacharyya, "Quantum neural network-inspired variational quantum circuit for simulating diamond 14NV center Hamiltonian with a proximal 13C isotope", Physica Scripta 100 2, 025111 (2025).

[5] Farzaneh Shayeganfar, Ali Ramazani, Veera Sundararaghavan, and Yuhua Duan, "Quantum graph learning and algorithms applied in quantum computer sciences and image classification", Applied Physics Reviews 12 2, 021327 (2025).

[6] Chiara Leadbeater, Louis Sharrock, Brian Coyle, and Marcello Benedetti, "F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits", Entropy 23 10, 1281 (2021).

[7] Mehri Mehrnia and Mohammed S.M. Elbaz, 2025 IEEE International Conference on Quantum Computing and Engineering (QCE) 2021 (2025) ISBN:979-8-3315-5736-2.

[8] Ho Lun Tang, V.O. Shkolnikov, George S. Barron, Harper R. Grimsley, Nicholas J. Mayhall, Edwin Barnes, and Sophia E. Economou, "Qubit-ADAPT-VQE: An Adaptive Algorithm for Constructing Hardware-Efficient Ansätze on a Quantum Processor", PRX Quantum 2 2, 020310 (2021).

[9] Daniel Basilewitsch, João F. Bravo, Christian Tutschku, and Frederick Struckmeier, "Quantum neural networks in practice: a comparative study with classical models from standard data sets to industrial images", Quantum Machine Intelligence 7 2, 110 (2025).

[10] Maximilian Wendlinger, Kilian Tscharke, and Pascal Debus, 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) 1447 (2024) ISBN:979-8-3315-4137-8.

[11] Zong-Liang Li and Shi-Xin Zhang, "Dual role of low-weight Pauli propagation: A flawed simulator but a powerful initializer for variational quantum algorithms", Physical Review Research 8 1, 013266 (2026).

[12] Lukas Mouton, Florentin Reiter, Ying Chen, and Patrick Rebentrost, "Deep-learning-based quantum algorithms for solving nonlinear partial differential equations", Physical Review A 110 2, 022612 (2024).

[13] Harriet L. Dao, "Exploring new variational quantum circuit ansatzes for solving SU(2) matrix models", The European Physical Journal C 85 6, 705 (2025).

[14] Johannes Selisko, Maximilian Amsler, Christopher Wever, Yukio Kawashima, Georgy Samsonidze, Rukhsan Ul Haq, Francesco Tacchino, Ivano Tavernelli, and Thomas Eckl, "Dynamical mean field theory for real materials on a quantum computer", npj Computational Materials 11 1, 325 (2025).

[15] Oriel Kiss, Francesco Tacchino, Sofia Vallecorsa, and Ivano Tavernelli, "Quantum neural networks force fields generation", Machine Learning: Science and Technology 3 3, 035004 (2022).

[16] Zoë Holmes, Andrew Arrasmith, Bin Yan, Patrick J. Coles, Andreas Albrecht, and Andrew T. Sornborger, "Barren Plateaus Preclude Learning Scramblers", Physical Review Letters 126 19, 190501 (2021).

[17] Riza Alaudin Syah, Irwan Alnarus Kautsar, and Haza Nuzly Bin Abdull Hamed, 2026 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) 266 (2026) ISBN:979-8-3195-3608-2.

[18] Lucas Slattery, Benjamin Villalonga, and Bryan K. Clark, "Unitary block optimization for variational quantum algorithms", Physical Review Research 4 2, 023072 (2022).

[19] Savvas Varsamopoulos, Evan Philip, Vincent E. Elfving, Herman W. T. van Vlijmen, Sairam Menon, Ann Vos, Natalia Dyubankova, Bert Torfs, and Anthony Rowe, "Quantum extremal learning", Quantum Machine Intelligence 6 2, 42 (2024).

[20] Oriel Kiss, Michele Grossi, Pavel Lougovski, Federico Sanchez, Sofia Vallecorsa, and Thomas Papenbrock, "Quantum computing of the Li6 nucleus via ordered unitary coupled clusters", Physical Review C 106 3, 034325 (2022).

[21] M. H. Cheng, K. E. Khosla, C. N. Self, M. Lin, B. X. Li, A. C. Medina, and M. S. Kim, "Clifford circuit initialization for variational quantum algorithms", Physical Review A 111 6, 062413 (2025).

[22] Ruhan Wang, Philip Richerme, and Fan Chen, "A hybrid quantum–classical neural network for learning transferable visual representation", Quantum Science and Technology 8 4, 045021 (2023).

[23] Roeland Wiersema, Efekan Kökcü, Alexander F. Kemper, and Bojko N. Bakalov, "Classification of dynamical Lie algebras of 2-local spin systems on linear, circular and fully connected topologies", npj Quantum Information 10 1, 110 (2024).

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

[25] 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).

[26] Iordanis Kerenidis and Alessandro Luongo, "Classification of the MNIST data set with quantum slow feature analysis", Physical Review A 101 6, 062327 (2020).

[27] Shiro Tamiya, Sho Koh, and Yuya O. Nakagawa, "Calculating nonadiabatic couplings and Berry's phase by variational quantum eigensolvers", Physical Review Research 3 2, 023244 (2021).

[28] Joseph Bowles, David Wierichs, and Chae-Yeun Park, "Backpropagation scaling in parameterised quantum circuits", Quantum 9, 1873 (2025).

[29] Satyanarayana Burugupalli, 2025 13th International Conference on Intelligent Systems and Embedded Design (ISED) 974 (2025) ISBN:979-8-3315-6726-2.

[30] Francesco Tacchino, Stefano Mangini, Panagiotis Kl. Barkoutsos, Chiara Macchiavello, Dario Gerace, Ivano Tavernelli, and Daniele Bajoni, "Variational Learning for Quantum Artificial Neural Networks", IEEE Transactions on Quantum Engineering 2, 1 (2021).

[31] Cristina Cîrstoiu, Zoë Holmes, Joseph Iosue, Lukasz Cincio, Patrick J. Coles, and Andrew Sornborger, "Variational fast forwarding for quantum simulation beyond the coherence time", npj Quantum Information 6 1, 82 (2020).

[32] Zhen Qin, "Geometric analysis of variational quantum eigensolver", Journal of Physics A: Mathematical and Theoretical 59 30, 305302 (2026).

[33] Yusen Wu, Zigeng Huang, Jinzhao Sun, Xiao Yuan, Jingbo B Wang, and Dingshun Lv, "Orbital expansion variational quantum eigensolver", Quantum Science and Technology 8 4, 045030 (2023).

[34] Alexey Melnikov, Mohammad Kordzanganeh, Alexander Alodjants, and Ray-Kuang Lee, "Quantum machine learning: from physics to software engineering", Advances in Physics: X 8 1, 2165452 (2023).

[35] Pilsung Kang, "QFFN-BERT: An Empirical Study of CLS-Level Quantum Feed-Forward Modules for Compact Transformer Classifiers", IEEE Access 14, 102693 (2026).

[36] Robert J. P. T. de Keijzer, Luke Y. Visser, Oliver Tse, and Servaas J. J. M. F. Kokkelmans, "Consensus-based qubit configuration optimization for variational algorithms on neutral atom quantum systems", npj Quantum Information 11 1, 186 (2025).

[37] Maria Revythi and Georgia Koukiou, "Quantum Machine Learning and Deep Learning: Fundamentals, Algorithms, Techniques, and Real-World Applications", Machine Learning and Knowledge Extraction 7 3, 75 (2025).

[38] Kasidit Srimahajariyapong, Supanut Thanasilp, and Thiparat Chotibut, "Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms", Communications Physics 9 1, 111 (2026).

[39] Yi Zheng, Cheng Xie, Chenlei Fang, Ying Zhou, Wei Pan, and Haobin Shi, "DVQE: Discrete variational quantum eigensolver for memory allocation in continual learning", Neurocomputing 696, 134086 (2026).

[40] Yu Pan, Yifan Tong, Shibei Xue, and Guofeng Zhang, "Efficient depth selection for the implementation of noisy quantum approximate optimization algorithm", Journal of the Franklin Institute 359 18, 11273 (2022).

[41] Christopher K. Long, Kieran Dalton, Crispin H. W. Barnes, David R. M. Arvidsson-Shukur, and Normann Mertig, "Layering and subpool exploration for adaptive variational quantum eigensolvers: Reducing circuit depth, runtime, and susceptibility to noise", Physical Review A 109 4, 042413 (2024).

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

[43] Shikun Zhang, Zheng Qin, Yongyou Zhang, Yang Zhou, Rui Li, Chunxiao Du, and Zhisong Xiao, "Diffusion‐Enhanced Optimization of Variational Quantum Eigensolver for General Hamiltonians", Advanced Quantum Technologies 9 2, e00766 (2026).

[44] 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).

[45] Ahmed Mohsin Ali, Sahar Qazi, Haider Thaer Abdulhameed Almuqdadi, Neeraj, Osamah S. Majeed, Alabbas Abdulkareem Majeed, Ahmed Abd Temur, Ghassan Haleem Mohsin Al Murshedi, and Mohammed Saleem Waheed, Artificial Intelligence in Precision Drug Design, Volume 1 297 (2026) ISBN:9780443444159.

[46] Lewis W. Anderson, Martin Kiffner, Panagiotis Kl. Barkoutsos, Ivano Tavernelli, Jason Crain, and Dieter Jaksch, "Coarse-grained intermolecular interactions on quantum processors", Physical Review A 105 6, 062409 (2022).

[47] Isabel Nha Minh Le, Oriel Kiss, Julian Schuhmacher, Ivano Tavernelli, and Francesco Tacchino, "Symmetry-invariant quantum machine learning force fields", New Journal of Physics 27 2, 023015 (2025).

[48] Yuta Kikuchi, Conor Mc Keever, Luuk Coopmans, Michael Lubasch, and Marcello Benedetti, "Realization of quantum signal processing on a noisy quantum computer", npj Quantum Information 9 1, 93 (2023).

[49] Michael L. Wall, Paraj Titum, Gregory Quiroz, Michael Foss-Feig, and Kaden R. A. Hazzard, "Tensor-network discriminator architecture for classification of quantum data on quantum computers", Physical Review A 105 6, 062439 (2022).

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

[51] Muhammad Kashif and Muhammad Shafique, 2025 International Joint Conference on Neural Networks (IJCNN) 1 (2025) ISBN:979-8-3315-1042-8.

[52] Yuhan Yao and Yoshihiko Hasegawa, "Linking barren plateaus to effective parameters in deep rotation-gate-based parameterized quantum circuits", Physical Review A 112 6, 062443 (2025).

[53] P. Chandarana, N. N. Hegade, K. Paul, F. Albarrán-Arriagada, E. Solano, A. del Campo, and Xi Chen, "Digitized-counterdiabatic quantum approximate optimization algorithm", Physical Review Research 4 1, 013141 (2022).

[54] Laszlo Gyongyosi and Sandor Imre, "Networked Quantum Services†", Quantum Information & Computation 25 2, 97 (2025).

[55] Ryan LaRose and Brian Coyle, "Robust data encodings for quantum classifiers", Physical Review A 102 3, 032420 (2020).

[56] Louis Schatzki, Martín Larocca, Quynh T. Nguyen, Frédéric Sauvage, and M. Cerezo, "Theoretical guarantees for permutation-equivariant quantum neural networks", npj Quantum Information 10 1, 12 (2024).

[57] Zidong Cui, Pan Zhang, and Ying Tang, "Quantum Flow Matching", PRX Intelligence 1 1, 013009 (2026).

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

[59] Yuxuan Du, Yibo Yang, Dacheng Tao, and Min-Hsiu Hsieh, "Problem-Dependent Power of Quantum Neural Networks on Multiclass Classification", Physical Review Letters 131 14, 140601 (2023).

[60] Xu-Dan Xie, Xingyu Guo, Hongxi Xing, Zheng-Yuan Xue, Dan-Bo Zhang, and Shi-Liang Zhu, "Variational thermal quantum simulation of the lattice Schwinger model", Physical Review D 106 5, 054509 (2022).

[61] Thinh Viet Le and Vassilis Kekatos, "Solving constrained optimization problems via the variational quantum eigensolver with constraints", Physical Review A 110 2, 022430 (2024).

[62] G. Paradezhenko, A. Pervishko, and D. Yudin, "Variatsionnaya kvantovaya optimizatsiya otkrytogo kar'era", Pisʹma v žurnal êksperimentalʹnoj i teoretičeskoj fiziki 119 5-6, 459 (2024).

[63] Panagiotis G. Anastasiou, Yanzhu Chen, Nicholas J. Mayhall, Edwin Barnes, and Sophia E. Economou, "TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit Ansätze", Physical Review Research 6 1, 013254 (2024).

[64] Ifan Williams and Mathieu Pellen, "A general approach to quantum integration of cross sections in high-energy physics", Quantum Science and Technology 10 4, 045017 (2025).

[65] Eoin Carolan, Nathan Keenan, Gabriele Cenedese, and Giuliano Benenti, "Variational preparation of quantum many-body scars with shallow circuits", Physical Review A 114 1, 012613 (2026).

[66] Ranyiliu Chen, Zhixin Song, Xuanqiang Zhao, and Xin Wang, "Variational quantum algorithms for trace distance and fidelity estimation", Quantum Science and Technology 7 1, 015019 (2022).

[67] Yuxuan Du, Xinbiao Wang, Naixu Guo, Zhan Yu, Yang Qian, Kaining Zhang, Min-Hsiu Hsieh, Patrick Rebentrost, and Dacheng Tao, A Gentle Introduction to Quantum Machine Learning 111 (2025) ISBN:978-981-95-1283-6.

[68] Andrea Skolik, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt, and Martin Leib, "Layerwise learning for quantum neural networks", Quantum Machine Intelligence 3 1, 5 (2021).

[69] Arne Wulff, Boyang Chen, Matthew Steinberg, Yinglu Tang, Matthias Möller, and Sebastian Feld, "Quantum computing and tensor networks for laminate design: A novel approach to stacking sequence retrieval", Computer Methods in Applied Mechanics and Engineering 432, 117380 (2024).

[70] Martín Larocca, Frédéric Sauvage, Faris M. Sbahi, Guillaume Verdon, Patrick J. Coles, and M. Cerezo, "Group-Invariant Quantum Machine Learning", PRX Quantum 3 3, 030341 (2022).

[71] Lucas Friedrich, Tiago de Souza Farias, and Jonas Maziero, "Barren plateaus are amplified by the dimension of qudits", Quantum Machine Intelligence 7 1, 56 (2025).

[72] Harshdeep Singh, Sonjoy Majumder, and Sabyashachi Mishra, "Hückel molecular orbital theory on a quantum computer: A scalable system-agnostic variational implementation with compact encoding", The Journal of Chemical Physics 160 19, 194106 (2024).

[73] Fabio Valerio Massoli, Lucia Vadicamo, Giuseppe Amato, and Fabrizio Falchi, "A Leap among Quantum Computing and Quantum Neural Networks: A Survey", ACM Computing Surveys 55 5, 1 (2023).

[74] M Cerezo and Patrick J Coles, "Higher order derivatives of quantum neural networks with barren plateaus", Quantum Science and Technology 6 3, 035006 (2021).

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

[76] Naoki Mitsuda, Tatsuhiro Ichimura, Kouhei Nakaji, Yohichi Suzuki, Tomoki Tanaka, Rudy Raymond, Hiroyuki Tezuka, Tamiya Onodera, and Naoki Yamamoto, "Approximate complex amplitude encoding algorithm and its application to data classification problems", Physical Review A 109 5, 052423 (2024).

[77] Sabri Meyer, Francesco Scala, Francesco Tacchino, and Aurelien Lucchi, "Gradient scalability and Taylor surrogation of quantum cost landscapes", Physical Review Research 8 2, 023325 (2026).

[78] Patrick Huembeli and Alexandre Dauphin, "Characterizing the loss landscape of variational quantum circuits", Quantum Science and Technology 6 2, 025011 (2021).

[79] Taylor L. Patti, Khadijeh Najafi, Xun Gao, and Susanne F. Yelin, "Entanglement devised barren plateau mitigation", Physical Review Research 3 3, 033090 (2021).

[80] Li-Wei Yu, Weikang Li, Qi Ye, Zhide Lu, Zizhao Han, and Dong-Ling Deng, "Expressibility-induced concentration of quantum neural tangent kernels", Reports on Progress in Physics 87 11, 110501 (2024).

[81] Mrittunjoy Guha Majumdar, "Fault-tolerant quantum neural networks suppressing barren plateaus via localised cost functions and shallow parametrised architectures", Pramana 100 2, 93 (2026).

[82] Muhammad Kashif and Saif Al-Kuwari, "The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs", International Journal of Parallel, Emergent and Distributed Systems 38 5, 362 (2023).

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

[84] Mohammad Kordzanganeh, Markus Buchberger, Basil Kyriacou, Maxim Povolotskii, Wilhelm Fischer, Andrii Kurkin, Wilfrid Somogyi, Asel Sagingalieva, Markus Pflitsch, and Alexey Melnikov, "Benchmarking Simulated and Physical Quantum Processing Units Using Quantum and Hybrid Algorithms", Advanced Quantum Technologies 6 8, 2300043 (2023).

[85] Bingzhi Zhang and Quntao Zhuang, "Energy-dependent barren plateau in bosonic variational quantum circuits", Quantum Science and Technology 10 1, 015009 (2025).

[86] Jonathan Wei Zhong Lau, Tobias Haug, Leong Chuan Kwek, and Kishor Bharti, "NISQ Algorithm for Hamiltonian simulation via truncated Taylor series", SciPost Physics 12 4, 122 (2022).

[87] Zoë Holmes, Kunal Sharma, M. Cerezo, and Patrick J. Coles, "Connecting Ansatz Expressibility to Gradient Magnitudes and Barren Plateaus", PRX Quantum 3 1, 010313 (2022).

[88] 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).

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

[90] Hassan Ugail, Newton Howard, and Zeheng Wang, "PsiAudit: An open-source toolkit for auditing symmetry-organised complexity in equivariant quantum neural networks", PLOS One 21 7, e0353739 (2026).

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

[92] Youngmin Kim, Enhyeok Jang, Hyungseok Kim, Seungwoo Choi, Changheon Lee, Donghwi Kim, Woomin Kyoung, Kyujin Shin, and Won Woo Ro, "Distribution-adaptive dynamic shot optimization for variational quantum algorithms", Physical Review Research 7 4, 043253 (2025).

[93] Akimoto Nakayama, Kosuke Mitarai, Leonardo Placidi, Takanori Sugimoto, and Keisuke Fujii, "VQE-generated quantum circuit dataset for machine learning", Physical Review Research 7 3, 033048 (2025).

[94] Rodrigo Coelho, André Sequeira, and Luís Paulo Santos, "VQC-based reinforcement learning with data re-uploading: performance and trainability", Quantum Machine Intelligence 6 2, 53 (2024).

[95] Yifeng Peng, Xinyi Li, Zhemin Zhang, Samuel Yen-Chi Chen, Zhiding Liang, and Ying Wang, 2025 IEEE International Conference on Quantum Computing and Engineering (QCE) 1708 (2025) ISBN:979-8-3315-5736-2.

[96] Yudai Suzuki, Hiroshi Yano, Rudy Raymond, and Naoki Yamamoto, 2021 IEEE International Conference on Quantum Computing and Engineering (QCE) 1 (2021) ISBN:978-1-6654-1691-7.

[97] Babatunde M. Ayeni, "Efficient particle-conserving symmetric quantum circuits", Physical Review A 111 2, 022629 (2025).

[98] Pranav Chandarana, Pablo Suárez Vieites, Narendra N Hegade, Enrique Solano, Yue Ban, and Xi Chen, "Meta-learning digitized-counterdiabatic quantum optimization", Quantum Science and Technology 8 4, 045007 (2023).

[99] Shabnam Jabeen, Dmytro Kurdydyk, Aadi Palnitkar, Mihir Talati, Jeffrey Yan, and Jinghong Yang, "Quantum machine learning for state tomography using classical data", Quantum Machine Intelligence 8 1, 40 (2026).

[100] Zhehao Yi and Rahul Bhadani, Frontiers in Optics + Laser Science 2025 (FiO, LS) FW6D.3 (2025) ISBN:978-1-957171-52-4.

[101] Xinwei Lee, Xinjian Yan, Ningyi Xie, Dongsheng Cai, Yoshiyuki Saito, and Nobuyoshi Asai, "Iterative layerwise training for the quantum approximate optimization algorithm", Physical Review A 109 5, 052406 (2024).

[102] Kouhei Nakaji, Hiroyuki Tezuka, and Naoki Yamamoto, "Quantum-classical hybrid neural networks in the neural tangent kernel regime", Quantum Science and Technology 9 1, 015022 (2024).

[103] Rui Huang and Haibo Yi, "A fully quantum-native recurrent neural network for end-to-end sequential learning on NISQ hardware", Machine Learning: Science and Technology 7 3, 035002 (2026).

[104] Zhide Lu, Pei-Xin Shen, and Dong-Ling Deng, "Markovian Quantum Neuroevolution for Machine Learning", Physical Review Applied 16 4, 044039 (2021).

[105] Yijie Zhu, Richard Jiang, Qiang Ni, and Ahmed Bouridane, "Enable Quantum Graph Neural Networks on a Single Qubit With Quantum Walk", IEEE Transactions on Artificial Intelligence 7 5, 2496 (2026).

[106] Michael R. Geller, Zoë Holmes, Patrick J. Coles, and Andrew Sornborger, "Experimental quantum learning of a spectral decomposition", Physical Review Research 3 3, 033200 (2021).

[107] Alexey Uvarov, Jacob D. Biamonte, and Dmitry Yudin, "Variational quantum eigensolver for frustrated quantum systems", Physical Review B 102 7, 075104 (2020).

[108] Max Wilson, Rachel Stromswold, Filip Wudarski, Stuart Hadfield, Norm M. Tubman, and Eleanor G. Rieffel, "Optimizing quantum heuristics with meta-learning", Quantum Machine Intelligence 3 1, 13 (2021).

[109] Nikita A. Nemkov, Evgeniy O. Kiktenko, and Aleksey K. Fedorov, "Barren plateaus swamped with traps", Physical Review A 111 1, 012441 (2025).

[110] Lorenzo Pastori, Arthur Grundner, Veronika Eyring, and Mierk Schwabe, "Quantum neural networks for cloud cover parameterizations in climate models", Machine Learning: Earth 2 1, 015008 (2026).

[111] Roeland Wiersema, Cunlu Zhou, Juan Felipe Carrasquilla, and Yong Baek Kim, "Measurement-induced entanglement phase transitions in variational quantum circuits", SciPost Physics 14 6, 147 (2023).

[112] Kun Wang, Zhixin Song, Xuanqiang Zhao, Zihe Wang, and Xin Wang, "Detecting and quantifying entanglement on near-term quantum devices", npj Quantum Information 8 1, 52 (2022).

[113] Yu Shee, Tzu-Lan Yeh, Jen-Yueh Hsiao, Ann Yang, Yen-Chu Lin, and Min-Hsiu Hsieh, "Quantum simulation of preferred tautomeric state prediction", npj Quantum Information 9 1, 102 (2023).

[114] Sebastiano Corli, Lorenzo Moro, Daniele Dragoni, Massimiliano Dispenza, and Enrico Prati, "Quantum machine learning algorithms for anomaly detection: A review", Future Generation Computer Systems 166, 107632 (2025).

[115] Jules Tilly, Hongxiang Chen, Shuxiang Cao, Dario Picozzi, Kanav Setia, Ying Li, Edward Grant, Leonard Wossnig, Ivan Rungger, George H. Booth, and Jonathan Tennyson, "The Variational Quantum Eigensolver: A review of methods and best practices", Physics Reports 986, 1 (2022).

[116] Anqi 安琪 Zhang 张, Chunhui 春辉 Wu 武, and Shengmei 生妹 Zhao 赵, "Gray code based gradient-free optimization algorithm for parameterized quantum circuit", Chinese Physics B 33 2, 020311 (2024).

[117] Jianshe Xie, Chen Xu, Chenhao Yin, Yumin Dong, and Zhirong Zhang, "Natural Evolutionary Gradient Descent Strategy for Variational Quantum Algorithms", Intelligent Computing 2, 0042 (2023).

[118] Shengbin Wang, Guihui Li, Zhimin Wang, Zhaoyun Chen, Peng Wang, Yongjian Gu, Yu-Chun Wu, and Guo-Ping Guo, "Improving the trainability of variational quantum eigensolvers on noisy intermediate-scale quantum computers for solving portfolio optimization using convex interpolation", Physical Review A 113 5, 052441 (2026).

[119] Chao-Han Huck Yang, Bo Li, Yu Zhang, Nanxin Chen, Tara N. Sainath, Sabato Marco Siniscalchi, and Chin-Hui Lee, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 1 (2023) ISBN:978-1-7281-6327-7.

[120] Weiyuan Gong, Dong Yuan, Weikang Li, and Dong-Ling Deng, "Enhancing quantum adversarial robustness by randomized encodings", Physical Review Research 6 2, 023020 (2024).

[121] Tomislav Piskor, Jan-Michael Reiner, Sebastian Zanker, Nicolas Vogt, Michael Marthaler, Frank K. Wilhelm, and Florian G. Eich, "Using gradient-based algorithms to determine ground-state energies on a quantum computer", Physical Review A 105 6, 062415 (2022).

[122] Christa Zoufal, Ryan V. Mishmash, Nitin Sharma, Niraj Kumar, Aashish Sheshadri, Amol Deshmukh, Noelle Ibrahim, Julien Gacon, and Stefan Woerner, "Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection", Quantum 7, 909 (2023).

[123] Yujin Kim and Daniel K. Park, "Expressivity of deterministic quantum computation with one qubit", Physical Review A 111 2, 022429 (2025).

[124] Chisomo Daka and Somnath Bhattacharyya, "NISQ in practice: navigating noise, scale, and hardware constraints of near-term devices in quantum machine learning workflows", New Journal of Physics 28 8, 081201 (2026).

[125] Marco Maronese, Claudio Destri, and Enrico Prati, "Quantum activation functions for quantum neural networks", Quantum Information Processing 21 4, 128 (2022).

[126] Ratun Rahman and Dinh C. Nguyen, "Escaping Barren Plateaus in Variational Quantum Algorithms Using Negative Learning Rate in Quantum Internet of Things", IEEE Internet of Things Journal 13 4, 7417 (2026).

[127] Mandaar B. Pande, Lecture Notes in Electrical Engineering 1221, 3 (2025) ISBN:978-981-97-4710-8.

[128] Albha O’Dwyer Boyle and Reza Nikandish, "A Hybrid Quantum-Classical Generative Adversarial Network for Near-Term Quantum Processors", IEEE Access 12, 102688 (2024).

[129] Elias X. Huber, Benjamin Y. L. Tan, Paul R. Griffin, and Dimitris G. Angelakis, "Exponential qubit reduction in optimization for financial transaction settlement", EPJ Quantum Technology 11 1, 52 (2024).

[130] Pinaki Sen, Amandeep Singh Bhatia, Kamalpreet Singh Bhangu, Ahmed Elbeltagi, and Thippa Reddy Gadekallu, "Variational quantum classifiers through the lens of the Hessian", PLOS ONE 17 1, e0262346 (2022).

[131] Hiroyuki Tezuka, Kouhei Nakaji, Takahiko Satoh, and Naoki Yamamoto, "Grover search revisited: Application to image pattern matching", Physical Review A 105 3, 032440 (2022).

[132] Yusen Wu and Jingbo B Wang, "Estimating Gibbs partition function with quantum Clifford sampling", Quantum Science and Technology 7 2, 025006 (2022).

[133] Christof Wendenius, Eileen Kuehn, and Achim Streit, Lecture Notes in Computer Science 13717, 515 (2023) ISBN:978-3-031-26418-4.

[134] Zhiqiang Cai, Jialin Chen, Ke Xu, and Lingli Wang, "Recognizing Good Variational Quantum Circuits with Monte Carlo Tree Search", (2023).

[135] Kosuke Mitarai, Yasunari Suzuki, Wataru Mizukami, Yuya O. Nakagawa, and Keisuke Fujii, "Quadratic Clifford expansion for efficient benchmarking and initialization of variational quantum algorithms", Physical Review Research 4 3, 033012 (2022).

[136] Maxine T. Khumalo, Hazel A. Chieza, Krupa Prag, and Matthew Woolway, "An investigation of IBM quantum computing device performance on combinatorial optimisation problems", Neural Computing and Applications 37 2, 611 (2025).

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[308] Yong-Xin Yao, Niladri Gomes, Feng Zhang, Cai-Zhuang Wang, Kai-Ming Ho, Thomas Iadecola, and Peter P. Orth, "Adaptive Variational Quantum Dynamics Simulations", PRX Quantum 2 3, 030307 (2021).

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[312] Xianzhi Huang, Lili Niu, Jie Chen, Lichao Li, Kashif Hayat, and Weiping Liu, "Quantum and classical computational synergy for emerging contaminants management: Advanced insights into cytochrome P450 metabolic mechanisms", Critical Reviews in Environmental Science and Technology 54 24, 1827 (2024).

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[404] Stefano Mangini, "Variational quantum algorithms for machine learning: theory and applications", arXiv:2306.09984, (2023).

[405] Gerhard Stenzel, Sebastian Zielinski, Michael Kölle, Philipp Altmann, Jonas Nüßlein, and Thomas Gabor, "Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting", arXiv:2404.09213, (2024).

[406] Marvin Richter, Abhishek Y. Dubey, Axel Plinge, Christopher Mutschler, Daniel D. Scherer, and Michael J. Hartmann, "Quantum Wasserstein Compilation: Unitary Compilation using the Quantum Earth Mover's Distance", arXiv:2409.05849, (2024).

[407] Noah L. Wach, Manuel S. Rudolph, Fred Jendrzejewski, and Sebastian Schmitt, "Data re-uploading with a single qudit", arXiv:2302.13932, (2023).

[408] Michelle Gelman, "A Survey of Methods for Mitigating Barren Plateaus for Parameterized Quantum Circuits", arXiv:2406.14285, (2024).

[409] Sabrina Herbst, Sandeep Suresh Cranganore, Vincenzo De Maio, and Ivona Brandic, "Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks", arXiv:2410.09470, (2024).

[410] Miguel Angel Lopez-Ruiz, Emily L. Tucker, Emma M. Arnold, Evgeny Epifanovsky, Ananth Kaushik, and Martin Roetteler, "A Non-Variational Quantum Approach to the Job Shop Scheduling Problem", arXiv:2510.26859, (2025).

[411] Lorenzo Pastori, Arthur Grundner, Veronika Eyring, and Mierk Schwabe, "Quantum Neural Networks for Cloud Cover Parameterizations in Climate Models", arXiv:2502.10131, (2025).

[412] Michael Kölle, Leonhard Klingert, Julian Schönberger, Philipp Altmann, Tobias Rohe, and Claudia Linnhoff-Popien, "Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits", arXiv:2509.11190, (2025).

[413] Daniele Lizzio Bosco, Shuteng Wang, Giuseppe Serra, and Vladislav Golyanik, "QNeRF: Neural Radiance Fields on a Simulated Gate-Based Quantum Computer", arXiv:2601.05250, (2026).

[414] Alexander Mandl, Johanna Barzen, Marvin Bechtold, Frank Leymann, and Lavinia Stiliadou, "Loss Behavior in Supervised Learning with Entangled States", arXiv:2509.10141, (2025).

[415] Mehri Mehrnia and Mohammed S. M. Elbaz, "Stochastic Entanglement Configuration for Constructive Entanglement Topologies in Quantum Machine Learning with Application to Cardiac MRI", arXiv:2507.11401, (2025).

[416] Anurag Anshu, "An energy-based uncertainty principle and low-energy state preparation", arXiv:2603.15495, (2026).

[417] Yihan Huang and Yangshuai Wang, "Analysis of Hessian Scaling for Local and Global Costs in Variational Quantum Algorithm", arXiv:2602.00783, (2026).

[418] Ngoc Nhi Nguyen, Thai T Vu, John Le, Hoa Khanh Dam, Dung Hoang Duong, and Dinh Thai Hoang, "BRIDG-Q: Barren-Plateau-Resilient Initialisation with Data-Aware LLM-Generated Quantum Circuits", arXiv:2603.23979, (2026).

[419] Nishikanta Mohanty, Bikash K. Behera, Badshah Mukherjee, Pravat Dash, Giuseppe Sergioli, and Roberto Giuntini, "QARIMA: A Quantum Approach To Classical Time Series Analysis", arXiv:2604.08277, (2026).

[420] Tomohiro Hashizume, Zhengjun Wang, Frank Schlawin, and Dieter Jaksch, "Quantum computation at the edge of chaos", arXiv:2604.15441, (2026).

[421] Hassan Ugail and Newton Howard, "A Coherence Law for Trainability in Noisy Equivariant Quantum Neural Networks", arXiv:2606.30688, (2026).

[422] Gordon Ma and Xiufan Li, "Trainability Beyond Linearity in Variational Quantum Objectives", arXiv:2604.18846, (2026).

The above citations are from Crossref's cited-by service (last updated successfully 2026-08-19 09:48:13) and SAO/NASA ADS (last updated successfully 2026-08-18 21:29:42). The list may be incomplete as not all publishers provide suitable and complete citation data.

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