Academic literature on the topic 'Variational quantum circuits'

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Journal articles on the topic "Variational quantum circuits"

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Plekhanov, Kirill, Matthias Rosenkranz, Mattia Fiorentini, and Michael Lubasch. "Variational quantum amplitude estimation." Quantum 6 (March 17, 2022): 670. http://dx.doi.org/10.22331/q-2022-03-17-670.

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We propose to perform amplitude estimation with the help of constant-depth quantum circuits that variationally approximate states during amplitude amplification. In the context of Monte Carlo (MC) integration, we numerically show that shallow circuits can accurately approximate many amplitude amplification steps. We combine the variational approach with maximum likelihood amplitude estimation [Y. Suzuki et al., Quantum Inf. Process. 19, 75 (2020)] in variational quantum amplitude estimation (VQAE). VQAE typically has larger computational requirements than classical MC sampling. To reduce the variational cost, we propose adaptive VQAE and numerically show in 6 to 12 qubit simulations that it can outperform classical MC sampling.
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Yetiş, Hasan, and Mehmet Karaköse. "A New Framework Containing Convolution and Pooling Circuits for Image Processing and Deep Learning Applications with Quantum Computing Implementation." Traitement du Signal 39, no. 2 (2022): 501–12. http://dx.doi.org/10.18280/ts.390212.

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The resource need for deep learning and quantum computers' high computing power potential encourage collaboration between the two fields. Today, variational quantum circuits are used to perform the convolution operation with quantum computing. However, the results produced by variational circuits do not show a direct resemblance to the classical convolution operation. Because classical data is encoded into quantum data with their exact values in value-encoded methods, in contrast to variational quantum circuits, arithmetical operations can be applied with high accuracy. In this study, value-encoded quantum circuits for convolution and pooling operations are proposed to apply deep learning in quantum computers in a traditional and proven way. To construct the convolution and pooling operations, some modules such as addition, multiplication, division, and comparison are created. In addition, a window-based framework for quantum image processing applications is proposed. The generated convolution and pooling circuits are simulated on the IBM QISKIT simulator in parallel thanks to the proposed framework. The obtained results are verified by the expected results. Due to the limitations of quantum simulators and computers in the NISQ era, the used grayscale images are resized to 8x8 and the resolution of the images is reduced to 3 qubits. With developing the quantum technologies, the proposed approach can be applied for bigger and higher resolution images. Although the proposed method causes more qubit usage and circuit depth compared to variational convolutional circuits, the results they produce are exactly the same as the classical convolution process.
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Salmeron, Jose L., and Isabel Fernández-Palop. "Variational Quantum Circuit Topology Grid Search for Hypocalcemia Following Thyroid Surgery." Mathematics 11, no. 17 (2023): 3659. http://dx.doi.org/10.3390/math11173659.

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Quantum computing’s potential to revolutionise medical applications has spurred interest in leveraging quantum algorithms for healthcare challenges. In this research, the authors explored the application of variational quantum circuits to predicting hypocalcemia risk following thyroid surgery. Hypocalcemia, resulting from hypoparathyroidism, is a common post-surgical complication. This novel approach includes a topology grid search of the variational quantum circuits. To execute the grid search, our research employed a classical optimiser that guided the adjustment of different circuit topologies, assessing their impact on predictive performance. Our research used, as relevant features, an intra-operative PTH (parathyroid hormone) at 10 min post-removal and percentage decrease of pre-operative and intra-operative PTH levels. The findings revealed insights into the interplay between variational quantum circuit topologies and predictive accuracy for hypocalcemia risk assessment.
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Sen, Pinaki, Amandeep Singh Bhatia, Kamalpreet Singh Bhangu, and Ahmed Elbeltagi. "Variational quantum classifiers through the lens of the Hessian." PLOS ONE 17, no. 1 (2022): e0262346. http://dx.doi.org/10.1371/journal.pone.0262346.

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In quantum computing, the variational quantum algorithms (VQAs) are well suited for finding optimal combinations of things in specific applications ranging from chemistry all the way to finance. The training of VQAs with gradient descent optimization algorithm has shown a good convergence. At an early stage, the simulation of variational quantum circuits on noisy intermediate-scale quantum (NISQ) devices suffers from noisy outputs. Just like classical deep learning, it also suffers from vanishing gradient problems. It is a realistic goal to study the topology of loss landscape, to visualize the curvature information and trainability of these circuits in the existence of vanishing gradients. In this paper, we calculate the Hessian and visualize the loss landscape of variational quantum classifiers at different points in parameter space. The curvature information of variational quantum classifiers (VQC) is interpreted and the loss function’s convergence is shown. It helps us better understand the behavior of variational quantum circuits to tackle optimization problems efficiently. We investigated the variational quantum classifiers via Hessian on quantum computers, starting with a simple 4-bit parity problem to gain insight into the practical behavior of Hessian, then thoroughly analyzed the behavior of Hessian’s eigenvalues on training the variational quantum classifier for the Diabetes dataset. Finally, we show how the adaptive Hessian learning rate can influence the convergence while training the variational circuits.
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Ogur, Besir, and Ihsan Yılmaz. "The effect of superposition and entanglement on hybrid quantum machine learning for weather forecasting." Quantum Information & Computation 23, no. 3&4 (2023): 181–94. http://dx.doi.org/10.26421/qic23.3-4-1.

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Recently, proposed algorithms for quantum computing and generated quantum computer technologies continue to evolve. On the other hand, machine learning has become an essential method for solving many problems such as computer vision, natural language processing, prediction and classification. Quantum machine learning is a new field developed by combining the advantages of these two primary methods. As a hybrid approach to quantum and classical computing, variational quantum circuits are a form of machine learning that allows predicting an output value against input variables. In this study, the effects of superposition and entanglement on weather forecasting, were investigated using a variational quantum circuit model when the dataset size is small. The use of the entanglement layer between the variational layers has made significant improvements on the circuit performance. The use of the superposition layer before the data encoding layer resulted in the use of less variational layers.
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Li, Guangxi, Zhixin Song, and Xin Wang. "VSQL: Variational Shadow Quantum Learning for Classification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 8357–65. http://dx.doi.org/10.1609/aaai.v35i9.17016.

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Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes the classical shadows of quantum data, which fundamentally represent the side information of quantum data with respect to certain physical observables. Specifically, we first use variational shadow quantum circuits to extract classical features in a convolution way and then utilize a fully-connected neural network to complete the classification task. We show that this method could sharply reduce the number of parameters and thus better facilitate quantum circuit training. Simultaneously, less noise will be introduced since fewer quantum gates are employed in such shadow circuits. Moreover, we show that the Barren Plateau issue, a significant gradient vanishing problem in quantum machine learning, could be avoided in VSQL. Finally, we demonstrate the efficiency of VSQL in quantum classification via numerical experiments on the classification of quantum states and the recognition of multi-labeled handwritten digits. In particular, our VSQL approach outperforms existing variational quantum classifiers in the test accuracy in the binary case of handwritten digit recognition and notably requires much fewer parameters.
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Lockwood, Owen, and Mei Si. "Reinforcement Learning with Quantum Variational Circuit." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, no. 1 (2020): 245–51. http://dx.doi.org/10.1609/aiide.v16i1.7437.

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The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning.
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Monbroussou, Léo, Eliott Z. Mamon, Jonas Landman, Alex B. Grilo, Romain Kukla, and Elham Kashefi. "Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning." Quantum 9 (May 15, 2025): 1745. https://doi.org/10.22331/q-2025-05-15-1745.

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Quantum machine learning (QML) has become a promising area for real world applications of quantum computers, but near-term methods and their scalability are still important research topics. In this context, we analyze the trainability and controllability of specific Hamming weight preserving variational quantum circuits (VQCs). These circuits use qubit gates that preserve subspaces of the Hilbert space, spanned by basis states with fixed Hamming weight k. In this work, we first design and prove the feasibility of new heuristic data loaders, performing quantum amplitude encoding of (nk)-dimensional vectors by training an n-qubit quantum circuit. These data loaders are obtained using controllability arguments, by checking the Quantum Fisher Information Matrix (QFIM)'s rank. Second, we provide a theoretical justification for the fact that the rank of the QFIM of any VQC state is almost-everywhere constant, which is of separate interest. Lastly, we analyze the trainability of Hamming weight preserving circuits, and show that the variance of the l2 cost function gradient is bounded according to the dimension (nk) of the subspace. This proves conditions of existence/lack of Barren Plateaus for these circuits, and highlights a setting where a recent conjecture on the link between controllability and trainability of variational quantum circuits does not apply.
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Funcke, Lena, Tobias Hartung, Karl Jansen, Stefan Kühn, and Paolo Stornati. "Dimensional Expressivity Analysis of Parametric Quantum Circuits." Quantum 5 (March 29, 2021): 422. http://dx.doi.org/10.22331/q-2021-03-29-422.

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Parametric quantum circuits play a crucial role in the performance of many variational quantum algorithms. To successfully implement such algorithms, one must design efficient quantum circuits that sufficiently approximate the solution space while maintaining a low parameter count and circuit depth. In this paper, develop a method to analyze the dimensional expressivity of parametric quantum circuits. Our technique allows for identifying superfluous parameters in the circuit layout and for obtaining a maximally expressive ansatz with a minimum number of parameters. Using a hybrid quantum-classical approach, we show how to efficiently implement the expressivity analysis using quantum hardware, and we provide a proof of principle demonstration of this procedure on IBM's quantum hardware. We also discuss the effect of symmetries and demonstrate how to incorporate or remove symmetries from the parametrized ansatz.
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Cai, Haoyuan, Qi Ye, and Dong-Ling Deng. "Sample complexity of learning parametric quantum circuits." Quantum Science and Technology 7, no. 2 (2022): 025014. http://dx.doi.org/10.1088/2058-9565/ac4f30.

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Abstract Quantum computers hold unprecedented potentials for machine learning applications. Here, we prove that physical quantum circuits are probably approximately correct learnable on a quantum computer via empirical risk minimization: to learn a parametric quantum circuit with at most n c gates and each gate acting on a constant number of qubits, the sample complexity is bounded by O ~ ( n c + 1 ) . In particular, we explicitly construct a family of variational quantum circuits with O(n c+1) elementary gates arranged in a fixed pattern, which can represent all physical quantum circuits consisting of at most n c elementary gates. Our results provide a valuable guide for quantum machine learning in both theory and practice.
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Dissertations / Theses on the topic "Variational quantum circuits"

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Murça, Miguel Eduardo de Vasconcelos Morais. "Molecular Geometry Calculations Using a Novel Quantum Variational Approach." Master's thesis, 2020. http://hdl.handle.net/10316/92475.

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Dissertação de Mestrado em Física apresentada à Faculdade de Ciências e Tecnologia<br>O trabalho apresentado nesta tese surge como uma extensão de duas linhas separadas de trabalho: por um lado, advém do trabalho prévio do grupo QUANTIC, da Universidade de Barcelona, onde parte do trabalho de tese aqui apresentado foi desenvolvido, sob um estágio Erasmus. O grupo QUANTIC tem como área principal de investigação a computação quântica; dos seus trabalhos recentes constam aplicações de Algoritmos Quânticos Variacionais (Quantum Variational Algorithms, QVAs) e geralmente computação quântica a diferentes problemas. Por outro lado, a publicação de 2008 por Bravyi, DiVincenzo, Loss e Terhal foi instrumental na elaboração do trabalho aqui apresentado, permitindo concretizar o objectivo proposto: construir um método para o cálculo de geometria molecular minimizante da energia, com requerimentos quânticos computacionais mínimos, tanto em termos da qualidade dos qubits utilizados, como em termos do número de qubits necessários. No contexto desse objectivo, uma parte substancial do trabalho desenvolvido nesta tese foi dedicado à construção de um simulador de circuitos quânticos, a fim de explorar as dificuldades teóricas e técnicas inerentes ao desenvolvimento de uma simulação total de um algoritmo quântico variacional. Na secção 1 é apresentado o material teórico de base ao trabalho desenvolvido nesta tese, nomeadamente: a aproximação de Born-Oppenheimer e a segunda quantização de um Hamiltoniano molecular (secção 1.1), a transformação deste Hamiltoniano para uma forma favorável à utilização de um computador quântico(secção 1.2), Algoritmos Quânticos Variacionais e as suas vantagens no regime atual de computação quântica (Noisy Intermediate-Scale Quantum, NISQ; secção 1.3), e finalmente a transformada de Schrieffer-Wolff (secção 1.4).As secções 3 e 4 formam o trabalho nuclear desta tese: na secção 3 apresenta-se o desenvolvimento "de raíz" de uma biblioteca em C/Python de simulação de circuitos quânticos e algoritmos quânticos variacionais. A biblioteca foi denominada QOP, como acrónimo de Quantum OPtimizer. Na secção 4 apresenta-se um método original para o cálculo de geometria molecular minimizante da energia (ou, mais genericamente, dos parâmetros minimizantes de energia de um Hamiltoniano parametrizado). Finalmente, o trabalho desenvolvido é aplicado a sistemas moleculares de teste, nomeadamente H2, HLi e O2, sendo os resultados obtidos para estes sistemas apresentados e discutidos, respetivamente, nas secções 6 e 7. Observa-se que a técnica proposta na seccção 4 é bem sucedida para alguns dos sistemas considerados. Verifica-se também que não é por vezes possível obter um comprimento de ligação molecular, podendo-se isso relacionar com o processo quântico variacional, mas também com o processo pelo qual se reduz a localidade dos Hamiltonianos considerados. Ainda assim, obtêm-se, pelo processo original proposto, comprimentos de ligação comparáveis aos obtidos com um tratamento Hartree-Fock para vários sistemas.<br>The work in this thesis bridges two separate lines of previous work: on one hand, it stems from the work of the QUANTIC group, of the University of Barcelona, who actively research on quantum computing, and with whom part of the work developed in the context of this thesis was done, under an Erasmus internship. The recent work of the group includes Quantum Variational Algorithms (QVAs) and applications of QVAs and generally quantum computing to different problems. On the other hand, the 2008 publication by Bravyi, DiVincenzo, Loss and Terhal was a starting point for the development of this thesis's work; the methodology therein presented is fundamental in achieving the goal for this thesis: to develop a new, quantum device oriented, method for obtaining the geometric parameters of a molecule (or otherwise physical parameters) that result in the lowest possible energy, and that has low quantum computational requirements (in qubit quality and number). In the context of this goal, a significant part of the thesis's work effort was dedicated to building a quantum circuit simulator from scratch, to explore theoretical and technical bottlenecks in a "full-stack" approach to simulating Quantum Variational Algorithms.In section 1 we present material which constitutes the background of the thesis’s work, namely the Born-Oppenheimer approximation and second-quantization of a molecular Hamiltonian (section 1.1), and how we may then translate such a second-quantized Hamiltonian into a form that can be evaluated using a quantum computer (section 1.2); Quantum Variational Algorithms and their advantages in a Noisy Intermediate-Scale Quantum (NISQ) regime (section 1.3), and finally the Schrieffer-Wolff transformation (section 1.4). Sections 3 and 4 form the core of this thesis’s work, corresponding to the development "from scratch" of a C/Python library to simulate quantum circuits and quantum variational algorithms (named QOP; section 3) and the elaboration of an original method for molecular geometric parameter calculation, or generally minimal energy parameter determination for some parameterized Hamiltonian (sections 4 and 5).Finally, we apply the developed work to a few selected systems (H2 , HLi, O2), presenting and discussing the obtained results in, respectively, sections 6 and 7, where we show that the technique proposed to calculate energy minimizing bond lengths is successful in some test cases, but may fail due to either the way in which the locality of the Hamiltonian is reduced or due to the quantum variational process. Despite these shortcomings, we obtain, using the technique, bond lengths comparable to those obtained using a Hartree-Fock approach.<br>Outro - Parte do trabalho da tese apresentada foi desenvolvido no âmbito de um estágio Erasmus+, que decorreu entre 01-09-2019 e 27-02-2020
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Book chapters on the topic "Variational quantum circuits"

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Conti, Claudio. "Variational Circuits for Quantum Solitons." In Quantum Science and Technology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-44226-1_13.

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Schuld, Maria, and Francesco Petruccione. "Variational Circuits as Machine Learning Models." In Quantum Science and Technology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83098-4_5.

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Rizvi, Syed Muhammad Abuzar, Muhammad Shohibul Ulum, Naema Asif, and Hyundong Shin. "Neural Networks with Variational Quantum Circuits." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47359-3_15.

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Gajjar, Pranshav, Zhenyu Zuo, Yanghepu Li, and Liang Zhao. "Enhancing Graph Convolutional Networks with Variational Quantum Circuits for Drug Activity Prediction." In Third Congress on Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9379-4_57.

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Kölle, Michael, Alexander Feist, Jonas Stein, Sebastian Wölckert, and Claudia Linnhoff-Popien. "Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-97635-3_33.

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Kölle, Michael, Karola Schneider, Sabrina Egger, et al. "Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87327-0_3.

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Karur Mudugal Mathad, Rajashekharaiah, Abhishek Saurabh, Aditya Mishra, et al. "Transfer Learning Using Variational Quantum Circuit." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95502-1_20.

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Cardama, F. Javier, Jorge Vázquez-Pérez, Tomás F. Pena, Juan C. Pichel, and Andrés Gómez. "Quantum Compilation Process: A Survey." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90200-0_9.

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Abstract Quantum compilation, critical for bridging high-level quantum programming and physical hardware, faces unique challenges distinct from classical compilation. As quantum computing advances, scalable and efficient quantum compilation methods become necessary. This paper surveys the landscape of quantum compilation, detailing the processes of qubit mapping and circuit optimization, and emphasizing the need for integration with classical computing to harness quantum advantages. Techniques such as Variational Quantum Eigensolver (VQE) exemplify hybrid approaches, highlighting the potential synergy between quantum and classical systems. It is concluded that, while quantum compilation retains many classic methodologies, it introduces novel complexities and opportunities for optimization and verification, essential for the evolving field of quantum computing.
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Lariane, Mohcene Mouad, and Hacene Belhadef. "Variational Circuit Based Hybrid Quantum-Classical Algorithm VC-HQCA." In Quantum Computing: Applications and Challenges. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59318-5_2.

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Grassl, Markus. "Variations on Encoding Circuits for Stabilizer Quantum Codes." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20901-7_9.

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Conference papers on the topic "Variational quantum circuits"

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Liu, Chen-Yu, Kuan-Cheng Chen, and Chu-Hsuan Abraham Lin. "Learning Quantum Phase Estimation by Variational Quantum Circuits." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651206.

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Liu, Chen-Yu, Samuel Yen-Chi Chen, Kuan-Cheng Chen, Wei-Jia Huang, and Yen-Jui Chang. "Programming Variational Quantum Circuits with Quantum-Train Agent." In 2025 International Conference on Quantum Communications, Networking, and Computing (QCNC). IEEE, 2025. https://doi.org/10.1109/qcnc64685.2025.00091.

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Li, Chuang, Zhaolin Liu, and Shibin Zhang. "Multiclassification quantum neural network based on variational quantum circuits." In Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), edited by Feng Yin and Zehui Zhan. SPIE, 2024. http://dx.doi.org/10.1117/12.3045429.

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Tseng, Huan-Hsin, Hsin-Yi Lin, Samuel Yen-Chi Chen, and Shinjae Yoo. "Transfer Learning Analysis of Variational Quantum Circuits." In 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2025. https://doi.org/10.1109/icasspw65056.2025.11011037.

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Gushanskiy, Sergey, Viktor Potapov, and Maxim Polenov. "Quantum Neural Networks: Bridging Topological Structures and Variational Quantum Circuits." In 2025 International Russian Smart Industry Conference (SmartIndustryCon). IEEE, 2025. https://doi.org/10.1109/smartindustrycon65166.2025.10986184.

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Berti, Alessandro, Giacomo Antonioli, Anna Bernasconi, Gianna M. Del Corso, Riccardo Guidotti, and Alessandro Poggiali. "Variational Compression of Circuits for State Preparation." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10250.

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Chen, Samuel Yen-Chi. "Learning to Program Variational Quantum Circuits with Fast Weights." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650743.

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Burgstahler, Blake, Ellis Wilson, Scott Pakin, and Frank Mueller. "Synthesis of Approximate Parametric Circuits for Variational Quantum Algorithms." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.00019.

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Kulshrestha, Ankit, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa, Bao Bach, and Ilya Safro. "QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.10264.

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Gharibyan, Hrant, Vincent Paul Su, and Hayk Tepanyan. "Hierarchical Learning for Training Large-Scale Variational Quantum Circuits." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00279.

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Reports on the topic "Variational quantum circuits"

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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum kernel methods, while analyzing their impact on neural networks, generative models, and reinforcement learning. Hybrid quantum-classical AI architectures, which combine quantum subroutines with classical deep learning models, are examined for their ability to provide computational advantages in optimization and large-scale data processing. Despite the promise of quantum AI, challenges such as qubit noise, error correction, and hardware scalability remain barriers to full-scale implementation. This study provides an in-depth evaluation of quantum-enhanced AI, highlighting existing applications, ongoing research, and future directions in quantum deep learning, autonomous systems, and scientific computing. The findings contribute to the development of scalable quantum machine learning frameworks, offering novel solutions for next-generation AI systems across finance, healthcare, cybersecurity, and robotics. Keywords Quantum machine learning, quantum computing, artificial intelligence, quantum neural networks, quantum kernel methods, hybrid quantum-classical AI, variational quantum algorithms, quantum generative models, reinforcement learning, quantum optimization, quantum advantage, deep learning, quantum circuits, quantum-enhanced AI, quantum deep learning, error correction, quantum-inspired algorithms, quantum annealing, probabilistic computing.
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