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Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is challenging -- the design space scales exponentially with circuit size, and there are few well-supported guiding principles for QML circuit design.
A single quantum cannot be cloned
W. K. Wootters and W. H. Zurek · 1982
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Quantum Computation and Quantum Information: 10th Anniversary Edition
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Optimal swap gate insertion for nearest neighbor quantum circuits
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Determining the minimal number of swap gates for multi-dimensional nearest neighbor quantum circuits
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Automatic differentiation in machine learning: a survey
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Classification with quantum neural networks on near term processors
Edward Farhi and Hartmut Neven · 2018
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Barren plateaus in quantum neural network training landscapes
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
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Automated optimization of large quantum circuits with continuous parameters
Yunseong Nam, Neil J. Ross, Yuan Su, Andrew M. Childs, and Dmitri Maslov · 2018
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Greedy randomized search for scalable compilation of quantum circuits
Angelo Oddi and Riccardo Rasconi · 2018
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Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
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Quantum Computing in the NISQ era and beyond
John Preskill · 2018
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Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 2019
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Searching for a robust neural architecture in four gpu hours
Xuanyi Dong and Yi Yang · 2019
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Supervised learning with quantum-enhanced feature spaces
Vojtěch Havlíček, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta · 2019
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Tackling the qubit mapping problem for nisq-era quantum devices
Gushu Li, Yufei Ding, and Yuan Xie · 2019
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DARTS: Differentiable architecture search
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Noise-adaptive compiler mappings for noisy intermediate-scale quantum computers
Prakash Murali, Jonathan M Baker, Ali Javadi-Abhari, Frederic T Chong, and Margaret Martonosi · 2019
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Regularized evolution for image classifier architecture search
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Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
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Optimized compilation of aggregated instructions for realistic quantum computers
Yunong Shi, Nelson Leung, Pranav Gokhale, Zane Rossi, David I Schuster, Henry Hoffmann, and Frederic T Chong · 2019
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Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
Sukin Sim, Peter D. Johnson, and Alán Aspuru-Guzik · 2019
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Not all qubits are created equal: a case for variability-aware policies for nisq-era quantum computers
Swamit S Tannu and Moinuddin K Qureshi · 2019
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Time-sliced quantum circuit partitioning for modular architectures
Jonathan M Baker, Casey Duckering, Alexander Hoover, and Frederic T Chong · 2020
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Optimized quantum compilation for near-term algorithms with openpulse
Pranav Gokhale, Ali Javadi-Abhari, Nathan Earnest, Yunong Shi, and Frederic T Chong · 2020
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Predicting many properties of a quantum system from very few measurements
Hsin-Yuan Huang, Richard Kueng, and John Preskill · 2020
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2020
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Quantum embeddings for machine learning
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Software mitigation of crosstalk on noisy intermediate-scale quantum computers
Prakash Murali, David C. Mckay, Margaret Martonosi, and Ali Javadi-Abhari · 2020
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Disq: A novel quantum output state classification method on ibm quantum computers using openpulse
Tirthak Patel and Devesh Tiwari · 2020
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The power of quantum neural networks
Amira Abbas, David Sutter, Christa Zoufal, Aurelien Lucchi, Alessio Figalli, and Stefan Woerner · 2021
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Encoding-dependent generalization bounds for parametrized quantum circuits
Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer, Jens Eisert, and Ryan Sweke · 2021
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Quantum advantage in learning from experiments
Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, and Jarrod R. McClean · 2022
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Robust resource-efficient quantum variational ansatz through an evolutionary algorithm
Yuhan Huang, Qingyu Li, Xiaokai Hou, Rebing Wu, Man-Hong Yung, Abolfazl Bayat, and Xiaoting Wang · 2022
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Exact and practical pattern matching for quantum circuit optimization
Raban Iten, Romain Moyard, Tony Metger, David Sutter, and Stefan Woerner · 2022
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Pan: Pulse ansatz on nisq machines
Zhiding Liang, Jinglei Cheng, Hang Ren, Hanrui Wang, Fei Hua, Yongshan Ding, Fred Chong, Song Han, Yiyu Shi, and Xuehai Qian · 2022
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Not all swaps have the same cost: A case for optimization-aware qubit routing
Ji Liu, Peiyi Li, and Huiyang Zhou · 2022
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Variational quantum algorithms
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Cost function dependent barren plateaus in shallow parametrized quantum circuits
M. Cerezo, Akira Sone, Tyler Volkoff, Lukasz Cincio, and Patrick J. Coles · 2021
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Adapt: Mitigating idling errors in qubits via adaptive dynamical decoupling
Poulami Das, Siddharth Dangwal, Swamit S Tannu, and Moinuddin Qureshi · 2021
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Jigsaw:boosting fidelity of nisq programs via measurement subsetting
Poulami Das, Swamit S Tannu, and Moinuddin Qureshi · 2021
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Experimental quantum generative adversarial networks for image generation
He-Liang Huang, Yuxuan Du, Ming Gong, Youwei Zhao, Yulin Wu, Chaoyue Wang, Shaowei Li, Futian Liang, Jin Lin, Yu Xu, Rui Yang, Tongliang Liu, Min-Hsiu Hsieh, Hui Deng, Hao Rong, Cheng-Zhi Peng, Chao-Yang Lu, Yu-Ao Chen, Dacheng Tao, Xiaobo Zhu, and Jian-Wei Pan · 2021
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Quest: systematically approximating quantum circuits for higher output fidelity
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Robust quantum circuit approximation for resource-efficient circuit synthesis
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Generalization despite overfitting in quantum machine learning models
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Optimal synthesis into fixed xx interactions
Eric C Peterson, Lev S Bishop, and Ali Javadi-Abhari · 2022
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Boosting quantum fidelity with an ordered diverse ensemble of clifford canary circuits
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Cafqa: A classical simulation bootstrap for variational quantum algorithms
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Generation of high-resolution handwritten digits with an ion-trap quantum computer
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Quilt: Effective multi-class classification on quantum computers using an ensemble of diverse quantum classifiers
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HAMMER: boosting fidelity of noisy quantum circuits by exploiting hamming behavior of erroneous outcomes
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Quantumnas: Noise-adaptive search for robust quantum circuits
H. Wang, Y. Ding, J. Gu, Y. Lin, D. Z. Pan, F. T. Chong, and S. Han · 2022
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Quantumnat: Quantum noise-aware training with noise injection, quantization and normalization
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Qoc: Quantum on-chip training with parameter shift and gradient pruning
Hanrui Wang, Zirui Li, Jiaqi Gu, Yongshan Ding, David Z. Pan, and Song Han · 2022
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Topology aware unitary synthesis for scalable quantum circuit optimization
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General parameter-shift rules for quantum gradients
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Quartz: superoptimization of quantum circuits
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Monte carlo tree search based hybrid optimization of variational quantum circuits
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Differentiable quantum architecture search
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Bandwidth enables generalization in quantum kernel models
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The randomized measurement toolbox
Andreas Elben, Steven T. Flammia, Hsin-Yuan Huang, Richard Kueng, John Preskill, Benoît Vermersch, and Peter Zoller · 2023
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Quantum machine learning of large datasets using randomized measurements
Tobias Haug, Chris N Self, and M S Kim · 2023
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Theory of overparametrization in quantum neural networks
Martín Larocca, Nathan Ju, Diego García-Martín, Patrick J. Coles, and Marco Cerezo · 2023
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