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Quantifying the complexity of quantum states is a longstanding key problem in various subfields of science, ranging from quantum computing to the black-hole theory.
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Pennylane: Automatic differentiation of hybrid quantum-classical computations
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Characterizing quantum supremacy in near-term devices
Sergio Boixo, Sergei V Isakov, Vadim N Smelyanskiy, Ryan Babbush, Nan Ding, Zhang Jiang, Michael J Bremner, John M Martinis, and Hartmut Neven · 2018
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Barren plateaus in quantum neural network training landscapes
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On information gain and regret bounds in gaussian process bandits
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Noise-induced barren plateaus in variational quantum algorithms
Samson Wang, Enrico Fontana, Marco Cerezo, Kunal Sharma, Akira Sone, Lukasz Cincio, and Patrick J Coles · 2021
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Strong quantum computational advantage using a superconducting quantum processor
Yulin Wu, Wan-Su Bao, Sirui Cao, Fusheng Chen, Ming-Cheng Chen, Xiawei Chen, Tung-Hsun Chung, Hui Deng, Yajie Du, Daojin Fan, et al · 2021
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A polynomial-time classical algorithm for noisy random circuit sampling
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Scalable and flexible classical shadow tomography with tensor networks
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Quantum supremacy using a programmable superconducting processor
Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando GSL Brandao, David A Buell, et al · 2019
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Quantum convolutional neural networks
Iris Cong, Soonwon Choi, and Mikhail D Lukin · 2019
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Identifying topological order through unsupervised machine learning
Joaquin F Rodriguez-Nieva and Mathias S Scheurer · 2019
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Unsupervised identification of topological phase transitions using predictive models
Eliska Greplova, Agnes Valenti, Gregor Boschung, Frank Schäfer, Niels Lörch, and Sebastian D Huber · 2020
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Quantum variational algorithms are swamped with traps
Eric R Anschuetz and Bobak T Kiani · 2022
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Shallow shadows: Expectation estimation using low-depth random clifford circuits
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Challenges and opportunities in quantum machine learning
M Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, and Patrick J Coles · 2022
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Tight bounds on the convergence of noisy random circuits to the uniform distribution
Abhinav Deshpande, Pradeep Niroula, Oles Shtanko, Alexey V Gorshkov, Bill Fefferman, and Michael J Gullans · 2022
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Efficient measure for the expressivity of variational quantum algorithms
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Linear growth of quantum circuit complexity
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Provably efficient machine learning for quantum many-body problems
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Linear growth of circuit complexity from brownian dynamics
Shao-Kai Jian, Gregory Bentsen, and Brian Swingle · 2022
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Efficient classical simulation of random shallow 2d quantum circuits
John C Napp, Rolando L La Placa, Alexander M Dalzell, Fernando GSL Brandao, and Aram W Harrow · 2022
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Optimizing shadow tomography with generalized measurements
H Chau Nguyen, Jan Lennart Bönsel, Jonathan Steinberg, and Otfried Gühne · 2022
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The learnability of pauli noise
Senrui Chen, Yunchao Liu, Matthew Otten, Alireza Seif, Bill Fefferman, and Liang Jiang · 2023
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Evidence for the utility of quantum computing before fault tolerance
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Random unitaries in extremely low depth
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