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Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas.
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Ronneberger, O., Fischer, P. & Brox, T · 2015
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Automated search for new quantum experiments
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Improved classical simulation of quantum circuits dominated by clifford gates
Bravyi, S. & Gosset, D · 2016
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Vaswani, A · 2017
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Arulkumaran, K., Deisenroth, M. P., Brundage, M. & Bharath, A. A · 2017
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graph2vec: Learning distributed representations of graphs (2017)
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Machine learning meets quantum state preparation. the phase diagram of quantum control
Bukov, M. et al · 2017
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Neural decoder for topological codes
Torlai, G. & Melko, R. G · 2017
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Efficient Variational Quantum Simulator Incorporating Active Error Minimization
Li, Y. & Benjamin, S. C · 2017
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Error Mitigation for Short-Depth Quantum Circuits
Temme, K., Bravyi, S. & Gambetta, J. M · 2017
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Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states
McClean, J. R., Kimchi-Schwartz, M. E., Carter, J. & de Jong, W. A · 2017
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Machine learning phases of matter
Carrasquilla, J. & Melko, R. G · 2017
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Solving the quantum many-body problem with artificial neural networks
Carleo, G. & Troyer, M · 2017
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
Silver, D. et al · 2017
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Quantum computing in the nisq era and beyond
Preskill, J · 2018
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Modelling non-markovian quantum processes with recurrent neural networks
Banchi, L., Grant, E., Rocchetto, A. & Severini, S · 2018
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Barren plateaus in quantum neural network training landscapes
McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R. & Neven, H · 2018
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Reinforcement learning in different phases of quantum control
Bukov, M. et al · 2018
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Machine learning assisted readout of trapped-ion qubits
Seif, A. et al · 2018
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Deep neural decoders for near term fault-tolerant experiments
Chamberland, C. & Ronagh, P · 2018
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Machine-learning-assisted correction of correlated qubit errors in a topological code
Baireuther, P., O’Brien, T. E., Tarasinski, B. & Beenakker, C. W · 2018
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Scalable neural network decoders for higher dimensional quantum codes
Breuckmann, N. P. & Ni, X · 2018
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Neural-network quantum state tomography
Torlai, G. et al · 2018
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Low-cost error mitigation by symmetry verification
Bonet-Monroig, X., Sagastizabal, R., Singh, M. & O’Brien, T. E · 2018
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Quantum supremacy using a programmable superconducting processor
Arute, F. et al · 2019
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Efficiently measuring a quantum device using machine learning
Lennon, D. T. et al · 2019
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Coherent transport of quantum states by deep reinforcement learning
Porotti, R., Tamascelli, D., Restelli, M. & Prati, E · 2019
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A convergence theory for deep learning via over-parameterization , 242–252 (PMLR, 2019)
Allen-Zhu, Z., Li, Y. & Song, Z · 2019
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Gedeon, T., Wong, K. W. & Lee, M. (eds) Gl2vec: Graph embedding enriched by line graphs with edge features
Chen, H. & Koga, H · 2019
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Learning to learn with quantum neural networks via classical neural networks
Verdon, G. et al · 2019
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Machine learning method for state preparation and gate synthesis on photonic quantum computers
Arrazola, J. M. et al · 2019
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When does reinforcement learning stand out in quantum control? a comparative study on state preparation
Zhang, X.-M., Wei, Z., Asad, R., Yang, X.-C. & Wang, X · 2019
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An adaptive variational algorithm for exact molecular simulations on a quantum computer
Grimsley, H. R., Economou, S. E., Barnes, E. & Mayhall, N. J · 2019
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Machine learning techniques for state recognition and auto-tuning in quantum dots
Kalantre, S. S. et al · 2019
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Calibration of quantum sensors by neural networks
Cimini, V. et al · 2019
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A machine learning approach for automated fine-tuning of semiconductor spin qubits
Teske, J. D. et al · 2019
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Quantum error correction for the toric code using deep reinforcement learning
Andreasson, P., Johansson, J., Liljestrand, S. & Granath, M · 2019
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Error-Mitigated Digital Quantum Simulation
McArdle, S., Yuan, X. & Benjamin, S · 2019
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Natural language processing
Chowdhary, K. & Chowdhary, K · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A. & Abbeel, P · 2020
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Modeling and control of a reconfigurable photonic circuit using deep learning
Youssry, A., Chapman, R. J., Peruzzo, A., Ferrie, C. & Tomamichel, M · 2020
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Unboxing quantum black box models: Learning non-markovian dynamics
Krastanov, S. et al · 2020
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A comprehensive survey on transfer learning
Zhuang, F. et al · 2020
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Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning , 30–36 (IEEE, 2020)
Daraeizadeh, S., Premaratne, S. P. & Matsuura, A. Y · 2020
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Machine-learning-based three-qubit gate design for the toffoli gate and parity check in transmon systems
Daraeizadeh, S. et al · 2020
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Classifying global state preparation via deep reinforcement learning
Haug, T. et al · 2020
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Mog-vqe: Multiobjective genetic variational quantum eigensolver (2020)
Chivilikhin, D. et al · 2020
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Quantum device fine-tuning using unsupervised embedding learning
van Esbroeck, N. M. et al · 2020
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Machine learning enables completely automatic tuning of a quantum device faster than human experts
Moon, H. et al · 2020
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Boosting on the shoulders of giants in quantum device calibration
Wozniakowski, A., Thompson, J., Gu, M. & Binder, F · 2020
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Extending qubit coherence by adaptive quantum environment learning
Scerri, E., Gauger, E. M. & Bonato, C · 2020
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Using a recurrent neural network to reconstruct quantum dynamics of a superconducting qubit from physical observations
Flurin, E., Martin, L. S., Hacohen-Gourgy, S. & Siddiqi, I · 2020
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Symmetries for a high-level neural decoder on the toric code
Wagner, T., Kampermann, H. & Bruß, D · 2020
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Reinforcement learning decoders for fault-tolerant quantum computation
Sweke, R., Kesselring, M. S., van Nieuwenburg, E. P. & Eisert, J · 2020
Cited alongside, same era.
Quantum error mitigation with artificial neural network
Kim, C., Park, K. D. & Rhee, J.-K · 2020
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Quantum computer systems for scientific discovery
Alexeev, Y. et al · 2021
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Realization of real-time fault-tolerant quantum error correction
Ryan-Anderson, C. et al · 2021
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Machine learning and deep learning
Janiesch, C., Zschech, P. & Heinrich, K · 2021
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Learning models of quantum systems from experiments
Gentile, A. A. et al · 2021
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Say no to optimization: A nonorthogonal quantum eigensolver
Baek, U. et al · 2023
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Mullinax, J. W. & Tubman, N. M · 2023
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Pre-optimizing variational quantum eigensolvers with tensor networks (2023)
Khan, A., Clark, B. K. & Tubman, N. M · 2023
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A quantum states preparation method based on difference-driven reinforcement learning , Vol. 13, 2350013 (World Scientific, 2023)
Liu, W., Xu, J. & Wang, B · 2023
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Ga4qco: Genetic algorithm for quantum circuit optimization (2023)
Sünkel, L., Martyniuk, D., Mattern, D., Jung, J. & Paschke, A · 2023
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Learning quantum hamiltonians from single-qubit measurements
Che, L. et al · 2021
Cited alongside, same era.
Deep learning enhanced individual nuclear-spin detection
Jung, K. et al · 2021
Cited alongside, same era.
Engineering high-coherence superconducting qubits
Siddiqi, I · 2021
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Automated design of superconducting circuits and its application to 4-local couplers
Menke, T. et al · 2021
Cited alongside, same era.
Conceptual understanding through efficient automated design of quantum optical experiments
Krenn, M., Kottmann, J. S., Tischler, N. & Aspuru-Guzik, A · 2021
Cited alongside, same era.
Breaking adiabatic quantum control with deep learning
Ding, Y. et al · 2021
Cited alongside, same era.
Bayesian learning of parameterised quantum circuits
Duffield, S., Benedetti, M. & Rosenkranz, M · 2023
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Identifying pauli spin blockade using deep learning
Schuff, J. et al · 2023
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Automated long-range compensation of an rf quantum dot sensor
Hickie, J. et al · 2023
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AI for quantum computing in silicon
Severin, B · 2023
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Self-correcting quantum many-body control using reinforcement learning with tensor networks
Metz, F. & Bukov, M · 2023
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Realizing a deep reinforcement learning agent for real-time quantum feedback
Reuer, K. et al · 2023
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Real-time quantum error correction beyond break-even
Sivak, V. V. et al · 2023
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Transformer-qec: Quantum error correction code decoding with transferable transformers
Wang, H. et al · 2023
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Learning to decode the surface code with a recurrent, transformer-based neural network
Bausch, J. et al · 2023
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Techniques for combining fast local decoders with global decoders under circuit-level noise
Chamberland, C., Goncalves, L., Sivarajah, P., Peterson, E. & Grimberg, S · 2023
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Data-driven decoding of quantum error correcting codes using graph neural networks
Lange, M. et al · 2023
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Wang, H. et al · 2023
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Jan Olle, M. P. F. M., Remmy Zen · 2023
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Discovery of optimal quantum error correcting codes via reinforcement learning
Su, V. P. et al · 2023
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Detection of entangled states supported by reinforcement learning
Cao, J.-H. et al · 2023
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Quantum error mitigation
Cai, Z. et al · 2023
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Machine learning for practical quantum error mitigation (arXiv:2309.17368) (2023)
Liao, H. et al · 2023
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Mnisq: A large-scale quantum circuit dataset for machine learning on/for quantum computers in the nisq era (2023)
Placidi, L. et al · 2023
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A polynomial-time classical algorithm for noisy random circuit sampling , STOC ’23 (ACM, 2023)
Aharonov, D., Gao, X., Landau, Z., Liu, Y. & Vazirani, U · 2023
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cuquantum sdk: A high-performance library for accelerating quantum science , Vol. 01, 1050–1061 (2023)
Bayraktar, H. et al · 2023
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Artificial intelligence (ai) for quantum and quantum for ai
Zhu, Y. & Yu, K · 2023
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Logical quantum processor based on reconfigurable atom arrays
Bluvstein, D. et al · 2024
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Quantum computing for scientific computing: A survey
Li, Y. et al · 2024
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Unravelling quantum dynamics using flow equations
Thomson, S. J. & Eisert, J · 2024
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Gpt (generative pre-trained transformer)–a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions
Yenduri, G. et al · 2024
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Systematic literature review: Quantum machine learning and its applications
Peral-García, D., Cruz-Benito, J. & García-Peñalvo, F. J · 2024
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Precision quantum parameter inference with continuous observation
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Experimental graybox quantum system identification and control
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Bridging the reality gap in quantum devices with physics-aware machine learning
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Cross-architecture tuning of silicon and sige-based quantum devices using machine learning
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Model-free distortion canceling and control of quantum devices
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Cross-platform autonomous control of minimal kitaev chains
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Parameter-setting heuristic for the quantum alternating operator ansatz
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