Fetching the paper…
Reading the bibliography…
While recent breakthroughs have proven the ability of noisy intermediate-scale quantum (NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the use of these devices for solving more practically relevant computational problems remains a challenge.
Ian Affleck, Tom Kennedy, Elliott Lieb, and Hal Tasaki, “Rigorous results on valence-bond ground states in antiferromagnets,” Physical Review Letters 59
1987
Earlier work this paper cites.
Mark Fannes, Bruno Nachtergaele, and Reinhard F Werner, “Finitely correlated states on quantum spin chains,” Communications in mathematical physics 144
1992
Earlier work this paper cites.
Steven R White, “Density matrix formulation for quantum renormalization groups,” Physical review letters 69
1992
Earlier work this paper cites.
Nikolaus Hansen and Andreas Ostermeier, “Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation,” in Proceedings of IEEE international conference on evolutionary computation (IEEE, 1996) pp. 312–317
1996
Earlier work this paper cites.
Rolando Somma, Gerardo Ortiz, James E Gubernatis, Emanuel Knill, and Raymond Laflamme, “Simulating physical phenomena by quantum networks,” Physical Review A 65
2002
Earlier work this paper cites.
David J. C. MacKay, Information Theory, Inference & Learning Algorithms (Cambridge University Press, New York, NY, USA, 2002)
2002
Earlier work this paper cites.
Guifré Vidal, “Efficient classical simulation of slightly entangled quantum computations,” Physical review letters 91
2003
Earlier work this paper cites.
Christian Schön, Enrique Solano, Frank Verstraete, J Ignacio Cirac, and Michael M Wolf, “Sequential generation of entangled multiqubit states,” Physical review letters 95
2005
Earlier work this paper cites.
Robert R Tucci, “An introduction to Cartan’s KAK decomposition for QC programmers,” arXiv preprint arXiv:0507171 (2005)
2005
Earlier work this paper cites.
Y-Y Shi, L-M Duan, and G Vidal, “Classical simulation of quantum many-body systems with a tree tensor network,” Physical review A 74
2006
Earlier work this paper cites.
D Perez-Garcia, F Verstraete, MM Wolf, and JI Cirac, “Matrix product state representations,” Quantum Information & Computation 7
2007
Earlier work this paper cites.
Igor L Markov and Yaoyun Shi, “Simulating quantum computation by contracting tensor networks,” SIAM Journal on Computing 38
2008
Earlier work this paper cites.
Marcus Cramer, Martin B Plenio, Steven T Flammia, Rolando Somma, David Gross, Stephen D Bartlett, Olivier Landon-Cardinal, David Poulin, and Yi-Kai Liu, “Efficient quantum state tomography,” Nature communications 1
2010
Earlier work this paper cites.
Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J Love, Alán Aspuru-Guzik, and Jeremy L O’brien, “A variational eigenvalue solver on a photonic quantum processor,” Nature communications 5
2014
Earlier work this paper cites.
Román Orús, “A practical introduction to tensor networks: Matrix product states and projected entangled pair states,” Annals of physics 349
2014
Earlier work this paper cites.
Scott Aaronson, “Read the fine print,” Nature Physics 11
2015
Earlier work this paper cites.
Dave Wecker, Matthew B Hastings, and Matthias Troyer, “Progress towards practical quantum variational algorithms,” Physical Review A 92
2015
Earlier work this paper cites.
Edwin Stoudenmire and David J Schwab, “Supervised learning with tensor networks,” Advances in Neural Information Processing Systems 29
2016
Earlier work this paper cites.
Alexander Novikov, Mikhail Trofimov, and Ivan Oseledets, “Exponential machines,” (2016)
2016
Earlier work this paper cites.
Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd, “Quantum machine learning,” Nature 549
2017
Earlier work this paper cites.
Jun Li, Xiaodong Yang, Xinhua Peng, and Chang-Pu Sun, “Hybrid quantum-classical approach to quantum optimal control,” Physical review letters 118
2017
Earlier work this paper cites.
John Preskill, “Quantum computing in the NISQ era and beyond,” Quantum 2
2018
Earlier work this paper cites.
Alejandro Perdomo-Ortiz, Marcello Benedetti, John Realpe-Gómez, and Rupak Biswas, “Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers,” Quantum Science and Technology 3
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Jarrod Mcclean, Sergio Boixo, Vadim Smelyanskiy, Ryan Babbush, and Hartmut Neven, “Barren plateaus in quantum neural network training landscapes,” Nature Communications 9
2018
Cited alongside, same era.
Jonathan Romero, Ryan Babbush, Jarrod R McClean, Cornelius Hempel, Peter J Love, and Alán Aspuru-Guzik, “Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz,” Quantum Science and Technology 4
2018
Cited alongside, same era.
Matthew Fishman, Steven R. White, and E. Miles Stoudenmire, “The itensor software library for tensor network calculations,” (2020)
2020
Later among the works it cites.
Samson Wang, Enrico Fontana, Marco Cerezo, Kunal Sharma, Akira Sone, Lukasz Cincio, and Patrick J Coles, “Noise-induced barren plateaus in variational quantum algorithms,” Nature communications 12
2021
Later among the works it cites.
Eric Ricardo Anschuetz, “Critical points in quantum generative models,” in International Conference on Learning Representations (2021)
2021
Later among the works it cites.
Peng-Fei Zhou, Rui Hong, and Shi-Ju Ran, “Automatically differentiable quantum circuit for many-qubit state preparation,” Physical Review A 104
2021
Later among the works it cites.
Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter, and Jens Eisert, “On the quantum versus classical learnability of discrete distributions,” Quantum 5
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii, “Quantum circuit learning,” Phys. Rev. A 98
2018
Cited alongside, same era.
Yuxuan Du, Min-Hsiu Hsieh, Tongliang Liu, and Dacheng Tao, “Expressive power of parametrized quantum circuits,” Physical Review Research 2
2018
Cited alongside, same era.
Zhao-Yu Han, Jun Wang, Heng Fan, Lei Wang, and Pan Zhang, “Unsupervised generative modeling using matrix product states,” PRX 8
2018
Cited alongside, same era.
National Academies of Sciences, Engineering, and Medicine and others, Quantum computing: progress and prospects (National Academies Press, 2019)
2019
Cited alongside, same era.
Yudong Cao, Jonathan Romero, Jonathan P. Olson, Matthias Degroote, Peter D. Johnson, Mária Kieferová, Ian D. Kivlichan, Tim Menke, Borja Peropadre, Nicolas P. D. Sawaya, Sukin Sim, Libor Veis, and Alán Aspuru-Guzik, “Quantum chemistry in the age of quantum computing,” Chemical Reviews 119
2019
Cited alongside, same era.
Maria Schuld and Nathan Killoran, “Quantum machine learning in feature hilbert spaces,” Physical Review Letters 122
2019
Cited alongside, same era.
Vojtěch Havlíček, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, and Jay M. Gambetta, “Supervised learning with quantum-enhanced feature spaces,” Nature 567
2019
Cited alongside, same era.
William Huggins, Piyush Patil, Bradley Mitchell, K Birgitta Whaley, and E Miles Stoudenmire, “Towards quantum machine learning with tensor networks,” Quantum Science and Technology 4
2019
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
Marcello Benedetti, Brian Coyle, Mattia Fiorentini, Michael Lubasch, and Matthias Rosenkranz, “Variational inference with a quantum computer,” Phys. Rev. Applied 16
2021
Later among the works it cites.
Ian MacCormack, Alexey Galda, and Adam L. Lyon, “Simulating large peps tensor networks on small quantum devices,” (2021)
2021
Later among the works it cites.
Yasunari Suzuki, Yoshiaki Kawase, Yuya Masumura, Yuria Hiraga, Masahiro Nakadai, Jiabao Chen, Ken M. Nakanishi, Kosuke Mitarai, Ryosuke Imai, Shiro Tamiya, Takahiro Yamamoto, Tennin Yan, Toru Kawakubo, Yuya O. Nakagawa, Yohei Ibe, Youyuan Zhang, Hirotsugu Yamashita, Hikaru Yoshimura, Akihiro Hayashi, and Keisuke Fujii, “Qulacs: a fast and versatile quantum circuit simulator for research purpose,” Quantum 5
2021
Later among the works it cites.
2021
Later among the works it cites.
Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, and Alán Aspuru-Guzik, “Noisy intermediate-scale quantum algorithms,” Rev. Mod. Phys. 94
2022
Closest in time.
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, “Quantum advantage in learning from experiments,” Science 376
2022
Closest in time.
Zoë Holmes, Kunal Sharma, Marco Cerezo, and Patrick J Coles, “Connecting ansatz expressibility to gradient magnitudes and barren plateaus,” PRX Quantum 3
2022
Closest in time.
2022
Closest in time.
Jakob S Kottmann and Alán Aspuru-Guzik, “Optimized low-depth quantum circuits for molecular electronic structure using a separable-pair approximation,” Physical Review A 105
2022
Closest in time.
2022
Closest in time.
Adrian Cho, “Ordinary computer matches Google’s quantum computer,” Science 377
2022
Closest in time.
James Dborin, Fergus Barratt, Vinul Wimalaweera, Lewis Wright, and Andrew Green, “Matrix product state pre-training for quantum machine learning,” Quantum Science and Technology (2022)
2022
Closest in time.
Prithvi Gundlapalli and Junyi Lee, “Deterministic and entanglement-efficient preparation of amplitude-encoded quantum registers,” Physical Review Applied 18
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.