Built on

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,”
1997
Earlier work this paper cites.
B. Yu, “Assouad, fano, and le cam,”
1997
Earlier work this paper cites.
B. Schölkopf, A. Smola, and K.-R. Müller, “Nonlinear component analysis as a kernel eigenvalue problem,”
1998
Earlier work this paper cites.
E. Dennis, A. Kitaev, A. Landahl, and J. Preskill, “Topological quantum memory,”
2002
Earlier work this paper cites.
M. Broughton, G. Verdon, T. McCourt, A. J. Martinez, J. H. Yoo, S. V. Isakov, P. Massey, R. Halavati, M. Y. Niu, A. Zlokapa, E. Peters, O. Lockwood, A. Skolik, S. Jerbi, V. Dunjko, M. Leib, M. Streif, D. V. Dollen, H. Chen, S. Cao, R. Wiersema, H.-Y. Huang, J. R. McClean, R. Babbush, S. Boixo, D. Bacon, A. K. Ho, H. Neven, and M. Mohseni, “Tensorflow quantum: A software framework for quantum machine learning,”
Original
2003
Earlier work this paper cites.
M. Mohseni and D. A. Lidar, “Direct characterization of quantum dynamics: General theory,”
2007
Earlier work this paper cites.
M. Mohseni, A. T. Rezakhani, and D. A. Lidar, “Quantum-process tomography: Resource analysis of different strategies,”
2008
Earlier work this paper cites.
A. I. Lvovsky, B. C. Sanders, and W. Tittel, “Optical quantum memory,”
2009
Earlier work this paper cites.
V. Giovannetti, S. Lloyd, and L. Maccone, “Advances in quantum metrology,”
2011
Earlier work this paper cites.
D. Gottesman, T. Jennewein, and S. Croke, “Longer-baseline telescopes using quantum repeaters,”
2012
Earlier work this paper cites.
K. P. Murphy, Machine learning: a probabilistic perspective (MIT press, 2012)
2012
Earlier work this paper cites.
L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. VanderPlas, A. Joly, B. Holt, and G. Varoquaux, “API design for machine learning software: experiences from the scikit-learn project,”
Original
2013
Earlier work this paper cites.
S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum principal component analysis,”
2014
Earlier work this paper cites.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,”
Original
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
Original
2014
Earlier work this paper cites.
D. Tang, B. Qin, and T. Liu, “Document modeling with gated recurrent neural network for sentiment classification,”
2015
Earlier work this paper cites.
G. Koch, R. Zemel, R. Salakhutdinov, et al. , “Siamese neural networks for one-shot image recognition,”
2015
Earlier work this paper cites.

Similar

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (The MIT Press, 2016)
2016
Cited alongside, same era.
C. L. Degen, F. Reinhard, and P. Cappellaro, “Quantum sensing,”
2017
Cited alongside, same era.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,”
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,”
2017
Cited alongside, same era.
J. Haah, A. W. Harrow, Z. Ji, X. Wu, and N. Yu, “Sample-optimal tomography of quantum states,”
2017
Cited alongside, same era.
J. Preskill, “Quantum computing in the NISQ era and beyond,”
2018

Then

W. J. Huggins, S. McArdle, T. E. O’Brien, J. Lee, N. C. Rubin, S. Boixo, K. B. Whaley, R. Babbush, and J. R. McClean, “Virtual distillation for quantum error mitigation,”
Original
2020
Later among the works it cites.
N.-H. Chia, A. Gilyén, T. Li, H.-H. Lin, E. Tang, and C. Wang, “Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning,”
2020
Later among the works it cites.
H.-Y. Huang, R. Kueng, and J. Preskill, “Predicting many properties of a quantum system from very few measurements,”
2020
Later among the works it cites.
S. Bubeck, S. Chen, and J. Li, “Entanglement is necessary for optimal quantum property testing,”
2020
Later among the works it cites.
M. Coudron and S. Menda, “Computations with greater quantum depth are strictly more powerful (relative to an oracle),”
2020

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2021

Quantum advantage in learning from experiments

Huang, Hsin-Yuan, Broughton, Michael, Cotler, Jordan et al.

Understand

Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world.

An experimental setup that transduces data from a physical system to a stable quantum memory, and processes that data using a quantum computer, could have significant advantages over conventional experiments in which the physical system is measured and the outcomes are processed using a classical computer.
We prove that, in various tasks, quantum machines can learn from exponentially fewer experiments than those required in conventional experiments.
The exponential advantage holds in predicting properties of physical systems, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics.

Built on

S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,”
1997
Earlier work this paper cites.
B. Yu, “Assouad, fano, and le cam,”
1997
Earlier work this paper cites.
B. Schölkopf, A. Smola, and K.-R. Müller, “Nonlinear component analysis as a kernel eigenvalue problem,”
1998
Earlier work this paper cites.
E. Dennis, A. Kitaev, A. Landahl, and J. Preskill, “Topological quantum memory,”
2002
Earlier work this paper cites.
M. Broughton, G. Verdon, T. McCourt, A. J. Martinez, J. H. Yoo, S. V. Isakov, P. Massey, R. Halavati, M. Y. Niu, A. Zlokapa, E. Peters, O. Lockwood, A. Skolik, S. Jerbi, V. Dunjko, M. Leib, M. Streif, D. V. Dollen, H. Chen, S. Cao, R. Wiersema, H.-Y. Huang, J. R. McClean, R. Babbush, S. Boixo, D. Bacon, A. K. Ho, H. Neven, and M. Mohseni, “Tensorflow quantum: A software framework for quantum machine learning,”
Original
2003
Earlier work this paper cites.
M. Mohseni and D. A. Lidar, “Direct characterization of quantum dynamics: General theory,”
2007
Earlier work this paper cites.
M. Mohseni, A. T. Rezakhani, and D. A. Lidar, “Quantum-process tomography: Resource analysis of different strategies,”
2008
Earlier work this paper cites.
A. I. Lvovsky, B. C. Sanders, and W. Tittel, “Optical quantum memory,”
2009
Earlier work this paper cites.
V. Giovannetti, S. Lloyd, and L. Maccone, “Advances in quantum metrology,”
2011
Earlier work this paper cites.
D. Gottesman, T. Jennewein, and S. Croke, “Longer-baseline telescopes using quantum repeaters,”
2012
Earlier work this paper cites.
K. P. Murphy, Machine learning: a probabilistic perspective (MIT press, 2012)
2012
Earlier work this paper cites.
L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. VanderPlas, A. Joly, B. Holt, and G. Varoquaux, “API design for machine learning software: experiences from the scikit-learn project,”
Original
2013
Earlier work this paper cites.
S. Lloyd, M. Mohseni, and P. Rebentrost, “Quantum principal component analysis,”
2014
Earlier work this paper cites.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,”
Original
2014
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
Original
2014
Earlier work this paper cites.
D. Tang, B. Qin, and T. Liu, “Document modeling with gated recurrent neural network for sentiment classification,”
2015
Earlier work this paper cites.
G. Koch, R. Zemel, R. Salakhutdinov, et al. , “Siamese neural networks for one-shot image recognition,”
2015
Earlier work this paper cites.

Similar

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (The MIT Press, 2016)
2016
Cited alongside, same era.
C. L. Degen, F. Reinhard, and P. Cappellaro, “Quantum sensing,”
2017
Cited alongside, same era.
J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,”
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,”
2017
Cited alongside, same era.
J. Haah, A. W. Harrow, Z. Ji, X. Wu, and N. Yu, “Sample-optimal tomography of quantum states,”
2017
Cited alongside, same era.
J. Preskill, “Quantum computing in the NISQ era and beyond,”
2018

Then

W. J. Huggins, S. McArdle, T. E. O’Brien, J. Lee, N. C. Rubin, S. Boixo, K. B. Whaley, R. Babbush, and J. R. McClean, “Virtual distillation for quantum error mitigation,”
Original
2020
Later among the works it cites.
N.-H. Chia, A. Gilyén, T. Li, H.-H. Lin, E. Tang, and C. Wang, “Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning,”
2020
Later among the works it cites.
H.-Y. Huang, R. Kueng, and J. Preskill, “Predicting many properties of a quantum system from very few measurements,”
2020
Later among the works it cites.
S. Bubeck, S. Chen, and J. Li, “Entanglement is necessary for optimal quantum property testing,”
2020
Later among the works it cites.
M. Coudron and S. Menda, “Computations with greater quantum depth are strictly more powerful (relative to an oracle),”
2020

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of machine learning (The MIT Press, 2018)

2018

Cited alongside, same era.

Z. Ji, Y.-K. Liu, and F. Song, “Pseudorandom quantum states,”

2018

Cited alongside, same era.

R. Vershynin, High-Dimensional Probability: An Introduction with Applications in Data Science (Cambridge University Press, 2018)

2018

Cited alongside, same era.

J. Watrous, The Theory of Quantum Information (Cambridge University Press, 2018)

2018

Cited alongside, same era.

F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. Brandão, D. A. Buell, et al. , “Quantum supremacy using a programmable superconducting processor,”

2019

Cited alongside, same era.

J. Cotler, S. Choi, A. Lukin, H. Gharibyan, T. Grover, M. E. Tai, M. Rispoli, R. Schittko, P. M. Preiss, A. M. Kaufman, et al. , “Quantum virtual cooling,”

2019

Cited alongside, same era.

Later among the works it cites.

N.-H. Chia, K.-M. Chung, and C.-Y. Lai, “On the need for large quantum depth,”

2020

Later among the works it cites.

C. Bădescu and R. O’Donnell, “Improved quantum data analysis,”

Original

2020

Later among the works it cites.

Z. Brakerski and O. Shmueli, “Scalable pseudorandom quantum states,”

2020

Later among the works it cites.

J. Bland-Hawthorn, M. J. Sellars, and J. G. Bartholomew, “Quantum memories and the double-slit experiment: implications for astronomical interferometry,”

2021

Closest in time.

D. Aharonov, J. Cotler, and X.-L. Qi, “Quantum algorithmic measurement,”

Original

2021

Closest in time.

B. Koczor, “The dominant eigenvector of a noisy quantum state,”

Original

2021

Closest in time.

E. Tang, “Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions,”

2021