Built on

H. Shen, Mutual Information Scaling and Expressive Power of Sequence Models, arXiv:1905.04271 [cond-mat, stat] (2019), arXiv:1905.04271 [cond-mat, stat]
Original
1905
Earlier work this paper cites.
J. Carrasquilla, D. Luo, F. Pérez, A. Milsted, B. K. Clark, M. Volkovs, and L. Aolita, Probabilistic simulation of quantum circuits with the transformer (2019b), arXiv:1912.11052 [cond-mat.str-el]
Original
1912
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural computation 9
1997
Earlier work this paper cites.
D. F. V. James, P. G. Kwiat, W. J. Munro, and A. G. White, Measurement of qubits, Physical Review A 64
2001
Earlier work this paper cites.
I. J. S. D. Vlugt, D. Iouchtchenko, E. Merali, P.-N. Roy, and R. G. Melko, Reconstructing quantum molecular rotor ground states (2020), arXiv:2003.14273
Original
2003
Earlier work this paper cites.
M. Paris and J. Rehacek, Quantum state estimation , Vol. 649 (Springer Science & Business Media, 2004)
2004
Earlier work this paper cites.
R. Blume-Kohout, J. K. Gamble, E. Nielsen, J. Mizrahi, J. D. Sterk, and P. Maunz, Robust, self-consistent, closed-form tomography of quantum logic gates on a trapped ion qubit, arXiv preprint arXiv:1310.4492 (2013)
Original
2013
Earlier work this paper cites.
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, Learning phrase representations using rnn encoder-decoder for statistical machine translation, arXiv preprint arXiv:1406.1078 (2014)
Original
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, arXiv preprint arXiv:1412.3555 (2014)
Original
2014
Earlier work this paper cites.

Similar

S. Sheldon, L. S. Bishop, E. Magesan, S. Filipp, J. M. Chow, and J. M. Gambetta, Characterizing errors on qubit operations via iterative randomized benchmarking, Physical Review A 93
2016
Cited alongside, same era.
J. Cheng, L. Dong, and M. Lapata, Long short-term memory-networks for machine reading, arXiv preprint arXiv:1601.06733 (2016)
Original
2016
Cited alongside, same era.
A. P. Parikh, O. Täckström, D. Das, and J. Uszkoreit, A decomposable attention model for natural language inference, arXiv preprint arXiv:1606.01933 (2016)
Original
2016
Cited alongside, same era.
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, et al. , Google’s neural machine translation system: Bridging the gap between human and machine translation, arXiv preprint arXiv:1609.08144 (2016)
Original
2016
Cited alongside, same era.

Then

N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, and J. Dean, Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, arXiv preprint arXiv:1701.06538 (2017)
Original
2017
Later among the works it cites.
R. Blume-Kohout, J. K. Gamble, E. Nielsen, K. Rudinger, J. Mizrahi, K. Fortier, and P. Maunz, Demonstration of qubit operations below a rigorous fault tolerance threshold with gate set tomography, Nature communications 8
2017
Later among the works it cites.
G. Torlai, G. Mazzola, J. Carrasquilla, M. Troyer, R. Melko, and G. Carleo, Neural-network quantum state tomography, Nature Physics 14
2018
Later among the works it cites.
G. Torlai and R. G. Melko, Latent space purification via neural density operators, Phys. Rev. Lett. 120
2018
Later among the works it cites.

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Reading the bibliography…

2020

Attention-based Quantum Tomography

Cha, Peter, Ginsparg, Paul, Wu, Felix et al.

Understand

With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge.

Recent works found promise in recasting the problem of quantum state reconstruction to learning the probability distribution of quantum state measurement vectors using generative neural network models.
Here we propose the "Attention-based Quantum Tomography" (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state.
The AQT is based on the model proposed in "Attention is all you need" by Vishwani et al (2017) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing models.

Built on

H. Shen, Mutual Information Scaling and Expressive Power of Sequence Models, arXiv:1905.04271 [cond-mat, stat] (2019), arXiv:1905.04271 [cond-mat, stat]
Original
1905
Earlier work this paper cites.
J. Carrasquilla, D. Luo, F. Pérez, A. Milsted, B. K. Clark, M. Volkovs, and L. Aolita, Probabilistic simulation of quantum circuits with the transformer (2019b), arXiv:1912.11052 [cond-mat.str-el]
Original
1912
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural computation 9
1997
Earlier work this paper cites.
D. F. V. James, P. G. Kwiat, W. J. Munro, and A. G. White, Measurement of qubits, Physical Review A 64
2001
Earlier work this paper cites.
I. J. S. D. Vlugt, D. Iouchtchenko, E. Merali, P.-N. Roy, and R. G. Melko, Reconstructing quantum molecular rotor ground states (2020), arXiv:2003.14273
Original
2003
Earlier work this paper cites.
M. Paris and J. Rehacek, Quantum state estimation , Vol. 649 (Springer Science & Business Media, 2004)
2004
Earlier work this paper cites.
R. Blume-Kohout, J. K. Gamble, E. Nielsen, J. Mizrahi, J. D. Sterk, and P. Maunz, Robust, self-consistent, closed-form tomography of quantum logic gates on a trapped ion qubit, arXiv preprint arXiv:1310.4492 (2013)
Original
2013
Earlier work this paper cites.
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, Learning phrase representations using rnn encoder-decoder for statistical machine translation, arXiv preprint arXiv:1406.1078 (2014)
Original
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, arXiv preprint arXiv:1412.3555 (2014)
Original
2014
Earlier work this paper cites.

Similar

S. Sheldon, L. S. Bishop, E. Magesan, S. Filipp, J. M. Chow, and J. M. Gambetta, Characterizing errors on qubit operations via iterative randomized benchmarking, Physical Review A 93
2016
Cited alongside, same era.
J. Cheng, L. Dong, and M. Lapata, Long short-term memory-networks for machine reading, arXiv preprint arXiv:1601.06733 (2016)
Original
2016
Cited alongside, same era.
A. P. Parikh, O. Täckström, D. Das, and J. Uszkoreit, A decomposable attention model for natural language inference, arXiv preprint arXiv:1606.01933 (2016)
Original
2016
Cited alongside, same era.
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, et al. , Google’s neural machine translation system: Bridging the gap between human and machine translation, arXiv preprint arXiv:1609.08144 (2016)
Original
2016
Cited alongside, same era.

Then

N. Shazeer, A. Mirhoseini, K. Maziarz, A. Davis, Q. Le, G. Hinton, and J. Dean, Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, arXiv preprint arXiv:1701.06538 (2017)
Original
2017
Later among the works it cites.
R. Blume-Kohout, J. K. Gamble, E. Nielsen, K. Rudinger, J. Mizrahi, K. Fortier, and P. Maunz, Demonstration of qubit operations below a rigorous fault tolerance threshold with gate set tomography, Nature communications 8
2017
Later among the works it cites.
G. Torlai, G. Mazzola, J. Carrasquilla, M. Troyer, R. Melko, and G. Carleo, Neural-network quantum state tomography, Nature Physics 14
2018
Later among the works it cites.
G. Torlai and R. G. Melko, Latent space purification via neural density operators, Phys. Rev. Lett. 120
2018
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

D. Suess, Łukasz Rudnicki, T. O. Maciel, and D. Gross, Error regions in quantum state tomography: computational complexity caused by geometry of quantum states (2016), arXiv:1608.00374

Original

2016

Cited alongside, same era.

G. Carleo and M. Troyer, Solving the quantum many-body problem with artificial neural networks, Science 355

2017

Cited alongside, same era.

H. Bernien, S. Schwartz, A. Keesling, H. Levine, A. Omran, H. Pichler, S. Choi, A. Zibrov, M. Endres, M. Greiner, V. Vuletic, and M. Lukin, Probing many-body dynamics on a 51-atom quantum simulator, Nature 551

2017

Cited alongside, same era.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention is all you need, in NIPS (2017)

2017

Cited alongside, same era.

J. Carrasquilla, G. Torlai, R. G. Melko, and L. Aolita, Reconstructing quantum states with generative models, Nature Machine Intelligence 1

Cited in the paper.

The implementation of AQT as well as all data used for this work are available at the public repository https://github.com/KimGroup/AQT

Cited in the paper.

Recent work [ 5 ] showed it is possible to estimate quantum fidelity without density matrix reconstruction in the special case when the target state is a pure state

Cited in the paper.

We note that the linear growth of sample complexity in system size for the classical fidelity threshold task seen in Fig. 2

Cited in the paper.

F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. Brandao, D. A. Buell, et al. , Quantum supremacy using a programmable superconducting processor, Nature 574

2019

Later among the works it cites.

G. Torlai, B. Timar, E. P. L. van Nieuwenburg, H. Levine, A. Omran, A. Keesling, H. Bernien, M. Greiner, V. Vuletić, M. D. Lukin, R. G. Melko, and M. Endres, Integrating neural networks with a quantum simulator for state reconstruction, Phys. Rev. Lett. 123

2019

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, Nature Physics 16

2020

Closest in time.

E. S. Tiunov, V. V. T. (Vyborova), A. E. Ulanov, A. I. Lvovsky, and A. K. Fedorov, Experimental quantum homodyne tomography via machine learning, Optica 7

2020

Closest in time.