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Quantum Information Processing
Erschienen in: Quantum Information Processing 10/2015

01.10.2015

Protocol for secure quantum machine learning at a distant place

verfasst von: Jeongho Bang, Seung-Woo Lee, Hyunseok Jeong

Erschienen in: Quantum Information Processing | Ausgabe 10/2015

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Abstract

The application of machine learning to quantum information processing has recently attracted keen interest, particularly for the optimization of control parameters in quantum tasks without any pre-programmed knowledge. By adapting the machine learning technique, we present a novel protocol in which an arbitrarily initialized device at a learner’s location is taught by a provider located at a distant place. The protocol is designed such that any external learner who attempts to participate in or disrupt the learning process can be prohibited or noticed. We numerically demonstrate that our protocol works faithfully for single-qubit operation devices. A trade-off between the inaccuracy and the learning time is also analyzed.
Vorheriger Artikel Three-particle Bell-like inequalities under Lorentz transformations
Nächster Artikel Testing Bell’s inequality with one-party weak measurements

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Fußnoten
1
Such an assumption could be strong in a device-independent quantum cryptographic scenario [13]. However, this condition is essential in machine learning because one should trust his/her machine to identify, evaluate and control the data in the learning process.
 
2
The group generators \(\hat{G}_j\) can generally be constructed in any d. Hence such parameterization is quite general (see “Appendix 1”). The real components \(a_j\) can be matched to some real control parameters in experiments, e.g., beam-splitter and phase-shifter alignments in a linear optical system [16] or radio frequency (rf) pulse sequences in a nuclear magnetic resonance (NMR) system [17].
 
3
This starting assumption is realistic and also may be important, since a single state \(\left| \chi _A\right\rangle \) could be used as a cryptographic name (i.e., identity) of Alice in a modified protocol, as described in Sect. 5. Thus, it may be more efficient that \(\left| \chi _A\right\rangle \) is prepared as an arbitrarily superposed state, e.g., \(a \left| 0\right\rangle + b \left| 1\right\rangle \).
 
4
Alice and Bob may use a scheme for user authentication to identify their signs [20‐22].
 
5
We assumed that there are no noise effects in the channels \({\mathcal {C}}^{AB}_r\) and \({\mathcal {C}}^{BA}_r\).
 
6
Here, \(\left| \chi _E\right\rangle \) and \(\left| \tau _E\right\rangle \) are Eve’s own fiducial and target state, respectively.
 
7
One of the powerful advantages of our protocol is that Bob does not need to set the device(s) corresponding to the target task, e.g., \(\hat{T}\), in his side, but this advantage may be weaken in the case where the security issue becomes more important.
 
8
Noting that \(\hat{W}\) has no influence on Alice’s learning in the case where \(\left| c\right\rangle \ne \left| 1\right\rangle \), it is easily checked that our previous analyses remain valid.
 
9
Note further that Eve can neither sort out \(\left| c\right\rangle =\left| 1\right\rangle \) in \({\mathcal {C}}^{\text {AB}}_r\) nor Alice’s state \(\left| \chi _A\right\rangle \) in \({\mathcal {C}}^{\text {AB}}_o\) (See also [SC.1] and [SC.2]).
 
10
The integration limits, from \(-\infty \) to \(\infty \), are approximated by this condition.
 
Literatur
1.
Langley, P.: Elements of Machine Learning. Morgan Kaufmann, San Francisco (1996)
2.
Manzano, D., Pawłowski, M., Brukner, Č.: The speed of quantum and classical learning for performing the \(k\)th root of NOT. New J. Phys. 11, 113018 (2009)CrossRefADS
3.
Hentschel, A., Sanders, B.C.: Machine learning for precise quantum measurement. Phys. Rev. Lett. 104, 063603 (2010)CrossRefADS
4.
Bang, J., Ryu, J., Yoo, S., Pawłowski, M., Lee, J.: A strategy for quantum algorithm design assisted by machine learning. New J. Phys. 11, 113018 (2014)
5.
Yoo, S., Bang, J., Lee, C., Lee, J.: A quantum speedup in machine learning: finding an N-bit Boolean function for a classification. New J. Phys. 16, 103014 (2014)CrossRefADS
6.
Tiersch, M., Ganahl, E. J., Briegel, H. J.: Adaptive quantum computation in changing environments using projective simulation. arXiv:​1407.​1535 (2014)
7.
Bennett, C.H., DiVincenzo, D.P., Shor, P.W., Smolin, J.A., Terhal, B.M., Wootters, W.K.: Remote State Preparation. Phys. Rev. Lett. 87, 077902 (2001)CrossRefADS
8.
Reznik, B., Aharonov, Y., Groisman, B.: Remote operations and interactions for systems of arbitrary-dimensional Hilbert space: state-operator approach. Phys. Rev. A 65, 032312 (2002)CrossRefADS
9.
10.
Barreno, M., Nelson, B., Sears, R., Joseph, A. D., Tygar, J. D.: Can machine learning be secure?. In: Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security, ACM, New York, ASIACCS ’06, pp. 16 (2006)
11.
Barreno, M., Nelson, B., Joseph, A.D., Tygar, J.D.: The security of machine learning. Mach. Learn. 81, 121 (2010)MathSciNetCrossRef
12.
Nelson, B., Rubinstein, B.I.P., Huang, L., Joseph, A.D., Lee, S.J., Rao, S., Tygar, J.D.: Query strategies for evading convex-inducing classifiers. J. Mach. Learn. Res. 13, 1293 (2012)MathSciNetMATH
13.
Acín, A., Brunner, N., Gisin, N., Massar, S., Pironio, S., Scarani, V.: Device-independent security of quantum cryptography against collective attacks. Phys. Rev. Lett. 98, 230501 (2007)CrossRefADS
14.
Hioe, F.T., Eberly, J.H.: N-level coherence vector and higher conservation laws in quantum optics and qusntum mechanics. Phys. Rev. Lett. 47, 838 (1981)MathSciNetCrossRefADS
15.
16.
Reck, M., Zeilinger, A., Bernstein, H.J., Bertani, P.: Experimental realization of any discrete unitary operator. Phys. Rev. Lett. 73, 58 (1994)CrossRefADS
17.
18.
Fiurášek, J.: Linear-optics quantum Toffoli and Fredkin gates. Phys. Rev. A 73, 062313 (2006)CrossRefADS
19.
Wang, B., Duan, L.-M.: Implementation scheme of controlled SWAP gates for quantum fingerprinting and photonic quantum computation. Phys. Rev. A 75, 050304 (2007)CrossRefADS
20.
Ljunggren, D., Bourennane, M., Karlsson, A.: Authority-based user authentication in quantum key distribution. Phys. Rev. A 62, 022305 (2000)CrossRefADS
21.
Curty, M., Santos, D.J.: Quantum authentication of classical messages. Phys. Rev. A 64, 062309 (2001)CrossRefADS
22.
23.
Bruß, D., Macchiavello, C.: Optimal state estimation for d-dimensional quantum system. Phys. Lett. A 253, 249 (1999)CrossRefADS
Metadaten
Titel
Protocol for secure quantum machine learning at a distant place
verfasst von
Jeongho Bang
Seung-Woo Lee
Hyunseok Jeong
Publikationsdatum
01.10.2015
Verlag
Springer US
Erschienen in
Quantum Information Processing / Ausgabe 10/2015
Print ISSN: 1570-0755
Elektronische ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-015-1089-7

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