Top

Quantum Information Processing

Published in:

01-03-2024

Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics

Authors: Andrey Zhukov, Walter Pogosov

Published in: Quantum Information Processing | Issue 3/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

We address a learning-based quantum error mitigation method, which utilizes deep neural network applied at the postprocessing stage, and study its performance in the presence of different types of quantum noises. We concentrate on the simulation of Trotterized dynamics of 2D spin lattice in the regime of high noise, when expectation values of bounded traceless observables are strongly suppressed. By using numerical simulations, we demonstrate a dramatic improvement of data quality for both local weight-1 and weight-2 observables for the depolarizing and inhomogeneous Pauli channels. At the same time, the effect of coherent ZZ crosstalks is not mitigated, so that in practice crosstalks should be at first converted into incoherent errors by randomized compiling.

previous article Randomized decision tree complexity of Deutsch–Jozsa problem and a generalization

next article Multi-user quantum private query using Bell states

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Preskill, J.: Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018)CrossRef

Kim, Y., Eddins, A., Anand, S., Wei, K.X., Van Den Berg, E., Rosenblatt, S., Nayfeh, H., Wu, Y., Zaletel, M., Temme, K., et al.: Evidence for the utility of quantum computing before fault tolerance. Nature 618(7965), 500–505 (2023)ADSCrossRef

Li, Y., Benjamin, S.C.: Efficient variational quantum simulator incorporating active error minimization. Phys. Rev. X 7(2), 021050 (2017)

Temme, K., Bravyi, S., Gambetta, J.M.: Error mitigation for short-depth quantum circuits. Phys. Rev. Lett. 119, 180509 (2017)ADSMathSciNetCrossRef

Endo, S., Benjamin, S.C., Li, Y.: Practical quantum error mitigation for near-future applications. Phys. Rev. X 8(3), 031027 (2018)

Kandala, A., Temme, K., Córcoles, A.D., Mezzacapo, A., Chow, J.M., Gambetta, J.M.: Error mitigation extends the computational reach of a noisy quantum processor. Nature 567(7749), 491–495 (2019)ADSCrossRef

He, A., Nachman, B., de Jong, W.A., Bauer, C.W.: Zero-noise extrapolation for quantum-gate error mitigation with identity insertions. Phys. Rev. A 102(1), 012426 (2020)ADSMathSciNetCrossRef

Vovrosh, J., Khosla, K.E., Greenaway, S., Self, C., Kim, M.S., Knolle, J.: Simple mitigation of global depolarizing errors in quantum simulations. Phys. Rev. E 104(3), 035309 (2021)ADSCrossRef

Urbanek, M., Nachman, B., Pascuzzi, V.R., He, A., Bauer, C.W., de Jong, W.A.: Mitigating depolarizing noise on quantum computers with noise-estimation circuits. Phys. Rev. Lett. 127(27), 270502 (2021)MathSciNetCrossRef

10.

Strikis, A., Qin, D., Chen, Y., Benjamin, S.C., Li, Y.: Learning-based quantum error mitigation. PRX. Quantum 2(4), 040330 (2021)ADS

11.

Czarnik, P., Arrasmith, A., Coles, P.J., Cincio, L.: Error mitigation with clifford quantum-circuit data. Quantum 5, 592 (2021)CrossRef

12.

Sun, J., Yuan, X., Tsunoda, T., Vedral, V., Benjamin, S.C., Endo, S.: Mitigating realistic noise in practical noisy intermediate-scale quantum devices. Phys. Rev. Appl. 15(3), 034026 (2021)ADSCrossRef

13.

Cai, Z.: Multi-exponential error extrapolation and combining error mitigation techniques for NISQ applications. Npj Quantum Inf. 7(1), 80 (2021)ADSCrossRef

14.

Mari, A., Shammah, N., Zeng, W.J.: Extending quantum probabilistic error cancellation by noise scaling. Phys. Rev. A 104(5), 052607 (2021)ADSMathSciNetCrossRef

15.

Cai, Z., Babbush, R., Benjamin, S.C., Endo, S., Huggins, W.J., Li, Y., McClean, J.R., O’Brien, T.E.: Quantum Error Mitigation. arXiv:2210.00921 (2022)

16.

Perrin, H., Scoquart, T., Shnirman, A., Schmalian, J., Snizhko, K.: Mitigating Crosstalk Errors by Randomized Compiling: Simulation of the BCS Model on a Superconducting Quantum Computer. arXiv:2305.02345 (2023)

17.

Kim, Y., Wood, C.J., Yoder, T.J., Merkel, S.T., Gambetta, J.M., Temme, K., Kandala, A.: Scalable error mitigation for noisy quantum circuits produces competitive expectation values. Nat. Phys. 19, 752–759 (2023)CrossRef

18.

Bultrini, D., Gordon, M.H., Czarnik, P., Arrasmith, A., Cerezo, M., Coles, P.J., Cincio, L.: Unifying and benchmarking state-of-the-art quantum error mitigation techniques. Quantum 7, 1034 (2023)CrossRef

19.

Van Den Berg, E., Minev, Z.K., Kandala, A., Temme, K.: Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors. Nat. Phys. 19, 1116–1121 (2023)CrossRef

20.

Kern, O., Alber, G., Shepelyansky, D.L.: Quantum error correction of coherent errors by randomization. Eur. Phys. J. D At. Mol. Opt. Plasma Phys. 32, 153–156 (2005)

21.

Wallman, J.J., Emerson, J.: Noise tailoring for scalable quantum computation via randomized compiling. Phys. Rev. A 94(5), 052325 (2016)ADSCrossRef

22.

Hashim, A., Naik, R.K., Morvan, A., Ville, J.-L., Mitchell, B., Kreikebaum, J.M., Davis, M., Smith, E., Iancu, C., O’Brien, K.P., Hincks, I., Wallman, J.J., Emerson, J., Siddiqi, I.: Randomized compiling for scalable quantum computing on a noisy superconducting quantum processor. Phys. Rev. X 11, 041039 (2021)

23.

Zhukov, A., Pogosov, W.: Quantum error reduction with deep neural network applied at the post-processing stage. Quantum Inf. Process. 21(3), 93 (2022)ADSMathSciNetCrossRef

24.

Lennon, D., Moon, H., Camenzind, L., Yu, L., Zumbühl, D., Briggs, G., Osborne, M., Laird, E., Ares, N.: Efficiently measuring a quantum device using machine learning. Npj Quantum Inf. 5(1), 1–8 (2019)

25.

Nautrup, H.P., Delfosse, N., Dunjko, V., Briegel, H.J., Friis, N.: Optimizing quantum error correction codes with reinforcement learning. Quantum 3, 215 (2019)CrossRef

26.

Baireuther, P., O’Brien, T.E., Tarasinski, B., Beenakker, C.W.: Machine-learning-assisted correction of correlated qubit errors in a topological code. Quantum 2, 48 (2018)CrossRef

27.

Andreasson, P., Johansson, J., Liljestrand, S., Granath, M.: Quantum error correction for the toric code using deep reinforcement learning. Quantum 3, 183 (2019)CrossRef

28.

Kalantre, S.S., Zwolak, J.P., Ragole, S., Wu, X., Zimmerman, N.M., Stewart, M.D., Taylor, J.M.: Machine learning techniques for state recognition and auto-tuning in quantum dots. Npj Quantum Inf. 5(1), 1–10 (2019)ADSCrossRef

29.

Bukov, M., Day, A.G., Sels, D., Weinberg, P., Polkovnikov, A., Mehta, P.: Reinforcement learning in different phases of quantum control. Phys. Rev. X 8(3), 031086 (2018)

30.

Niu, M.Y., Boixo, S., Smelyanskiy, V.N., Neven, H.: Universal quantum control through deep reinforcement learning. Npj Quantum Inf. 5(1), 1–8 (2019)CrossRef

31.

Babukhin, D.V., Zhukov, A.A., Pogosov, W.V.: Nondestructive classification of quantum states using an algorithmic quantum computer. Quantum Mach. Intell. 1(3), 87–96 (2019)CrossRef

32.

Carrasquilla, J., Melko, R.G.: Machine learning phases of matter. Nat. Phys. 13(5), 431–434 (2017)CrossRef

33.

Altepeter, J.B., Jeffrey, E.R., Kwiat, P.G.: Photonic state tomography. Adv. At. Mol. Opt. Phys. 52, 105–159 (2005)ADSCrossRef

34.

Torlai, G., Mazzola, G., Carrasquilla, J., Troyer, M., Melko, R., Carleo, G.: Neural-network quantum state tomography. Nat. Phys. 14(5), 447–450 (2018)CrossRef

35.

Neugebauer, M., Fischer, L., Jäger, A., Czischek, S., Jochim, S., Weidemüller, M., Gärttner, M.: Neural-network quantum state tomography in a two-qubit experiment. Phys. Rev. A 102(4), 042604 (2020)ADSCrossRef

36.

Lohani, S., Kirby, B.T., Brodsky, M., Danaci, O., Glasser, R.T.: Machine learning assisted quantum state estimation. Mach. Learn. Sci. Technol. 1(3), 035007 (2020)CrossRef

37.

Sehayek, D., Golubeva, A., Albergo, M.S., Kulchytskyy, B., Torlai, G., Melko, R.G.: Learnability scaling of quantum states: restricted Boltzmann machines. Phys. Rev. B 100(19), 195125 (2019)ADSCrossRef

38.

Palmieri, A.M., Kovlakov, E., Bianchi, F., Yudin, D., Straupe, S., Biamonte, J.D., Kulik, S.: Experimental neural network enhanced quantum tomography. Npj Quantum Inf. 6(1), 20 (2020)ADSCrossRef

39.

Hashim, A., Seritan, S., Proctor, T., Rudinger, K., Goss, N., Naik, R.K., Kreikebaum, J.M., Santiago, D.I., Siddiqi, I.: Benchmarking quantum logic operations relative to thresholds for fault tolerance. Npj Quantum Inf. 9(1) (2023)

40.

Zhukov, A.A., Kiktenko, E.O., Elistratov, A.A., Pogosov, W.V., Lozovik, Y.E.: Quantum communication protocols as a benchmark for programmable quantum computers. Quantum Inf. Process. 18(1), 1–23 (2019)ADSCrossRef

41.

Babukhin, D.V., Zhukov, A.A., Pogosov, W.V.: Hybrid digital-analog simulation of many-body dynamics with superconducting qubits. Phys. Rev. A 101, 052337 (2020)ADSCrossRef

42.

Ostmeyer, J.: Optimised trotter decompositions for classical and quantum computing. J. Phys. A Math. Theor. 56(28), 285303 (2023)ADSMathSciNetCrossRef

43.

Mc Keever, C., Lubasch, M.: Classically optimized Hamiltonian simulation. Phys. Rev. Res. 5, 023146 (2023)CrossRef

44.

Genois, E., Gross, J.A., Di Paolo, A., Stevenson, N.J., Koolstra, G., Hashim, A., Siddiqi, I., Blais, A.: Quantum-tailored machine-learning characterization of a superconducting qubit. PRX Quantum 2, 040355 (2021)ADSCrossRef

Title: Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics
Authors: Andrey Zhukov
Walter Pogosov
Publication date: 01-03-2024
Publisher: Springer US
Published in: Quantum Information Processing / Issue 3/2024
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI: https://doi.org/10.1007/s11128-024-04296-y

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 3/2024

Advancing multi-party quantum key agreement protocols: the power of a two-layer approach

Relating intrinsic concurrence to quantum steering and its application in teleportation for three-qubit states

Does conditional entropy squeezing indicate normalized entropic uncertainty relation steering?

On the algebraic structure of quasi-polycyclic codes and new quantum codes

Geometric speed limit for fermionic dimer as a hallmark of Coulomb interaction

Euclidean time method in generalized eigenvalue equation