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Mathematics

Universal discriminative quantum neural networks

Profile image of Masoud MohseniMasoud Mohseni

2020, Quantum Machine Intelligence

https://doi.org/10.1007/S42484-020-00025-7
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11 pages

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Abstract

Recent results have demonstrated the successful applications of quantum-classical hybrid methods to train quantum circuits for a variety of machine learning tasks. A natural question to ask is consequentially whether we can also train such quantum circuits to discriminate quantum data, i.e., perform classification on data stored in form of quantum states. Although quantum mechanics fundamentally forbids deterministic discrimination of non-orthogonal states, we show in this work that it is possible to train a quantum circuit to discriminate such data with a trade-off between minimizing error rates and inconclusiveness rates of the classification tasks. Our approach achieves at the same time a performance which is close to the theoretically optimal values and a generalization ability to previously unseen quantum data. This generalization power hence distinguishes our work from previous circuit optimization results and furthermore provides an example of a quantum machine learning task ...

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References (43)

  1. Amin MH, Andriyash E, Rolfe J, Kulchytskyy B, Melko R (2018) Quantum Boltzmann machine. Physical Review X 8(2):021050. https://doi.org/10.1103/physrevx.8.021050
  2. 1 This assumes that the cost function follows a normal distribution with variance of the order 1
  3. √ N , where N is the number of measurements made in reach run in order to calculate the cost function. Banchi L, Pancotti N, Bose S (2016) Quantum gate learning in qubit networks: Toffoli gate without time-dependent control. Npj Quantum Inf 2:16019
  4. Barnett SM, Croke S (2009) Quantum state discrimination. Adv Opt Photonics 1(2):238. https://www.osapublishing.org/aop/abstract. cfm?uri=aop-1-2-238
  5. Barzanjeh S, Guha S, Weedbrook C, Vitali D, Shapiro JH, Pirandola S (2015) Microwave quantum illumination. Phys Rev Lett 114(8):080503. https://doi.org/10.1103/physrevlett.114.080503
  6. Bennett CH (1992a) Quantum cryptography using any two nonorthog- onal states. Phys Rev Lett 68(21):3121
  7. Bennett CH (1992b) Quantum cryptography using any two nonorthogonal states. Phys Rev Lett 68(21):3121-3124. https://doi.org/10.1103/physrevlett.68.3121
  8. Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195-202. https://doi.org/10.1038/nature23474
  9. Chefles A (2000) Quantum state discrimination. Contemp Phys 41(6):401-424
  10. Ciliberto C, Herbster M, Ialongo AD, Pontil M, Rocchetto A, Severini S, Wossnig L (2018) Quantum machine learning: a classical perspective. Proc R Soc A 474(2209):20170551
  11. Cong I, Choi S, Lukin MD (2019) Quantum convolu- tional neural networks. Nat Phys 15(12):1273-1278. https://doi.org/10.1038/s41567-019-0648-8
  12. Degen C, Reinhard F, Cappellaro P (2017) Quantum sensing. Rev Mod Phys 89(3):035002. https://doi.org/10.1103/revmodphys.89. 035002
  13. Duan LM, Guo GC (1998) Probabilistic cloning and identification of linearly independent quantum states. Phys Rev Lett 80(22):4999
  14. Fanizza M, Mari A, Giovannetti V (2018) Optimal universal learning machines for quantum state discrimination. arXiv:180503477
  15. Farhi E, Neven H (2018) Classification with quantum neural networks on near term processors. arXiv:180206002
  16. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems, pp 2672-2680
  17. Grant E, Benedetti M, Cao S, Hallam A, Lockhart J, Stojevic V, Green AG, Severini S (2018) Hierarchical quantum classifiers. arXiv:180403680
  18. Innocenti L, Banchi L, Ferraro A, Bose S, Paternostro M (2018) Supervised learning of time-independent Hamiltonians for gate design. arXiv:180307119
  19. Iten R, Colbeck R, Christandl M (2016) Quantum circuits for quantum channels. Phys Rev A Atom Mol Opt Phys 93(3):052316. https://doi.org/10.1103/PhysRevA.95.052316, arXiv:1609.08103
  20. Iten R, Colbeck R, Kukuljan I, Home J, Christandl M (2015) Quantum circuits for isometries. Physical Review A -Atomic, Molecular, and Optical Physics. https://doi.org/10.1103/PhysRevA.93.032318. arXiv:1501.06911
  21. Karzig T, Knapp C, Lutchyn RM, Bonderson P, Hastings MB, Nayak C, Alicea J, Flensberg K, Plugge S, Oreg Y, Marcus CM, Freedman MH (2017) Scalable designs for quasiparticle-poisoning-protected topological quantum computa- tion with Majorana zero modes. Phys Rev B 95(23):235305. https://doi.org/10.1103/physrevb.95.235305
  22. Khatri S, LaRose R, Poremba A, Cincio L, Sornborger AT, Coles PJ (2019) Quantum-assisted quantum compiling. Quantum 3:140. https://doi.org/10.22331/q-2019-05-13-140
  23. Kimble HJ (2008) The quantum Internet. Nature 453(7198):1023- 1030. https://doi.org/10.1038/nature07127
  24. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980
  25. Kübler JM, Arrasmith A, Cincio L, Coles PJ (2019) An adap- tive optimizer for measurement-frugal variational algorithms. arXiv:1909.09083
  26. Li Y, Benjamin SC (2017) Efficient variational quantum simulator incorporating active error minimization. Phys Rev X 7(2):021050
  27. Lloyd S, Weedbrook C (2018) Quantum generative adversarial learning. Phys Rev Lett 121(4):040502. htps:/doi.org/10.1103/ physrevlett.121.040502
  28. Mitarai K, Negoro M, Kitagawa M, Fujii K (2018) Quantum circuit learning. arXiv:180300745
  29. Mohseni M, Steinberg AM, Bergou JA (2004) Optical real- ization of optimal unambiguous discrimination for pure and mixed quantum states. Phys Rev Lett 93(20):200403. https://doi.org/10.1103/PhysRevLett.93.200403, 0401002
  30. Ostaszewski M, Grant E, Benedetti M (2019) Quantum circuit structure learning. arXiv:1905.09692
  31. Qi XL, Zhang SC (2011) Topological insulators and superconduc- tors. Rev Mod Phys 83(4):1057-1110. https://doi.org/10.1103/ revmodphys.83.1057
  32. Raynal P, Lütkenhaus N, van Enk SJ (2003) Reduction theorems for optimal unambiguous state discrimination of density matri- ces. Phys Rev A 68:022308. https://doi.org/10.1103/PhysRevA. 68.022308. arXiv:0304179
  33. Ren JG, Xu P, Yong HL, Zhang L, Liao SK, Yin J, Liu WY, Cai WQ, Yang M, Li L, Yang KX, Han X, Yao YQ, Li J, Wu HY, Wan S, Liu L, Liu DQ, Kuang YW, He ZP, Shang P, Guo C, Zheng RH, Tian K, Zhu ZC, Liu NL, Lu CY, Shu R, Chen YA, Peng CZ, Wang JY, Pan JW (2017) Ground-to-satellite quantum teleportation. Nature 549(7670):70-73. https://doi.org/10.1038/nature23675
  34. Rocchetto A, Grant E, Strelchuk S, Carleo G, Severini S (2018) Learning hard quantum distributions with variational autoen- coders. npj Quantum Information 4(1), https://doi.org/10.1038/ s41534-018-0077-z, arXiv:1710.00725
  35. Romero J, Olson JP, Aspuru-Guzik A (2017) Quantum autoencoders for efficient compression of quantum data. Quantum Sci Technol 2(4):045001
  36. Schaller G, Schützhold R (2006) Quantum algorithm for optical- template recognition with noise filtering. Phys Rev A 74(1):012303. https://doi.org/10.1103/physreva.74.012303
  37. Schuld M, Bocharov A, Svore K, Wiebe N (2018) Circuit-centric quantum classifiers. arXiv:180400633
  38. Shende VV, Bullock SS, Markov IL (2006) Synthesis of quantum logic circuits. IEEE Transactions on Computer- Aided Design of Integrated Circuits and Systems p 18, https://doi.org/10.1109/TCAD.2005.855930, arXiv:0406176
  39. Shende VV, Markov IL, Bullock SS (2004) Smaller two-qubit circuits for quantum communication and computation. In: Proceedings - Design automation and test in Europe conference and exhibition, vol 2, pp 980-985. https://doi.org/10.1109/DATE.2004.1269020
  40. Sønderby CK, Raiko T, Maaløe L, Sønderby SK, Winther O (2016) Ladder variational autoencoders. arXiv:1602.02282
  41. Verdon G, Broughton M, Biamonte J (2017) A quantum algorithm to train neural networks using low-depth circuits. arXiv:171205304
  42. Wan KH, Dahlsten O, Kristjánsson H, Gardner R, Kim M (2017) Quantum generalisation of feedforward neural networks. npj Quantum Inf 3(1):36
  43. Xu X, Sun J, Endo S, Li Y, Benjamin SC, Yuan X (2019) Variational algorithms for linear algebra. arXiv:1909.03898

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