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Frequency- and dissipation-dependent entanglement advantage in spin-network Quantum Reservoir Computing (2403.08998v2)

Published 13 Mar 2024 in quant-ph

Abstract: We study the performance of an Ising spin network for quantum reservoir computing (QRC) in linear and non-linear memory tasks. We investigate the extent to which quantumness enhances performance by monitoring the behaviour of quantum entanglement, which we quantify by the partial transpose of the density matrix. In the most general case where the effects of dissipation are incorporated, our results indicate that the strength of the entanglement advantage depends on the frequency of the input signal; the benefit of entanglement is greater with more rapidly fluctuating signals, whereas a low-frequency input is better suited to a non-entangled reservoir. This may be understood as a condition for an entanglement advantage to manifest itself: the system's quantum memory must survive for long enough for the temporal structure of the input signal to reveal itself. We also find that quantum entanglement empowers a spin-network quantum reservoir to remember a greater number of temporal features.

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References (62)
  1. Role of non-linear data processing on speech recognition task in the framework of reservoir computing. Scientific Reports, 10(1), Jan. 2020. ISSN 2045-2322. doi: 10.1038/s41598-019-56991-x. URL http://dx.doi.org/10.1038/s41598-019-56991-x.
  2. Brain-inspired photonic signal processor for generating periodic patterns and emulating chaotic systems. Physical Review Applied, 7(5), May 2017. ISSN 2331-7019. doi: 10.1103/physrevapplied.7.054014. URL http://dx.doi.org/10.1103/PhysRevApplied.7.054014.
  3. A. Atiya and A. Parlos. New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Transactions on Neural Networks, 11(3):697–709, 2000. doi: 10.1109/72.846741.
  4. Reservoir computing for macroeconomic forecasting with mixed-frequency data. International Journal of Forecasting, Dec. 2023. ISSN 0169-2070. doi: 10.1016/j.ijforecast.2023.10.009. URL http://dx.doi.org/10.1016/j.ijforecast.2023.10.009.
  5. L. Banchi. Accuracy vs memory advantage in the quantum simulation of stochastic processes, 2023. URL https://arxiv.org/abs/2312.13473.
  6. Coherent excitation transfer in a spin chain of three rydberg atoms. Phys. Rev. Lett., 114:113002, Mar 2015. doi: 10.1103/PhysRevLett.114.113002. URL https://link.aps.org/doi/10.1103/PhysRevLett.114.113002.
  7. Quantum-enhanced analysis of discrete stochastic processes. npj Quantum Information, 7(1), Aug. 2021. ISSN 2056-6387. doi: 10.1038/s41534-021-00459-2. URL http://dx.doi.org/10.1038/s41534-021-00459-2.
  8. Quantum reservoir computing using arrays of rydberg atoms. PRX Quantum, 3:030325, Aug 2022. doi: 10.1103/PRXQuantum.3.030325. URL https://link.aps.org/doi/10.1103/PRXQuantum.3.030325.
  9. H. Breuer and F. Petruccione. The Theory of Open Quantum Systems. Oxford University Press, Oxford, 2002.
  10. Provable quantum advantage in randomness processing. Nature Communications, 6(1), Sept. 2015. ISSN 2041-1723. doi: 10.1038/ncomms9203. URL http://dx.doi.org/10.1038/ncomms9203.
  11. Information processing capacity of dynamical systems. Scientific Reports, 2:514, 2012. doi: 10.1038/srep00514. URL https://doi.org/10.1038/srep00514.
  12. Reservoir computing with a single delay-coupled non-linear mechanical oscillator. Journal of Applied Physics, 124(15), Oct. 2018. ISSN 1089-7550. doi: 10.1063/1.5038038. URL http://dx.doi.org/10.1063/1.5038038.
  13. Taking advantage of noise in quantum reservoir computing. Scientific Reports, 13(1), May 2023. ISSN 2045-2322. doi: 10.1038/s41598-023-35461-5. URL http://dx.doi.org/10.1038/s41598-023-35461-5.
  14. Quantum reservoir computing implementation on coherently coupled quantum oscillators. npj Quantum Information, 9(1), July 2023. ISSN 2056-6387. doi: 10.1038/s41534-023-00734-4. URL http://dx.doi.org/10.1038/s41534-023-00734-4.
  15. A large-scale model of the functioning brain. Science, 338(6111):1202–1205, Nov. 2012. ISSN 1095-9203. doi: 10.1126/science.1225266. URL http://dx.doi.org/10.1126/science.1225266.
  16. Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction. Scientific Reports, 13(1), Nov. 2023. ISSN 2045-2322. doi: 10.1038/s41598-023-45015-4. URL http://dx.doi.org/10.1038/s41598-023-45015-4.
  17. K. Fujii and K. Nakajima. Harnessing disordered-ensemble quantum dynamics for machine learning. Phys. Rev. Appl., 8:024030, Aug 2017. doi: 10.1103/PhysRevApplied.8.024030. URL https://link.aps.org/doi/10.1103/PhysRevApplied.8.024030.
  18. K. Fujii and K. Nakajima. Quantum reservoir computing: A reservoir approach toward quantum machine learning on near-term quantum devices. In K. Nakajima and I. Fischer, editors, Reservoir Computing, Natural Computing Series. Springer, Singapore, 2021. doi: 10.1007/978-981-13-1687-6˙18. URL https://doi.org/10.1007/978-981-13-1687-6_18.
  19. Macromagnetic simulation for reservoir computing utilizing spin dynamics in magnetic tunnel junctions. Physical Review Applied, 10(3), Sept. 2018. ISSN 2331-7019. doi: 10.1103/physrevapplied.10.034063. URL http://dx.doi.org/10.1103/PhysRevApplied.10.034063.
  20. Scalable photonic platform for real-time quantum reservoir computing. Physical Review Applied, 20(1), July 2023. ISSN 2331-7019. doi: 10.1103/physrevapplied.20.014051. URL http://dx.doi.org/10.1103/PhysRevApplied.20.014051.
  21. Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting. Nature Nanotechnology, 17(5):460–469, May 2022. ISSN 1748-3395. doi: 10.1038/s41565-022-01091-7. URL http://dx.doi.org/10.1038/s41565-022-01091-7.
  22. D. Gilboa and J. R. McClean. Exponential quantum communication advantage in distributed learning, 2023.
  23. Exploring quantumness in quantum reservoir computing. Phys. Rev. A, 108:052427, Nov 2023. doi: 10.1103/PhysRevA.108.052427. URL https://link.aps.org/doi/10.1103/PhysRevA.108.052427.
  24. Quantum reservoir computing with a single nonlinear oscillator. Physical Review Research, 3(1), Jan. 2021. ISSN 2643-1564. doi: 10.1103/physrevresearch.3.013077. URL http://dx.doi.org/10.1103/PhysRevResearch.3.013077.
  25. Quantum computing with trapped ions. Physics Reports, 469(4):155–203, 2008. ISSN 0370-1573. doi: https://doi.org/10.1016/j.physrep.2008.09.003. URL https://www.sciencedirect.com/science/article/pii/S0370157308003463.
  26. Quantum computing with neutral atoms. Quantum, 4:327, 2020. doi: 10.22331/q-2020-09-21-327. URL https://doi.org/10.22331/q-2020-09-21-327.
  27. H. Jaeger and H. Haas. Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304:78–80, 2004. doi: 10.1126/science.1091277. URL https://www.science.org/doi/10.1126/science.1091277.
  28. Y. Kora and C. Simon. Coarse-graining and criticality in the human connectome, 2023.
  29. K. Korzekwa and M. Lostaglio. Quantum advantage in simulating stochastic processes. Physical Review X, 11(2), Apr. 2021. ISSN 2160-3308. doi: 10.1103/physrevx.11.021019. URL http://dx.doi.org/10.1103/PhysRevX.11.021019.
  30. Pattern recognition in reciprocal space with a magnon-scattering reservoir. Nature Communications, 14(1), July 2023. ISSN 2041-1723. doi: 10.1038/s41467-023-39452-y. URL http://dx.doi.org/10.1038/s41467-023-39452-y.
  31. High-speed photonic reservoir computing using a time-delay-based architecture: Million words per second classification. Physical Review X, 7(1), Feb. 2017. ISSN 2160-3308. doi: 10.1103/physrevx.7.011015. URL http://dx.doi.org/10.1103/PhysRevX.7.011015.
  32. Simulation complexity of open quantum dynamics: Connection with tensor networks. Physical Review Letters, 122(16), Apr. 2019. ISSN 1079-7114. doi: 10.1103/physrevlett.122.160401. URL http://dx.doi.org/10.1103/PhysRevLett.122.160401.
  33. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14(11):2531–2560, 2002. doi: 10.1162/089976602760407955. URL https://doi.org/10.1162/089976602760407955.
  34. Dynamical phase transitions in quantum reservoir computing. Phys. Rev. Lett., 127:100502, Aug 2021. doi: 10.1103/PhysRevLett.127.100502. URL https://link.aps.org/doi/10.1103/PhysRevLett.127.100502.
  35. Information processing capacity of spin-based quantum reservoir computing systems. Cognitive Computation, 15:1440–1451, 2023. doi: 10.1007/s12559-020-09772-y. URL https://link.springer.com/article/10.1007/s12559-020-09772-y.
  36. Information processing capacity of spin-based quantum reservoir computing systems. Cognitive Computation, 15(5):1440–1451, Oct. 2020. ISSN 1866-9964. doi: 10.1007/s12559-020-09772-y. URL http://dx.doi.org/10.1007/s12559-020-09772-y.
  37. Non-linear processing with a surface acoustic wave reservoir computer. Microsystem Technologies, 29(8):1197–1206, May 2023. ISSN 1432-1858. doi: 10.1007/s00542-023-05463-4. URL http://dx.doi.org/10.1007/s00542-023-05463-4.
  38. Correlations between quantumness and learning performance in reservoir computing with a single oscillator, 2023. URL https://arxiv.org/abs/2304.03462.
  39. Analytical evidence of nonlinearity in qubits and continuous-variable quantum reservoir computing. J. of Phys. Complex., 2(4):045008, 2021. doi: 10.1088/2632-072X/ac340e.
  40. Boosting computational power through spatial multiplexing in quantum reservoir computing. Physical Review Applied, 11(3):034021–1, Mar. 2019. doi: 10.1103/physrevapplied.11.034021. URL https://doi.org/10.1103/physrevapplied.11.034021.
  41. W. Nicola and C. Clopath. Supervised learning in spiking neural networks with force training. Nature Communications, 8(1), Dec. 2017. ISSN 2041-1723. doi: 10.1038/s41467-017-01827-3. URL http://dx.doi.org/10.1038/s41467-017-01827-3.
  42. Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing. Communications Physics, 4(1), Mar. 2021. ISSN 2399-3650. doi: 10.1038/s42005-021-00556-w. URL http://dx.doi.org/10.1038/s42005-021-00556-w.
  43. Relaxation of an isolated dipolar-interacting rydberg quantum spin system. Phys. Rev. Lett., 120:063601, Feb 2018. doi: 10.1103/PhysRevLett.120.063601. URL https://link.aps.org/doi/10.1103/PhysRevLett.120.063601.
  44. Experimentally modeling stochastic processes with less memory by the use of a quantum processor. Science Advances, 3(2), Feb. 2017. ISSN 2375-2548. doi: 10.1126/sciadv.1601302. URL http://dx.doi.org/10.1126/sciadv.1601302.
  45. Nanoscale neural network using non-linear spin-wave interference. Nature Communications, 12(1), Nov. 2021. ISSN 2041-1723. doi: 10.1038/s41467-021-26711-z. URL http://dx.doi.org/10.1038/s41467-021-26711-z.
  46. P. Pfeuty and R. J. Elliott. The ising model with a transverse field. ii. ground state properties. Journal of Physics C: Solid State Physics, 4:2370, 1971.
  47. M. B. Plenio. Logarithmic negativity: A full entanglement monotone that is not convex. Phys. Rev. Lett., 95:090503, Aug 2005. doi: 10.1103/PhysRevLett.95.090503. URL https://link.aps.org/doi/10.1103/PhysRevLett.95.090503.
  48. Quantum simulation of 2d antiferromagnets with hundreds of rydberg atoms. Nature, 595:233–238, 2021. doi: 10.1038/s41586-021-03585-1. URL https://doi.org/10.1038/s41586-021-03585-1.
  49. Learning to select actions with spiking neurons in the basal ganglia. Frontiers in Neuroscience, 6, 2012. ISSN 1662-4548. doi: 10.3389/fnins.2012.00002. URL http://dx.doi.org/10.3389/fnins.2012.00002.
  50. Emergent criticality in complex turing b‐type atomic switch networks. Advanced Materials, 24(2):286–293, Oct. 2011. ISSN 1521-4095. doi: 10.1002/adma.201103053. URL http://dx.doi.org/10.1002/adma.201103053.
  51. R. B. Stinchcombe. Ising model in a transverse field. i. basic theory. Journal of Physics C: Solid State Physics, 6(15):2459, 1973.
  52. S. Sunada and A. Uchida. Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing. Optica, 8(11):1388, Nov. 2021. ISSN 2334-2536. doi: 10.1364/optica.434918. URL http://dx.doi.org/10.1364/OPTICA.434918.
  53. Natural quantum reservoir computing for temporal information processing. Scientific Reports, 12:1353, 2022a. doi: 10.1038/s41598-022-05061-w. URL https://doi.org/10.1038/s41598-022-05061-w.
  54. Natural quantum reservoir computing for temporal information processing. Scientific Reports, 12(1), Jan. 2022b. ISSN 2045-2322. doi: 10.1038/s41598-022-05061-w. URL http://dx.doi.org/10.1038/s41598-022-05061-w.
  55. Recent advances in physical reservoir computing: A review. Neural Networks, 115:100–123, July 2019. ISSN 0893-6080. doi: 10.1016/j.neunet.2019.03.005. URL http://dx.doi.org/10.1016/j.neunet.2019.03.005.
  56. Neuromorphic computing with nanoscale spintronic oscillators. Nature, 547(7664):428–431, July 2017. ISSN 1476-4687. doi: 10.1038/nature23011. URL http://dx.doi.org/10.1038/nature23011.
  57. Evaluation of memory capacity of spin torque oscillator for recurrent neural networks. Japanese Journal of Applied Physics, 57(12):120307, Oct. 2018. ISSN 1347-4065. doi: 10.7567/jjap.57.120307. URL http://dx.doi.org/10.7567/JJAP.57.120307.
  58. Experimental demonstration of reservoir computing on a silicon photonics chip. Nature Communications, 5(1), Mar. 2014. ISSN 2041-1723. doi: 10.1038/ncomms4541. URL http://dx.doi.org/10.1038/ncomms4541.
  59. An experimental unification of reservoir computing methods. Neural Networks, 20:391–403, 2007. ISSN 0893-6080. doi: 10.1016/j.neunet.2007.04.003. URL https://www.sciencedirect.com/science/article/pii/S089360800700038X.
  60. G. Vidal and R. F. Werner. Computable measure of entanglement. Phys. Rev. A, 65:032314, Feb 2002. doi: 10.1103/PhysRevA.65.032314. URL https://link.aps.org/doi/10.1103/PhysRevA.65.032314.
  61. J. von Neumann. First draft of a report on the edvac. IEEE Annals of the History of Computing, 15(4):27–75, 1993. ISSN 1058-6180. doi: 10.1109/85.238389. URL http://dx.doi.org/10.1109/85.238389.
  62. On-chip phonon-magnon reservoir for neuromorphic computing. Nature Communications, 14(1), Dec. 2023. ISSN 2041-1723. doi: 10.1038/s41467-023-43891-y. URL http://dx.doi.org/10.1038/s41467-023-43891-y.
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