• Advanced Photonics
  • Vol. 3, Issue 6, 066002 (2021)
Alessia Suprano1、†, Danilo Zia1, Emanuele Polino1, Taira Giordani1, Luca Innocenti2、3、4, Alessandro Ferraro3, Mauro Paternostro3, Nicolò Spagnolo1, and Fabio Sciarrino1、*
Author Affiliations
  • 1Sapienza Università di Roma, Dipartimento di Fisica, Roma, Italy
  • 2Palacký University, Department of Optics, Olomouc, Czech Republic
  • 3Queen’s University Belfast, School of Mathematics and Physics, Centre for Theoretical Atomic, Molecular, and Optical Physics, Belfast, United Kingdom
  • 4Università degli Studi di Palermo, Dipartimento di Fisica e Chimica-Emilio Segrè, Palermo, Italy
  • show less
    DOI: 10.1117/1.AP.3.6.066002 Cite this Article Set citation alerts
    Alessia Suprano, Danilo Zia, Emanuele Polino, Taira Giordani, Luca Innocenti, Alessandro Ferraro, Mauro Paternostro, Nicolò Spagnolo, Fabio Sciarrino. Dynamical learning of a photonics quantum-state engineering process[J]. Advanced Photonics, 2021, 3(6): 066002 Copy Citation Text show less
    References

    [1] H. Bechmann-Pasquinucci, A. Peres. Quantum cryptography with 3-state systems. Phys. Rev. Lett., 85, 3313-3316(2000).

    [2] T. Vértesi, S. Pironio, N. Brunner. Closing the detection loophole in Bell experiments using qudits. Phys. Rev. Lett., 104, 060401(2010).

    [3] B. P. Lanyon et al. Simplifying quantum logic using higher-dimensional Hilbert spaces. Nat. Phys., 5, 134-140(2009).

    [4] T. C. Ralph, K. J. Resch, A. Gilchrist. Efficient Toffoli gates using qudits. Phys. Rev. A, 75, 022313(2007).

    [5] B. Anderson et al. Accurate and robust unitary transformations of a high-dimensional quantum system. Phys. Rev. Lett., 114, 240401(2015).

    [6] A. Rossi et al. Multipath entanglement of two photons. Phys. Rev. Lett., 102, 153902(2009).

    [7] M. Hofheinz et al. Synthesizing arbitrary quantum states in a superconducting resonator. Nature, 459, 546-549(2009).

    [8] A. C. Dada et al. Experimental high-dimensional two-photon entanglement and violations of generalized Bell inequalities. Nat. Phys., 7, 677-680(2011).

    [9] S. Rosenblum et al. A CNOT gate between multiphoton qubits encoded in two cavities. Nat. Commun., 9, 652(2018).

    [10] R. W. Heeres et al. Implementing a universal gate set on a logical qubit encoded in an oscillator. Nat. Commun., 8, 94(2017).

    [11] T. Giordani et al. Experimental engineering of arbitrary qudit states with discrete-time quantum walks. Phys. Rev. Lett., 122, 020503(2019).

    [12] S. E. Venegas-Andraca. Quantum walks: a comprehensive review. Quantum Inf. Process., 11, 1015-1106(2012).

    [13] H. Schmitz et al. Quantum walk of a trapped ion in phase space. Phys. Rev. Lett., 103, 090504(2009).

    [14] F. Zähringer et al. Realization of a quantum walk with one and two trapped ions. Phys. Rev. Lett., 104, 100503(2010).

    [15] M. Karski et al. Quantum walk in position space with single optically trapped atoms. Science, 325, 174-177(2009).

    [16] L. Sansoni et al. Two-particle bosonic-fermionic quantum walk via integrated photonics. Phys. Rev. Lett., 108, 010502(2012).

    [17] A. Crespi et al. Anderson localization of entangled photons in an integrated quantum walk. Nat. Photonics, 7, 322-328(2013).

    [18] F. Cardano et al. Quantum walks and wavepacket dynamics on a lattice with twisted photons. Sci. Adv., 1, e1500087(2015).

    [19] F. Caruso et al. Fast escape of a quantum walker from an integrated photonic maze. Nat. Commun., 7, 11682(2016).

    [20] T. Kitagawa et al. Observation of topologically protected bound states in photonic quantum walks. Nat. Commun., 3, 882(2012).

    [21] X. Qiang et al. Efficient quantum walk on a quantum processor. Nat. Commun., 7, 11511(2016).

    [22] J. O. Owens et al. Two-photon quantum walks in an elliptical direct-write waveguide array. New J. Phys., 13, 075003(2011).

    [23] J. Boutari et al. Large scale quantum walks by means of optical fiber cavities. J. Opt., 18, 094007(2016).

    [24] L. Innocenti et al. Quantum state engineering using one-dimensional discrete-time quantum walks. Phys. Rev. A, 96, 062326(2017).

    [25] A. Suprano et al. Enhanced detection techniques of orbital angular momentum states in the classical and quantum regimes. New J. Phys., 23, 073014(2021).

    [26] L. Allen et al. Orbital angular momentum of light and the transformation of Laguerre-Gaussian laser modes. Phys. Rev. A, 45, 8185-8189(1992).

    [27] A. M. Yao, M. J. Padgett. Orbital angular momentum: origins, behavior and applications. Adv. Opt. Photonics, 3, 161-204(2011).

    [28] B. Piccirillo et al. The orbital angular momentum of light: genesis and evolution of the concept and of the associated photonic technology. Riv. Nuovo Cimento, 36, 501-555(2013).

    [29] Q. Zhan. Trapping metallic Rayleigh particles with radial polarization. Opt. Express, 12, 3377-3382(2004).

    [30] S. Fürhapter et al. Spiral phase contrast imaging in microscopy. Opt. Express, 13, 689-694(2005).

    [31] F. Tamburini et al. Overcoming the Rayleigh criterion limit with optical vortices. Phys. Rev. Lett., 97, 163903(2006).

    [32] M. P. Lavery et al. Detection of a spinning object using light’s orbital angular momentum. Science, 341, 537-540(2013).

    [33] L. Torner, J. P. Torres, S. Carrasco. Digital spiral imaging. Opt. Express, 13, 873-881(2005).

    [34] D. S. Simon, A. V. Sergienko. Two-photon spiral imaging with correlated orbital angular momentum states. Phys. Rev. A, 85, 043825(2012).

    [35] N. Uribe-Patarroyo et al. Object identification using correlated orbital angular momentum states. Phys. Rev. Lett., 110, 043601(2013).

    [36] A. E. Willner et al. Optical communications using orbital angular momentum beams. Adv. Opt. Photonics, 7, 66-106(2015).

    [37] N. Bozinovic et al. Terabit-scale orbital angular momentum mode division multiplexing in fibers. Science, 340, 1545-1548(2013).

    [38] M. Malik et al. Influence of atmospheric turbulence on optical communications using orbital angular momentum for encoding. Opt. Express, 20, 13195-13200(2012).

    [39] J. Baghdady et al. Multi-gigabit/s underwater optical communication link using orbital angular momentum multiplexing. Opt. Express, 24, 9794-9805(2016).

    [40] J. Wang. Advances in communications using optical vortices. Photonics Res., 4, B14-B28(2016).

    [41] D. Cozzolino et al. High-dimensional quantum communication: benefits, progress, and future challenges. Adv. Quantum Technol., 2, 1900038(2019).

    [42] X.-L. Wang et al. Quantum teleportation of multiple degrees of freedom of a single photon. Nature, 518, 516-519(2015).

    [43] M. Krenn et al. Twisted photon entanglement through turbulent air across Vienna. Proc. Natl. Acad. Sci. U. S. A, 112, 14197-14201(2015).

    [44] M. Malik et al. Multi-photon entanglement in high dimensions. Nat. Photonics, 10, 248-252(2016).

    [45] A. Sit et al. High-dimensional intracity quantum cryptography with structured photons. Optica, 4, 1006-1010(2017).

    [46] S. D. Bartlett, H. deGuise, B. C. Sanders. Quantum encodings in spin systems and harmonic oscillators. Phys. Rev. A, 65, 052316(2002).

    [47] F. Cardano et al. Statistical moments of quantum-walk dynamics reveal topological quantum transitions. Nat. Commun., 7, 11439(2016).

    [48] I. Buluta, F. Nori. Quantum simulators. Science, 326, 108-111(2009).

    [49] M. Mirhosseini et al. High-dimensional quantum cryptography with twisted light. New J. Phys., 17, 033033(2015).

    [50] F. Bouchard et al. Quantum cryptography with twisted photons through an outdoor underwater channel. Opt. Express, 26, 22563-22573(2018).

    [51] J. Li, M. Zhang, D. Wang. Adaptive demodulator using machine learning for orbital angular momentum shift keying. IEEE Photonics Technol. Lett., 29, 1455-1458(2017).

    [52] J. M. Arrazola et al. Machine learning method for state preparation and gate synthesis on photonic quantum computers. Quantum Sci. Technol., 4, 024004(2019).

    [53] J. Mackeprang, D. B. R. Dasari, J. Wrachtrup. A reinforcement learning approach for quantum state engineering. Quantum Mach. Intell., 2, 5(2020).

    [54] W. Ma et al. Deep learning for the design of photonic structures. Nat. Photonics, 15, 77-90(2021).

    [55] P. R. Wiecha et al. Deep learning in nano-photonics: inverse design and beyond. Photonics Res., 9, B182-B200(2021).

    [56] M. Benedetti et al. A generative modeling approach for benchmarking and training shallow quantum circuits. npj Quantum Inf., 5, 45(2019).

    [57] S. Yu et al. Reconstruction of a photonic qubit state with reinforcement learning. Adv. Quantum Technol., 2, 1800074(2019).

    [58] T. Giordani et al. Machine learning-based classification of vector vortex beams. Phys. Rev. Lett., 124, 160401(2020).

    [59] A. A. Melnikov et al. Active learning machine learns to create new quantum experiments. Proc. Natl. Acad. Sci. U. S. A., 115, 1221-1226(2018).

    [60] Y. Ren et al. Genetic-algorithm-based deep neural networks for highly efficient photonic device design. Photonics Res., 9, B247-B252(2021).

    [61] L. O’Driscoll, R. Nichols, P. Knott. A hybrid machine learning algorithm for designing quantum experiments. Quantum Mach. Intell., 1, 5-15(2019).

    [62] A. Lumino et al. Experimental phase estimation enhanced by machine learning. Phys. Rev. Appl., 10, 044033(2018).

    [63] R. Santagati et al. Witnessing eigenstates for quantum simulation of Hamiltonian spectra. Sci. Adv., 4, eaap9646(2018).

    [64] J. Wang et al. Experimental quantum Hamiltonian learning. Nat. Phys., 13, 551-555(2017).

    [65] K. Rambhatla et al. Adaptive phase estimation through a genetic algorithm. Phys. Rev. Res., 2, 033078(2020).

    [66] A. A. Melnikov, P. Sekatski, N. Sangouard. Setting up experimental bell tests with reinforcement learning. Phys. Rev. Lett., 125, 160401(2020).

    [67] D. Poderini et al. Ab-initio experimental violation of bell inequalities(2021).

    [68] K. Bharti et al. Machine learning meets quantum foundations: a brief survey. AVS Quantum Sci., 2, 034101(2020).

    [69] R. Fickler, M. Ginoya, R. W. Boyd. Custom-tailored spatial mode sorting by controlled random scattering. Phys. Rev. B, 95, 161108(2017).

    [70] A. Costa, G. Nannicini. RBFOpt: an open-source library for black-box optimization with costly function evaluations. Math. Programming Comput., 10, 597-629(2018).

    [71] G. Nannicini. On the implementation of a global optimization method for mixed-variable problems(2021).

    [72] M. J. D. Powell, W. Light. The theory of radial basis function approximation in 1990. Advances in Numerical Analysis, Vol. II: Wavelets, Subdivision Algorithms and Radial Functions, 105-210(1992).

    [73] M. J. D. Powell, M. W. Müller et al. Recent research at Cambridge on radial basis functions. New Developments in Approximation Theory, 215-232(1999).

    [74] M. D. Buhmann. Radial Basis Functions: Theory and Implementations, Cambridge Monographs on Applied and Computational Mathematics(2003).

    [75] L. Marrucci, C. Manzo, D. Paparo. Optical spin-to-orbital angular momentum conversion in inhomogeneous anisotropic media. Phys. Rev. Lett., 96, 163905(2006).

    [76] The source code of the RBFOpt algorithm.

    [77] RBFOpt documentation.

    [78] M. Fitzi, N. Gisin, U. Maurer. Quantum solution to the byzantine agreement problem. Phys. Rev. Lett., 87, 217901(2001).

    [79] N. J. Cerf et al. Security of quantum key distribution using d-level systems. Phys. Rev. Lett., 88, 127902(2002). https://doi.org/10.1103/PhysRevLett.88.127902

    [80] D. Bruß, C. Macchiavello. Optimal eavesdropping in cryptography with three-dimensional quantum states. Phys. Rev. Lett., 88, 127901(2002).

    [81] A. Acin, N. Gisin, V. Scarani. Security bounds in quantum cryptography using d-level systems(2003).

    [82] N. K. Langford et al. Measuring entangled qutrits and their use for quantum bit commitment. Phys. Rev. Lett., 93, 053601(2004).

    [83] E. T. Campbell, H. Anwar, D. E. Browne. Magic-state distillation in all prime dimensions using quantum Reed–Muller codes. Phys. Rev. X, 2, 041021(2012).

    [84] H.-M. Gutmann. A radial basis function method for global optimization. J. Global Optim., 19, 201-227(2001).

    [85] R. Regis, C. Shoemaker. A stochastic radial basis function method for the global optimization of expensive functions. INFORMS J. Comput., 19, 497-509(2007).

    [86] E. Bolduc et al. Exact solution to simultaneous intensity and phase encryption with a single phase-only hologram. Opt. Lett., 38, 3546-3549(2013).

    [87] A. Forbes, A. Dudley, M. McLaren. Creation and detection of optical modes with spatial light modulators. Adv. Opt. Photonics, 8, 200-227(2016).

    [88] H. Zhong et al. Quantum computational advantage using photons. Science, 370, 1460-1463(2020).

    [89] V. Cimini et al. Calibration of multiparameter sensors via machine learning at the single-photon level. Phys. Rev. Appl., 15, 044003(2021).

    [90] F. Hoch et al. Boson sampling in a reconfigurable continuously-coupled 3D photonic circuit(2021).

    [91] D. Brod et al. Photonic implementation of boson sampling: a review. Adv. Photonics, 1, 034001(2019).

    [92] J. Pan et al. Multiphoton entanglement and interferometry. Rev. Mod. Phys., 84, 777-838(2012).

    [93] M. J. D. Powell. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J., 7, 155-162(1964).

    Alessia Suprano, Danilo Zia, Emanuele Polino, Taira Giordani, Luca Innocenti, Alessandro Ferraro, Mauro Paternostro, Nicolò Spagnolo, Fabio Sciarrino. Dynamical learning of a photonics quantum-state engineering process[J]. Advanced Photonics, 2021, 3(6): 066002
    Download Citation