Abstract

Advances in photonic integrated circuits have recently enabled electrically reconfigurable optical systems that can implement universal linear optics transformations on spatial mode sets. This review paper covers progress in such “programmable nanophotonic processors” as well as emerging applications of the technology to problems including classical and quantum information processing and machine learning.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (6)

D. Pérez, I. Gasulla, and J. Capmany, “Programmable multifunctional integrated nanophotonics,” Nanophotonics 7, 1351–1371 (2018).
[Crossref]

S. Sun, H. Kim, Z. Luo, G. S. Solomon, and E. Waks, “A single-photon switch and transistor enabled by a solid-state quantum memory,” Science 361, 57–60 (2018).
[Crossref]

C. Sparrow, E. Martn-López, N. Maraviglia, A. Neville, C. Harrold, J. Carolan, Y. N. Joglekar, T. Hashimoto, N. Matsuda, J. L. O’Brien, D. P. Tew, and A. Laing, “Simulating the vibrational quantum dynamics of molecules using photonics,” Nature 557, 660–667 (2018).
[Crossref]

Y. Lahini, G. R. Steinbrecher, A. D. Bookatz, and D. Englund, “Quantum logic using correlated one-dimensional quantum walks,” npj Quantum Inf. 4, 2 (2018).
[Crossref]

M. Hu, C. E. Graves, C. Li, Y. Li, N. Ge, E. Montgomery, N. Davila, H. Jiang, R. S. Williams, J. J. Yang, Q. Xia, and J. P. Strachan, “Memristor-based analog computation and neural network classification with a dot product engine,” Adv. Mater. 30, 1705914 (2018).
[Crossref]

T. W. Hughes, M. Minkov, Y. Shi, and S. Fan, “Training of photonic neural networks through in situ backpropagation and gradient measurement,” Optica 5, 864–871 (2018).
[Crossref]

2017 (12)

J. Torrejon, M. Riou, F. A. Araujo, S. Tsunegi, G. Khalsa, D. Querlioz, P. Bortolotti, V. Cros, K. Yakushiji, A. Fukushima, H. Kubota, S. Yuasa, M. D. Stiles, and J. Grollier, “Neuromorphic computing with nanoscale spintronic oscillators,” Nature 547, 428–431 (2017).
[Crossref]

Y. H. Chen, T. Krishna, J. S. Emer, and V. Sze, “Eyeriss: an energy-efficient reconfigurable accelerator for deep convolutional neural networks,” IEEE J. Solid-State Circuits 52, 127–138 (2017).
[Crossref]

J.-H. Kim, S. Aghaeimeibodi, C. J. K. Richardson, R. P. Leavitt, D. Englund, and E. Waks, “Hybrid integration of solid-state quantum emitters on a silicon photonic chip,” Nano Lett. 17, 7394–7400 (2017).
[Crossref]

T. Rudolph, “Why I am optimistic about the silicon-photonic route to quantum computing,” APL Photon. 2, 030901 (2017).
[Crossref]

D. Pérez, I. Gasulla, L. Crudgington, D. J. Thomson, A. Z. Khokhar, K. Li, W. Cao, G. Z. Mashanovich, and J. Capmany, “Multipurpose silicon photonics signal processor core,” Nat. Commun. 8, 636 (2017).
[Crossref]

N. C. Harris, G. R. Steinbrecher, M. Prabhu, Y. Lahini, J. Mower, D. Bunandar, C. Chen, F. N. C. Wong, T. Baehr-Jones, M. Hochberg, S. Lloyd, and D. Englund, “Quantum transport simulations in a programmable nanophotonic processor,” Nat. Photonics 11, 447–452 (2017).
[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).
[Crossref]

A. N. Tait, T. F. de Lima, E. Zhou, A. X. Wu, M. A. Nahmias, B. J. Shastri, and P. R. Prucnal, “Neuromorphic photonic networks using silicon photonic weight banks,” Sci. Rep. 7, 7430 (2017).
[Crossref]

M. Pant, H. Krovi, D. Englund, and S. Guha, “Rate-distance tradeoff and resource costs for all-optical quantum repeaters,” Phys. Rev. A 95, 012304 (2017).
[Crossref]

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2016 (11)

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A. Sipahigil, R. E. Evans, D. D. Sukachev, M. J. Burek, J. Borregaard, M. K. Bhaskar, C. T. Nguyen, J. L. Pacheco, H. A. Atikian, C. Meuwly, R. M. Camacho, F. Jelezko, E. Bielejec, H. Park, M. Lončar, and M. D. Lukin, “An integrated diamond nanophotonics platform for quantum optical networks,” Science 354, 847–850 (2016).
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A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. Grabska-Barwińska, S. G. Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou, A. P. Badia, K. M. Hermann, Y. Zwols, G. Ostrovski, A. Cain, H. King, C. Summerfield, P. Blunsom, K. Kavukcuoglu, and D. Hassabis, “Hybrid computing using a neural network with dynamic external memory,” Nature 538, 471–476 (2016).
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A. Pantazi, S. Woźniak, T. Tuma, and E. Eleftheriou, “All-memristive neuromorphic computing with level-tuned neurons,” Nanotechnology 27, 355205 (2016).
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D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529, 484–489 (2016).
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S. K. Esser, P. A. Merolla, J. V. Arthur, A. S. Cassidy, R. Appuswamy, A. Andreopoulos, D. J. Berg, J. L. McKinstry, T. Melano, D. R. Barch, C. di Nolfo, P. Datta, A. Amir, B. Taba, M. D. Flickner, and D. S. Modha, “Convolutional networks for fast, energy-efficient neuromorphic computing,” Proc. Natl. Acad. Sci. USA 113, 11441–11446 (2016).
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2015 (15)

B. Shen, P. Wang, R. Polson, and R. Menon, “An integrated-nanophotonics polarization beamsplitter with 2.4 × 2.4 μm2 footprint,” Nat. Photonics 9, 378–382 (2015).
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Y. Yang, Y. Ma, H. Guan, Y. Liu, S. Danziger, S. Ocheltree, K. Bergman, T. Baehr-Jones, and M. Hochberg, “Phase coherence length in silicon photonic platform,” Opt. Express 23, 16890–16902 (2015).
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D. A. B. Miller, “Sorting out light,” Science 347, 1423–1424 (2015).
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C. Sun, M. T. Wade, Y. Lee, J. S. Orcutt, L. Alloatti, M. S. Georgas, A. S. Waterman, J. M. Shainline, R. R. Avizienis, S. Lin, B. R. Moss, R. Kumar, F. Pavanello, A. H. Atabaki, H. M. Cook, A. J. Ou, J. C. Leu, Y.-H. Chen, K. Asanović, R. J. Ram, M. Popović, and V. M. Stojanović, “Single-chip microprocessor that communicates directly using light,” Nature 528, 534–538 (2015).
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A. Y. Piggott, J. Lu, K. G. Lagoudakis, J. Petykiewicz, T. M. Babinec, and J. Vučković, “Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer,” Nat. Photonics 9, 374–377 (2015).
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2014 (4)

N. C. Harris, Y. Ma, J. Mower, T. Baehr-Jones, D. Englund, M. Hochberg, and C. Galland, “Efficient, compact and low loss thermo-optic phase shifter in silicon,” Opt. Express 22, 10487–10493 (2014).
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M. Poot and H. X. Tang, “Broadband nanoelectromechanical phase shifting of light on a chip,” Appl. Phys. Lett. 104, 061101 (2014).
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K. Nemoto, M. Trupke, S. J. Devitt, A. M. Stephens, B. Scharfenberger, K. Buczak, T. Nöbauer, M. S. Everitt, J. Schmiedmayer, and W. J. Munro, “Photonic architecture for scalable quantum information processing in diamond,” Phys. Rev. X 4, 031022 (2014).
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K. Vandoorne, P. Mechet, T. Van Vaerenbergh, M. Fiers, G. Morthier, D. Verstraeten, B. Schrauwen, J. Dambre, and P. Bienstman, “Experimental demonstration of reservoir computing on a silicon photonics chip,” Nat. Commun. 5, 3541 (2014).
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2013 (7)

2012 (3)

A. Aspuru-Guzik and P. Walther, “Photonic quantum simulators,” Nat. Phys. 8, 285–291 (2012).
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L. Larger, M. C. Soriano, D. Brunner, L. Appeltant, J. M. Gutierrez, L. Pesquera, C. R. Mirasso, and I. Fischer, “Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing,” Opt. Express 20, 3241–3249 (2012).
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2011 (2)

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C.-S. Poon and K. Zhou, “Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities,” Front. Neurosci. 5, 108 (2011).
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2010 (4)

J. Misra and I. Saha, “Artificial neural networks in hardware: a survey of two decades of progress,” Neurocomputing 74, 239–255 (2010).
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A. Peruzzo, M. Lobino, J. C. F. Matthews, N. Matsuda, A. Politi, K. Poulios, X.-Q. Zhou, Y. Lahini, N. Ismail, K. Wörhoff, Y. Bromberg, Y. Silberberg, M. G. Thompson, and J. L. O’Brien, “Quantum walks of correlated photons,” Science 329, 1500–1503 (2010).
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K. Nozaki, T. Tanabe, A. Shinya, S. Matsuo, T. Sato, H. Taniyama, and M. Notomi, “Sub-femtojoule all-optical switching using a photonic-crystal nanocavity,” Nat. Photonics 4, 477–483 (2010).
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2009 (1)

P. Rebentrost, M. Mohseni, I. Kassal, S. Lloyd, and A. Aspuru-Guzik, “Environment-assisted quantum transport,” New J. Phys. 11, 033003 (2009).
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2007 (2)

P. Kok, W. J. Munro, K. Nemoto, T. C. Ralph, J. P. Dowling, and G. J. Milburn, “Linear optical quantum computing with photonic qubits,” Rev. Mod. Phys. 79, 135–174 (2007).
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2003 (1)

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2002 (1)

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2001 (1)

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2000 (1)

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Figures (7)

Fig. 1.
Fig. 1. (a), (b) Universal unitary networks composed of MZIs; (a) shows the “Reck” encoding and (b) shows the “Clements” encoding. Inset shows the unit cell of a PNP, a programmable MZI. (c) Universal linear network composed of two universal unitary circuits and an additional column of “loss” MZIs originally described by Miller [16].
Fig. 2.
Fig. 2. (a) Optical micrograph of 26-mode, 88-MZI PNP [1]. PCBs are visible above and below the chip. (b) Artistic rendering of a U ( 4 ) PNP by Annoni et al. [26]. (c) Germanium-doped glass six-mode, 15-MZI PNP by Carolan et al. [15]. (d) Four-mode, six-MZI PNP by Ribeiro et al. [27] implemented in the SOI platform.
Fig. 3.
Fig. 3. (a) Phase shifter addressing scheme. (b) Poincaré sphere showing the space of transformations enabled between the top “ t ” and bottom “ b ” waveguide modes. Without an external phase shifter, transformations are confined to the blue plane; with an external phase shifter, transformations span the sphere. (c) Programming model for programmable nanophotonic processors. After each round of programming, the results of the measurement step can be used to correct the program.
Fig. 4.
Fig. 4. (a) Schematic of system for coherent summing of light from N input spatial modes. (b) Schematic drawing of a U ( 4 ) , Reck-topology PNP with a four-input, four-output multimode interferometer tied to the input waveguides. Active MZIs in this experiment are highlighted blue. (c) To implement dynamic mode mixing, 980 nm light is focused on the multimode interferometer. Eye diagram for signal passing through the mixer (c.i) without the perturbing laser, (c.ii) with the perturbing laser and automatic calibration disabled, and (c.iii) with the perturbing laser and automatic calibration enabled.
Fig. 5.
Fig. 5. (a) Schematic representation of the 26-mode PNP along with the coordinate system definition for quantum transport experiments. (b) Conceptual drawing of the phase landscape for a strong, statically disordered system where light is localized initially to waveguide i 14 . By introducing dynamic phase disorder (shown as red vibrations), it is possible to optimize transport of light to distant waveguide sites.
Fig. 6.
Fig. 6. Linear optical quantum logic gates in a PNP [15]. (a) Heralded controlled-NOT gate schematic. (b) Program within the U ( 6 ) PNP. (c) Computational truth table, with theoretical result overlaid. Correspondence between MZI reflectivities and colored beam splitters in (b) shown at right.
Fig. 7.
Fig. 7. (a) Optical neural network architecture overview. (b) Zoom into a single layer of the neural network. The optical interference unit can be realized, as shown in Fig. 1(b).

Tables (1)

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Table 1. Example Matrices That Can Be Generated by a Single MZI with an Internal Phase Shifter, an External Phase Shifter ϕ , and Control over the Input Phase Difference γ a

Equations (1)

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U ( 2 ) = ( e i ϕ sin ( θ / 2 ) e i ϕ cos ( θ / 2 ) cos ( θ / 2 ) sin ( θ / 2 ) ) ,

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