After several decades of intensive theoretical and experimental efforts, the field of quantum information processing is at a critical moment: special-purpose quantum information processors are at or past the “quantum complexity frontier” where classical computers can no longer predict their outputs: we can “program complexity”, unable to predict the outcome. Meanwhile, new technologies to connect quantum processors by photons give rise to quantum networks with functions impossible on today’s “classical-physics” internet. 
But to harness the power of quantum complexity in “noisy intermediate-scale” quantum computers and networks, we need new methods to control and understand them — and perhaps to manage noise sufficiently to reach fault tolerance. This talk discusses one approach: large-scale programmable photonic integrated circuits[1] (PICs) designed to control photons and atomic or atom-like quantum memories[2–7]. The second part of the talk considers another “complexity frontier” requiring large-scale control: that encountered in machine learning and signal processing. These problems present new opportunities at the intersection with quantum information technologies — specifically, we will consider new directions for processing classical and quantum information in deep learning neural networks architectures, with a particular focus on hardware error correction[8–11].

Figure: A programmable photonic integrated circuit (center) for machine learning acceleration (left) or quantum repeater networks (right).

Bio
Dirk Englund received his BS in Physics from Caltech in 2002. After a Fulbright fellowship at T.U. Eindhoven, he completed an MS in Electrical Engineering and a PhD in Applied Physics at Stanford University in 2008. After a postdoctoral fellowship at Harvard University, he joined Columbia University as Assistant Professor of E.E. and of Applied Physics. He joined the MIT EECS faculty in 2013. Major recognitions include the 2011 Presidential Early Career Award in Science and Engineering, the 2011 Sloan Fellowship in Physics, the OSA’s 2017 Adolph Lomb Medal, the Bose Research Fellowship in 2018, and a Humboldt Research Fellowship in 2020.
References
[1]    W. Bogaerts, D. Pérez, J. Capmany, D. A. B. Miller, J. Poon, D. Englund, F. Morichetti, and A. Melloni, Programmable Photonic Circuits, Nature 586, 207 (2020).
[2]    N. H. Wan, T.-J. Lu, K. C. Chen, M. P. Walsh, M. E. Trusheim, L. De Santis, E. A. Bersin, I. B. Harris, S. L. Mouradian, I. R. Christen, E. S. Bielejec, and D. Englund, Large-Scale Integration of Artificial Atoms in Hybrid Photonic Circuits, Nature 583, 226 (2020).
[3]    J. Carolan, M. Mohseni, J. P. Olson, M. Prabhu, C. Chen, D. Bunandar, M. Y. Niu, N. C. Harris, F. N. C. Wong, M. Hochberg, S. Lloyd, and D. Englund, Variational Quantum Unsampling on a Quantum Photonic Processor, Nat. Phys. 16, 322 (2020).
[4]    T. Neuman, M. Eichenfield, M. Trusheim, L. Hackett, P. Narang, and D. Englund, A Phononic Bus for Coherent Interfaces Between a Superconducting Quantum Processor, Spin Memory, and Photonic Quantum Networks, http://arxiv.org/abs/2003.08383.
[5]    M. E. Trusheim, B. Pingault, N. H. Wan, M. Gündoğan, L. De Santis, R. Debroux, D. Gangloff, C. Purser, K. C. Chen, M. Walsh, J. J. Rose, J. N. Becker, B. Lienhard, E. Bersin, I. Paradeisanos, G. Wang, D. Lyzwa, A. R.-P. Montblanch, G. Malladi, H. Bakhru, A. C. Ferrari, I. A. Walmsley, M. Atatüre, and D. Englund, Transform-Limited Photons From a Coherent Tin-Vacancy Spin in Diamond, Phys. Rev. Lett. 124, 023602 (2020).
[6]    R. Debroux, C. P. Michaels, C. M. Purser, N. Wan, M. E. Trusheim, J. A. Martínez, R. A. Parker, A. M. Stramma, K. C. Chen, L. de Santis, E. M. Alexeev, A. C. Ferrari, D. Englund, D. A. Gangloff, and M. Atatüre, Quantum Control of the Tin-Vacancy Spin Qubit in Diamond, http://arxiv.org/abs/2106.00723.
[7]    V. Saggio, B. E. Asenbeck, A. Hamann, T. Strömberg, P. Schiansky, V. Dunjko, N. Friis, N. C. Harris, M. Hochberg, D. Englund, S. Wölk, H. J. Briegel, and P. Walther, Experimental Quantum Speed-up in Reinforcement Learning Agents, Nature.
[8]    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 (2017).
[9]    R. Hamerly, L. Bernstein, A. Sludds, M. Soljačić, and D. Englund, Large-Scale Optical Neural Networks Based on Photoelectric Multiplication, Phys. Rev. X 9, 021032 (2019).
[10]    G. Wetzstein, A. Ozcan, S. Gigan, S. Fan, D. Englund, M. Soljačić, C. Denz, D. A. B. Miller, and D. Psaltis, Inference in Artificial Intelligence with Deep Optics and Photonics, Nature 588, 39 (2020).
[11]    R. Hamerly, S. Bandyopadhyay, and D. Englund, Accurate Self-Configuration of Rectangular Multiport Interferometers, http://arxiv.org/abs/2106.03249.