Upcoming Talks

Entanglement is known to boost the efficiency of classical communication. In distributed computation, for instance, exploiting entanglement can reduce the number of communicated bits or increase the probability to obtain a correct answer. Entanglement-assisted classical communication protocols usually consist of two successive rounds: first a Bell test round, in which the parties measure their local shares of the entangled state, and then a communication round, where they exchange classical messages. Here, we go beyond this standard approach and investigate adaptive uses of entanglement: we allow the receiver to wait for the arrival of the sender’s message before measuring his share of the entangled state. We first show that such adaptive protocols improve the success probability in Random Access Codes. Second, we show that once adaptive measurements are used, an entanglement-assisted bit becomes a strictly stronger resource than a qubit in prepare-and-measure scenarios. We discuss the extension of these ideas to scenarios involving quantum communication and identify resource inequalities.

Ph.D. "Spin dynamics in rare-earth ion-doped crystals for optical quantum memories", from the University of Geneva, under the supervision of Dr. Mikael Afzelius and Prof. Nicolas Gisin. Postdocs: quantum correlations (foundational, theory), e.g. Bell nonlocality, contextuality. I am an Assistant Professor in Instituto Superior Técnico, and have recently started a group in Instituto de Telecomunicações. We have a small lab called the Quantum Photonics Laboratory (QuLab), where we work on applications of quantum correlations to quantum communication, and free-space quantum communication.

Advances in the fabrication of large-scale integrated silicon photonics have sparked interest in optical systems that process information at high speeds with ultra-low energy consumption. Recent demonstrations have shown these systems' ability to accelerate tasks in quantum simulation, artificial intelligence, and signal processing. In this talk, I will discuss work towards scaling up these systems to perform useful computation. I will begin by discussing the development of error correction algorithms for programmable photonic processors, whose capabilities are believed to be limited by fabrication error. By applying deterministic, gate-by-gate error correction, I show that these systems, despite relying on imprecise, analog components, can be efficiently programmed to implement highly accurate computation. I will also discuss my work towards realizing low-loss, alignment-tolerant optical interconnects, facilitating the assembly of complex photonic systems with large channel counts. Finally, I will discuss the design and demonstration of a single-chip, end-to-end photonic processor for deep neural networks (DNNs). This fully-integrated coherent optical neural network (FICONN), which monolithically integrates multiple all-optical processor units for matrix algebra and nonlinear activation functions into a single chip, implements single-shot inference across a DNN with sub-nanosecond latency. We experimentally demonstrate on-chip, in situ training of a DNN, obtaining accuracies comparable to a digital system. Our work lends experimental evidence to proposals for optically-accelerated training, enabling orders of magnitude improvements in the throughput of training data. Moreover, the FICONN opens the path to inference at nanosecond latency and femtojoule per operation energy efficiency.

Saumil Bandyopadhyay received his S.B. and M.Eng. in Electrical Engineering from MIT in 2017 and 2018, respectively. He is a recipient of the NSF Graduate Research Fellowship and is currently with the Quantum Photonics Group at MIT, where he works on integrated silicon photonic systems for computing.