Past Seminars

In a seminal talk [Kit02], Kitaev presented the lower bound analysis for the task of two-party strong coin flipping using a magical semidefinite program (SDP) which established a non-zero bias on its best case security. Following this, much of the security analysis in modern day quantum cryptography relies on SDP formulations. In our work, we introduce and use the novel framework of stochastic semidefinite programming (sSDP) to develop first quantum protocols for tasks such as Rabin oblivious transfer. We also demonstrate the use of sSDP to combine insecure protocols resulting into one with a decent security.

Akshay Bansal is a final year Ph.D. candidate in Computer Science at Virginia Tech advised by Jamie Sikora, with a research focus on quantum algorithms, learning theory, and convex optimization. He holds a bachelors in Chemical Engineering from IIT Kanpur and masters in Computer Science from ISI Kolkata. He has previously worked at the Center of Quantum Technologies in Singapore, and held research position at IISc Bengaluru. His current work delves into the mathematical foundations of quantum cryptography, classical and quantum machine learning, and convex analysis.

The vacuum beam guide (VBG) presents a completely different solution for quantum communication channels to overcome the limitations of existing fiber and satellite technologies for long-distance quantum communication. With an array of aligned lenses spaced kilometers apart, the VBG offers ultra-high transparency over a wide range of optical bandwidth. Despite realistic imperfections, the VBG can outperform the best fiber by three orders of magnitude in terms of attenuation rate. The VBG can achieve an ultra-high quantum channel capacity beyond 10^13 qubit/sec over a continental scale, which is several orders of magnitude higher than the state-of-the-art quantum satellite communication rate. Remarkably, without relying on quantum repeaters, the VBG can provide a ground-based, low-loss, high-bandwidth quantum communication channel that enables novel distributed quantum information applications for computing, communication, and sensing.

Yesun is a graduate student from Prof. Liang Jiang's group, who obtained his bachelor’s degree in photoelectric Information Science from USTC in 2022, before which he studied quantum nonlinear optics. He now focuses on the systematic study of large-scale quantum distributed systems.

Quantum networks facilitate numerous applications including secure communication and distributed quantum computation by performing entanglement distribution. For some multi-user quantum applications access to a shared multipartite state is required. We consider the problem of designing protocols for distributing such states, at an increased rate. For this, we propose three protocols that leverage multipath routing to increase the distribution rate for multi-user applications. In this talk, we introduce and discuss results on this area and show how multipath routing can be applied to networks with NISQ era constraints such as networks with a limited number of quantum memories and finite decoherence times.

Evan Sutcliffe is a 3rd year PhD student at University College London. Based in the Optical Networks Group, his research interests are entanglement distribution and routing in quantum networks.

Quantifying entanglement is an important task by which the resourcefulness of a quantum state can be measured. Here we develop a quantum algorithm that tests for and quantifies the separability of a general bipartite state, by making use of the quantum steering effect, the latter originally discovered by Schrödinger. Our separability test consists of a distributed quantum computation involving two parties: a computationally limited client, who prepares a purification of the state of interest, and a computationally unbounded server, who tries to steer the reduced systems to a probabilistic ensemble of pure product states. To design a practical algorithm, we replace the role of the server by a combination of parameterized unitary circuits and classical optimization techniques to perform the necessary computation. The result is a variational quantum steering algorithm (VQSA), which is a modified separability test that is better suited for the capabilities of quantum computers available today. We then simulate our VQSA on noisy quantum simulators and find favorable convergence properties on the examples tested. We also develop semidefinite programs, executable on classical computers, that benchmark the results obtained from our VQSA. Our findings here thus provide a meaningful connection between steering, entanglement, quantum algorithms, and quantum computational complexity theory. They also demonstrate the value of a parameterized mid-circuit measurement in a VQSA and represent a first-of-its-kind application for a distributed VQA.

A PhD candidate at Cornell University working with Dr. Mark M. Wilde

In this talk I will discuss some practical multiplexing-based policies for long-distance entanglement distribution in quantum networks. We propose two paradigmatic policies namely farthest neighbour swap-asap and strongest neighbour swap-asap, adapting swap as soon as policies for multiplexing based networks. We have benchmarked these policies against their non-multiplexed analogue which is a parallel swap-asap policy and a random-ranking multiplexed policy. We show that multiplexed policies yield a considerable advantage both in terms of reducing the average waiting time for end-to-end connectivity and increasing the fidelity of the end-to-end link. Further, an important consideration for practical and scalable implementation of entanglement distribution policies are their classical communication (CC) costs. Here we have considered these overheads and shown than quasi-local multiplexed policies ---using some but not all global knowledge of the network state--- do retain their advantage over non-multiplexed/random versions and completely local policies when CC costs are included. We also show the interplay of this advantage with the increasing size of the network (number of nodes), and extent of global knowledge utilized by the policy. Our results show that utilizing knowledge of the network state can enhance network outcomes even when the CC costs associated with such knowledge are accounted for. This is a very important conclusion from the point of view determining useful policies beyond the fully-local ones like swap-asap, especially for large quantum networks and achieving reasonable figures of merit for near-term and current quantum network realizations. To make this more quantitative, we give network outcomes for two concrete memory platforms namely rare-earth ion and diamond-vacancy based quantum memories, using multiplexing based policies.

I am a graduate student in the Department of Physics and Astronomy at Louisiana State University working on theory and simulation of terrestrial and space-based quantum networks. My other research interests include critical phenomena and dynamics of many-body quantum systems, quantum optics and its interplay with gravity, and interferometry utilizing non-classical states of light.

Quantum networks have the potential to completely transform current computation, communication, and sensing technologies by enabling the exchange of quantum data across vast distances. However, considerable hardware advances are needed before a practical quantum internet becomes a reality. Currently, one of the most significant challenges is effectively distributing entanglement among spatially separated network nodes. In this talk, I will present our work on finding improved protocols for entanglement distribution in a linear chain of nodes that take practical limitations into account. We use Reinforcement Learning approach to discover new protocols that offer improvement in terms of waiting time and fidelity of end-to-end entanglement. We believe that this improvement is the result of collaboration between the network nodes and provide quantifiers for it. Finally, I will present our approach of nested protocols to handle computationally costly long repeater chains.

Pratik is a PhD student in Quantum Science & Technology group at Louisiana State University. He completed his undergraduate and Master's degree in India and joined LSU for his PhD in 2018. Pratik is advised by Dr. Hwang Lee and mainly works in theoretical quantum optics and quantum computing.

Quantum teleportation, as a key protocol for quantum communication and quantum computing, demonstrates the stark difference between quantum and classical information transmission. An ideal teleportation protocol requires a pure maximally entangled state as the teleportation channel, while in real implementations the shared entanglement is severely degraded due to decoherence. In this talk, I will demonstrate how the utilization of weak measurement and environment-assisted measurement can enhance the performance of teleportation in the presence of noise to a considerable extent.

Sajede Harraz is a postdoctoral fellow at the University of Science and Technology of China (USTC). She received her Ph.D. in quantum system control from USTC in 2018. Prior to that, she received her M.Sc. degree in network security from Sharif University of Technology. Her current research interests include quantum state estimation, quantum state protection, and quantum communications through noisy channels.

A quantum internet aims at harnessing networked quantum technologies, namely by distributing bipartite entanglement between distant nodes. However, multipartite entanglement between the nodes may empower the quantum internet for additional or better applications for communications, sensing, and computation. In this work, we present an algorithm for generating multipartite entanglement between different nodes of a quantum network with noisy quantum repeaters and imperfect quantum memories, where the links are entangled pairs. Our algorithm is optimal for GHZ states with 3 qubits, maximising simultaneously the final state fidelity and the rate of entanglement distribution. Furthermore, we determine the conditions yielding this simultaneous optimality for GHZ states with a higher number of qubits, and for other types of multipartite entanglement. Our algorithm is general also in the sense that it can optimise simultaneously arbitrary parameters. In this talk I'll also go through some extensions of this work, in particular for the case where the parameters are described by a continuous function, correspondent to a trade-off model between fidelity and rate.

Luis Bugalho is a Portuguese physicist currently doing research as a PhD student between CeFEMA in Lisbon and LIP6 in Paris. In the past years he has studied some aspects of distributing entanglement over quantum networks. His current area of research is related with quantum sensor networks, which harness the power of distributed quantum sensing.