CQN is developing the entire technology stack to reliably carry quantum data across the globe, serving diverse applications across many user groups simultaneously... spurring new technology industries and a competitive marketplace of quantum service providers and application developers.
Join us for a World Quantum Day panel organized jointly by the Perimeter Institute and Quantum Ethics Project and sponsored by CQN. Our discussion will feature Raymond LaFlamme (Institute for Quantum Computing), Zeki Seskir (Karlsruhe Institute of Technology (KIT)) Jean Olemou (Leap Quantik) Taqi Raza (Center for Quantum Networks) and Joan Arrow (Quantum Ethics Project, Center for Quantum Networks). The panel will discuss how […]
Narayanan Rengaswamy is a an assistant professor in the Electrical and Computer Engineering program at the University of Arizona.He also works at the NSF Engineering Research Center for Quantum Networks (CQN) in the university. He discusses his research focuses on quantum error correction and fault tolerance.
Optica Quantum is a new online-only journal dedicated to high-impact results in quantum information science and technology (QIST), as enabled by optics and photonics. Optica Quantum will publish its first issue in September 2023. Its scope will encompass theoretical and experimental research as well as technological advances in and applications of quantum optics. In addition, the Journal will […]
We are pleased to announce the appointment of Julie Des Jardins as the new Director for Diversity and Culture of Inclusion (DCI) within CQN. Dr. Des Jardins is a cultural historian, educator, and DEI practitioner who examines gender, race, and intersectional identity in American culture, particularly in academia, athletics, politics, and STEM. She has also […]
A short video highlighting CQN’s work in building the quantum Internet was featured at the American Physical Society (APS) meeting in Las Vegas in March 2023. The six-minute video features laboratory footage from multiple CQN campuses and interviews with director Saikat Guha, as well as investigators Linran Fan, Dirk Englund, Jane Bambauer, Don Towsley, and […]
All nine courses can be found on our YouTube channel in the CQN Winter School for Quantum Networks playlist. Slides associated with the courses can be found here.
Leandros Tassiulas has won one of thirteen JP Morgan Chase Faculty Research Awards for his work on artificial intelligence. The awards aim to “empower the best research thinkers across AI today” in order to “advance cutting-edge AI research to solve real-world problems.” Leandros is one of CQN’s primary investigators at Yale, which is one of […]
The team behind the University of Arizona’s $99-million Grand Challenges Research Building (GCRB) is wrapping up a seven-level lab structure. The new building will house around a half-dozen different cutting-edge functions and programs in an extremely tight footprint within just 2.5 years. Among other operations, GCRB will serve as the new headquarters of the Center […]
Check out additional stories and events from the Center for Quantum Networks.
Federated learning (FL) algorithms usually sample a fraction of clients in
each round (partial participation) when the number of participants is large and
the server’s communication bandwidth is limited. Recent works on the
convergence analysis of FL have focused on unbiased client sampling, e.g.,
sampling uniformly at random, which suffers from slow wall-clock time for
convergence due to high degrees of system heterogeneity and statistical
heterogeneity. This paper aims to design an adaptive client sampling algorithm
for FL over wireless networks that tackles both system and statistical
heterogeneity to minimize the wall-clock convergence time. We obtain a new
tractable convergence bound for FL algorithms with arbitrary client sampling
probability. Based on the bound, we analytically establish the relationship
between the total learning time and sampling probability with an adaptive
bandwidth allocation scheme, which results in a non-convex optimization
problem. We design an efficient algorithm for learning the unknown parameters
in the convergence bound and develop a low-complexity algorithm to
approximately solve the non-convex problem. Our solution reveals the impact of
system and statistical heterogeneity parameters on the optimal client sampling
design. Moreover, our solution shows that as the number of sampled clients
increases, the total convergence time first decreases and then increases
because a larger sampling number reduces the number of rounds for convergence
but results in a longer expected time per-round due to limited wireless
bandwidth. Experimental results from both hardware prototype and simulation
demonstrate that our proposed sampling scheme significantly reduces the
convergence time compared to several baseline sampling schemes.
Recent advancements in thin-film lithium niobate (TFLN) photonics have led to
a new generation of high-performance electro-optic devices, including
modulators, frequency combs, and microwave-to-optical transducers. However, the
broader adoption of TFLN-based devices that rely on all-optical nonlinearities
have been limited by the sensitivity of quasi-phase matching (QPM), realized
via ferroelectric poling, to fabrication tolerances. Here, we propose a
scalable fabrication process aimed at improving the wavelength-accuracy of
optical frequency mixers in TFLN. In contrast to the conventional
pole-before-etch approach, we first define the waveguide in TFLN and then
perform ferroelectric poling. This sequence allows for precise metrology before
and after waveguide definition to fully capture the geometry imperfections.
Systematic errors can also be calibrated by measuring a subset of devices to
fine-tune the QPM design for remaining devices on the wafer. Using this method,
we fabricated a large number of second harmonic generation devices aimed at
generating 737 nm light, with 73% operating within 5 nm of the target
wavelength. Furthermore, we also demonstrate thermo-optic tuning and trimming
of the devices via cladding deposition, with the former bringing ~96% of tested
devices to the target wavelength. Our technique enables the rapid growth of
integrated quantum frequency converters, photon pair sources, and optical
parametric amplifiers, thus facilitating the integration of TFLN-based
nonlinear frequency mixers into more complex and functional photonic systems.
The standard approach to universal fault-tolerant quantum computing is to
develop a general purpose quantum error correction mechanism that can implement
a universal set of logical gates fault-tolerantly. Given such a scheme, any
quantum algorithm can be realized fault-tolerantly by composing the relevant
logical gates from this set. However, we know that quantum computers provide a
significant quantum advantage only for specific quantum algorithms. Hence, a
universal quantum computer can likely gain from compiling such specific
algorithms using tailored quantum error correction schemes. In this work, we
take the first steps towards such algorithm-tailored quantum fault-tolerance.
We consider Trotter circuits in quantum simulation, which is an important
application of quantum computing. We develop a solve-and-stitch algorithm to
systematically synthesize physical realizations of Clifford Trotter circuits on
the well-known $[![ n,n-2,2 ]!]$ error-detecting code family. Our analysis
shows that this family implements Trotter circuits with optimal depth, thereby
serving as an illuminating example of tailored quantum error correction. We
achieve fault-tolerance for these circuits using flag gadgets, which add
minimal overhead. The solve-and-stitch algorithm has the potential to scale
beyond this specific example and hence provide a principled approach to
tailored fault-tolerance in quantum computing.
A quantum transducer converts an input signal to an output at a different
frequency, while maintaining the quantum information with high fidelity. When
operating between the microwave and optical frequencies, it is crucial for
quantum networking between quantum computers via low-loss optical links, and
thereby enabling distributed quantum computing. However, the state-of-the-art
quantum transducers suffer from low transduction efficiency due to weak
nonlinear coupling, wherein increasing pump power to enhance efficiency leads
to inevitable thermal noise from heating. Moreover, we reveal that the
efficiency-bandwidth product in such systems is fundamentally limited by pump
power and nonlinear coupling coefficient, irrespective of cavity engineering
efforts. To resolve the conundrum, we propose to boost the transduction
efficiency by consuming entanglement within the same frequency band (e.g.,
microwave-microwave or optical-optical entanglement). Via a
squeezer-coupler-antisqueezer sandwich structure, the protocol enhances the
transduction efficiency to unity in the ideal lossless case, given an
arbitrarily weak nonlinear coupling, which establishes a high-fidelity quantum
communication link without any signal encoding. In practical cavity systems,
our entanglement-assisted protocol surpasses the non-assisted fundamental limit
of the efficiency-bandwidth product and reduces the threshold cooperativity for
positive quantum capacity by a factor proportional to two-mode squeezing gain.
Given a fixed cooperativity, our approach increases the broadband quantum
capacity by orders of magnitude.
See all the latest pre-published papers from our team of collaborators.
