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.
On Monday, September 30 2024, the 4th floor of the Grand Challenges Research Building at the University of Arizona, hosted an open house. The goal was to create a community around the quantum research happening at the University of Arizona. In attendance was Ford Burkhart who wrote a lovely piece on the event and work […]
We’re pleased to release the dates for our 2025 Winter School on Quantum Networks. Keep an eye out for the registration to open soon! All courses will be held via Zoom, co-taught by CQN faculty, postdocs and students.
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.
Check out additional stories and events from the Center for Quantum Networks.
Quantum error correction (QEC) is essential for reliable quantum information
processing. Targeting a particular error channel, both the encoding and the
recovery channel can be optimized through a biconvex optimization to give a
high-performance, noise-adapted QEC scheme. We solve the biconvex optimization
by the technique of alternating semi-definite programming and identify a new
four-qubit code for amplitude damping channel, one major noise in
superconducting circuits and a good model for spontaneous emission and energy
dissipation. We also construct analytical encoding and recovery channels that
are close to the numerically optimized ones. We show that the new code notably
outperforms the Leung-Nielsen-Chuang-Yamamoto four-qubit code in terms of the
entanglement fidelity over an amplitude damping channel.
Wireless ray-tracing (RT) is emerging as a key tool for three-dimensional
(3D) wireless channel modeling, driven by advances in graphical rendering.
Current approaches struggle to accurately model beyond 5G (B5G) network
signaling, which often operates at higher frequencies and is more susceptible
to environmental conditions and changes. Existing online learning solutions
require real-time environmental supervision during training, which is both
costly and incompatible with GPU-based processing. In response, we propose a
novel approach that redefines ray trajectory generation as a sequential
decision-making problem, leveraging generative models to jointly learn the
optical, physical, and signal properties within each designated environment.
Our work introduces the Scene-Aware Neural Decision Wireless Channel Raytracing
Hierarchy (SANDWICH), an innovative offline, fully differentiable approach that
can be trained entirely on GPUs. SANDWICH offers superior performance compared
to existing online learning methods, outperforms the baseline by 4e^-2 radian
in RT accuracy, and only fades 0.5 dB away from toplined channel gain
estimation.
We study an architecture for fault-tolerant measurement-based quantum
computation (FT-MBQC) over optically-networked trapped-ion modules. The
architecture is implemented with a finite number of modules and ions per
module, and leverages photonic interactions for generating remote entanglement
between modules and local Coulomb interactions for intra-modular entangling
gates. We focus on generating the topologically protected
Raussendorf-Harrington-Goyal (RHG) lattice cluster state, which is known to be
robust against lattice bond failures and qubit noise, with the modules acting
as lattice sites. To ensure that the remote entanglement generation rates
surpass the bond-failure tolerance threshold of the RHG lattice, we employ
spatial and temporal multiplexing. For realistic system timing parameters, we
estimate the code cycle time of the RHG lattice and the ion resources required
in a bi-layered implementation, where the number of modules matches the number
of sites in two lattice layers, and qubits are reinitialized after measurement.
For large distances between modules, we incorporate quantum repeaters between
sites and analyze the benefits in terms of cumulative resource requirements.
Finally, we derive and analyze a qubit noise-tolerance threshold inequality for
the RHG lattice generation in the proposed architecture that accounts for noise
from various sources. This includes the depolarizing noise arising from the
photonically-mediated remote entanglement generation between modules due to
finite optical detection efficiency, limited visibility, and the presence of
dark clicks, in addition to the noise from imperfect gates and measurements,
and memory decoherence with time. Our work thus underscores the hardware and
channel threshold requirements to realize distributed FT-MBQC in a leading
qubit platform today — trapped ions.
We explore the use of a spatial mode sorter to image a nanomechanical
resonator, with the goal of studying the quantum limits of active imaging and
extending the toolbox for optomechanical force sensing. In our experiment, we
reflect a Gaussian laser beam from a vibrating nanoribbon and pass the
reflected beam through a commercial spatial mode demultiplexer (Cailabs
Proteus). The intensity in each demultiplexed channel depends on the mechanical
mode shapes and encodes information about their displacement amplitudes. As a
concrete demonstration, we monitor the angular displacement of the ribbon’s
fundamental torsion mode by illuminating in the fundamental Hermite-Gauss mode
(HG$_{00}$) and reading out in the HG$_{01}$ mode. We show that this technique
permits readout of the ribbon’s torsional vibration with a precision near the
quantum limit. Our results highlight new opportunities at the interface of
quantum imaging and quantum optomechanics.
See all the latest pre-published papers from our team of collaborators.
