UW QuantumX

Maxwell Parsons (University of Washington), QISE Seminar: Engineering Qubit Control for Scalable Quantum Systems
2026-03-06 13:30 - 14:30 (America/Los_Angeles) | Electrical and Computer Engineering (ECE), Room 037, 185 W Stevens Wy NE, Seattke, Washington, 98185
Abstract

Quantum computing is advancing along two primary scaling paradigms: distributed quantum systems connected through entanglement networks, and increasingly large individual quantum processors. Both approaches require not only long-lived qubits, but control architectures deliberately engineered to support error correction at scale. In my laboratory, we investigate these paradigms through complementary experimental platforms: color-center quantum memories for networked architectures and reconfigurable neutral-atom arrays for large-scale processors.

In color-center systems, an optically-addressable central electronic spin coherently couples to nearby nuclear spins to form a modular quantum memory with a photonic interface, suitable for quantum networking. Here, dominant limitations arise from structured environmental spin-noise and the common fluctuator associated with optical transitions of the electronic state. We are developing control strategies tailored to this noise environment, engineering microwave and optical protocols that stabilize multi-spin registers and extend usable memory lifetimes in a manner compatible with networked error-correction schemes.

In neutral-atom systems, we explore opportunities enabled by three-dimensional qubit geometries uniquely accessible in optically trapped atom arrays. Three-dimensional connectivity offers architectural advantages for efficient error correction, but imposes stringent requirements on local optical control and crosstalk suppression. At the same time, three-dimensional geometries can enable scaling in physical qubit number due to the re-use of optical power across layers of qubits for trapping and gate control. We are co-designing 3D neutral-atom architectures and scalable optical control hardware to match qubit geometry to fault-tolerant operation and are establishing a dedicated testbed for developing and characterizing these strategies.

Across both efforts, the central theme is control–architecture co-design: engineering qubit control systems that are intentionally matched to geometry, noise environment, and error-correction strategy.

Bio

Max Parsons is an Assistant Professor in the Department of Electrical & Computer Engineering. His research focuses on advancing quantum hardware for computing, sensing, and communication by developing scalable control of neutral atoms and solid-state quantum systems. At UW, he leads efforts in optical control of qubits and experimental testbeds for neutral atom quantum processors and spin-defect quantum memories. Parsons completed his PhD in Physics at Harvard University in 2016, where he pioneered techniques for laser cooling and atom-resolved imaging of fermionic atoms for quantum simulation. Prior to joining UW in 2022 to develop the QT3 lab, he worked in industry on mixed-reality displays at Meta’s Reality Labs and on neutral-atom quantum computing hardware at Atom Computing. He is an inventor on more than 35 patents in quantum computing and mixed-reality technologies.

UW ECE Research Colloquium Lecture Series: Shuhan Liu, Stanford University
2026-03-05 10:30 - 11:30 | Electrical and Computer Engineering (ECE), Room 037, 185 W Stevens Wy NE, Seattke, Washington, 98185
Event interval: Single day eventCampus location: Electrical and Computer Engineering Building (ECE)Campus room: ECE 037Accessibility Contact: events@ece.uw.eduEvent Types: Lectures/SeminarsLink: https://www.ece.uw.edu/colloquia/middas-memory-integration-and-data-dis-aggregation/
MIDDAS: Memory Integration and Data Dis-Aggregation
Abstract
Since the invention of the integrated circuit in 1958, the integration of exponentially more devices onto a single chip has transformed computing—yet memory remains largely separated from logic, resulting in a “memory wall”. Recent advances in memory research have introduced a variety of new memory technologies. My research focus, Memory Integration and Data Dis-Aggregation (MIDDAS), envisions a future where massive, diverse memories are physically integrated yet functionally store disaggregated data. MIDDAS encompasses a continuous spectrum of memory characteristics. This is exemplified by BRIDGE (Blended Retention-Indexed Diverse Gain cEll), a gain cell memory platform developed in my PhD research. The 2-transistor (2T) gain cell memory offers high density and CMOS integration compatibility. By introducing oxide semiconductor (OS) transistors with ultra-low leakage current (< 1e-17 A/μm), BRIDGE expands the design space to support retention times spanning microseconds to seconds. BRIDGE is demonstrated on fabricated N40 CMOS+X monolithic 3D integration chip with Atomic-Layer-Deposited (ALD) Indium Tin Oxide (ITO) FET. Hybrid gain cell (OS-Si) demonstrates 3x density and lower energy compared to high-density (HD) SRAM, scalable to N5 and beyond. Furthermore, integrating gain cells with non-volatile memories (e.g., RRAM) unlocks synergistic system-level benefits from device-circuit-architecture co-design, embodying the “1+1>2” philosophy where diverse memory technologies collaboratively enhance system functionality through integration. MIDDAS repositions memory as a scalable, intelligent toolbox for AI-era computing, capitalizing on the predictability of memory access, bridging device innovation with software demands.
Biography
Shuhan Liu is a PhD candidate at Stanford University, advised by H.-S. Philip Wong. She earned B.S. degree from Peking University in 2020. She received 2024 IEEE EDS PhD Fellowship and 2024 IEEE IEDM Best Student Paper Award.

UW ECE Research Colloquium Lecture Series: Yanjie Shao, Massachusetts Institute of Technology
2026-02-26 10:30 - 11:30 | Electrical and Computer Engineering (ECE), Room 037, 185 W Stevens Wy NE, Seattke, Washington, 98185
Campus location: Electrical and Computer Engineering Building (ECE)Campus room: ECE 037Accessibility Contact: events@ece.uw.edu
Event Link
Ultra-Scaled Energy-Efficient Electronics
Abstract
The explosive growth of data-centric computing in the era of artificial intelligence has made energy efficiency a central challenge for modern microelectronics. Two fundamental limitations now dominate: (1) the “power wall”, where stalled voltage scaling in advanced complementary metal–oxide–semiconductor (CMOS) technologies has largely limited further reductions in transistor switching energy, and (2) the “memory wall”, where data movement between computing and memory units increasingly dominates energy cost and restricts information throughput. In this talk, I will present two complementary approaches, leveraging new material systems and nanoscale processing to enable unprecedented device operating regimes and scalable 3D integration paradigms beyond conventional CMOS scaling. First, I target a supply voltage ≤ 0.3 V by exploiting quantum-mechanical tunneling in a broken-band heterojunction semiconductor system (GaSb/InAs). I will show that a combination of sub-thermionic turn-on, high drive current and ultimate device scalability can be achieved simultaneously in a vertical-nanowire tunneling transistor configuration. Second, I will describe a low-thermal-budget (≤ 400 °C) electronic technology integration platform enabled by amorphous oxide semiconductors (AOS) and ferroelectric (FE) hafnium-zirconium oxide. By exploiting plasma-enhanced atomic-layer deposition, enhancement-mode AOS transistors with record logic performance are realized, and nanoscale FE memory transistors comprising a single domain are demonstrated. Together, these results highlight how emerging materials can drivehigh-performance and multifunctional devices that unlock new pathways for future energy-efficient 3D electronics.
Biography
Yanjie Shao is currently a postdoctoral researcher at the Massachusetts Institute of Technology (MIT) in the Microsystems Technology Laboratories (MTL), working with Prof. Jesús del Alamo and Prof. Dimitri Antoniadis. He received his Ph.D. (2023) and S.M. (2021) in Electrical Engineering from MIT, advised by Prof. Jesús del Alamo, and his B.S. in Physics from the University of Science and Technology of China (USTC). His research focuses on addressing fundamental materials, device, and integration challenges to advance energy-efficient semiconductor and microelectronics technologies. He is a recipient of the 2023 Intel Outstanding Researcher Award.

Daniel Higginbottom (Simon Fraser University), QISE Seminar: Engineering silicon colour centres for quantum networks
2026-02-20 13:30 - 14:30 | Electrical and Computer Engineering (ECE), Room 037, 185 W Stevens Wy NE, Seattke, Washington, 98185
Abstract

The performance of quantum networks for long-distance communication, sensing, and distributed quantum computing will be contingent upon the quality of their light-matter interconnects. For networks at scale, these interconnects should be manufacturable and deployable. Solid-state colour centres are single-photon emitters which may offer optically-coupled spin qubit registers for deployable entanglement distribution networks. Of the potential semiconductor hosts, silicon is an ideal platform for commercial quantum technologies. It is a “semiconductor vacuum” with record-setting spin qubit performance, and silicon nanofabrication is an advanced industrial process and the backbone of the microelectronics industry. Although they were neglected until quite recently, silicon colour centres are now established as a quantum platform with technological appeal: they emit in or near the optical telecommunications bands, host intrinsic spin qubit registers, and integrate directly with photonic and electronic circuits on chip. In this talk I will discuss progress towards networked silicon colour centre devices and identify emerging candidates from the rapidly expanding alphabet of silicon colour centres. In particular, I will summarize recent results with the T centre, a CCH defect in silicon. A surprising isotope-dependent lifetime effect suggests that the T centre can be made almost perfectly efficient by isotopic substitution. Cavity-integrated T centres show dramatic Purcell enhancements, enabling faster and more coherent emission, and indistinguishable emission is employed to entangle T centres on separate chips, six meters apart. Determining the hyperfine tensors of the T centre’s intrinsic spin qubits reveals unusual schemes for protecting spin coherence during entanglement attempts. Finally, a new class of opto-electronic devices combining single emitters, optical resonators, and diodes enable a host of spin-photon control techniques including electrically-injected single-photon emission, Stark tuning, and electrical spin initialization. These results illustrate how silicon colour centres may be deployed as an on-chip spin-photon quantum processor, and how these processors may be connected over optical fibre in a metropolitan-scale quantum internet.Bio

Dr Daniel Higginbottom is an Assistant Professor in the Simon Fraser University Department of Physics and a Director at the quantum technology company Photonic Inc. His research has spanned quantum information with platforms including integrated photonics, optically trapped atoms, electrically trapped ions, and silicon spin qubits, for which he received a Banting Research Fellowship. His achievements include benchmark results with single photon sources and optical quantum memories. Recently, he has pioneered the device integration of silicon colour centres, most notably the T centre, for quantum technologies. The primary goal of his research is developing practical, and scalable, quantum technology platforms.

Michael Beverland (IBM), QISE Seminar: Real-time decoding for fault-tolerant quantum computers
2026-02-13 13:30 - 14:30 | Electrical and Computer Engineering (ECE), Room 037, 185 W Stevens Wy NE, Seattke, Washington, 98185
Abstract: Fault-tolerant quantum computers involve running circuits on quantum hardware that sometimes undergo faults in such a way that the faults can be identified and fixed to ensure the quantum computation runs reliably. To do this, information is protected in a quantum error correcting code, and carefully-designed logical operations are carried out on the protected information, with information about the noise that arises during the entire process being generated in a continuous stream of classical output called the syndrome. Decoding is the task of taking the syndrome data and using it to identify what faults occurred so that they can be fixed. This decoding task is run on a classical computer, and is needed to make the quantum computer work – but it is a very challenging unsolved problem to design a decoding algorithm that performs well enough in practice.Real-time decoding for fault tolerance is a central challenge as we move beyond NISQ. The decoding timescale is set by the QEC cycle time of the hardware, which is microseconds for superconducting platforms. Meeting this constraint likely requires specialized classical hardware such as FPGAs or ASICs, whose high degree of parallelism changes the relative performance of decoding algorithms, for example allowing Gaussian elimination to run in linear parallel time on FPGAs rather than cubic time on CPUs, and therefore motivates hardware-aware redesign rather than direct porting of CPU-based methods.In this talk, I discuss recent progress toward real-time decoding under these constraints, and argue that message-passing decoders, particularly the Relay-BP algorithm, offer a promising route to real-time decoding. Relay-BP improves on the convergence of standard belief propagation while retaining a lightweight, highly parallel structure suitable for FPGA implementation, and significantly outperforms alternative decoders for quantum LDPC codes.Beyond average decoding speed, I address the backlog problem that arises from variable decoding latency. I present conditions on decoder latency distributions under which fast average-case decoding and sufficiently light latency tails allow decoding to keep pace with syndrome generation, ensuring bounded computational slowdown in large-scale fault-tolerant computations.

LinkedIn: Michael Beverland

UW ECE Special Research Lecture: Francois Rivet, University of Bordeaux, France
2026-01-21 16:00 - 17:00 | Washington
Event interval: Single day eventCampus room: EEB 269Accessibility Contact: events@ece.uw.eduEvent Types: Academics,Lectures/SeminarsEvent sponsors: IEEE Solid-State Circuit Society, Seattle Chapter
Let's Connect Intelligences
AbstractWho remembers a world without cell phones, the Internet, and ChatGPT? Radio Frequency Integrated Circuits (RFIC) have enabled democratizing communications with ever-greater data exchanges. At the IMS laboratory, we invent the technology and systems that allow us to increase the communication potential from one generation to the next tenfold: goodbye 4G and soon 5G, we are making 6G with the following generation in our sights. What more can we connect and how? Get ready for the revolution where human and artificial intelligences will communicate in tomorrow'snetworks with integrated circuits we will invent now.
BioFrancois Rivet received his Master's and Ph.D. degrees in 2005 and 2009 from the University of Bordeaux, France. Since June 2010, he has been tenured as an Associate Professor at the Bordeaux Institute of Technology (Bordeaux INP). His research is focused on the design of RFICs in the IMS Laboratory, the University of Bordeaux microelectronics laboratory. In 2014, he founded the "Circuits and Systems" research team. Rivet has publications in top-ranked journals, international and national conferences, and holds 20 patents. He is involved in several Steering and Technical Program Committees of flagship conferences. He was General Chair of the IEEE Radio Frequency Integrated Circuits Symposium (RFIC) in 2025 in San Francisco, USA. He is a member of the Board of Governors of the IEEE Circuits and Systems Society since 2024.

Seminar with scholars from Seoul National University
2026-01-20 15:00 - 16:00 | Bill & Melinda Gates Center for Computer Science & Engineering (CSE2)
Event interval: Single day eventCampus location: Bill & Melinda Gates Center for Computer Science & Engineering (CSE2)Campus room: 371Accessibility Contact: aafrontdesk@uw.eduEvent Types: Lectures/Seminars
We have two great speakers, both PhD candidates in mechanical engineering from Seoul National University!
1. Youngkwon “YK” KimModular Reconfigurability for Auxiliary Attitude Control
AbstractThe evolution of spaceborne structures is increasingly driven by modular design principles that enable adaptable, scalable, and multifunctional operational capabilities. From deployable arrays to robotic servicing platforms, such modularity is redefining how spacecraft adapt to evolving mission requirements. In this work, we investigate how structural metamorphism within multifunctional modular assemblies can be exploited as an auxiliary attitude-control mechanism for spacecraft. We consider a reconfigurable modular chain–spacecraft assembly in which the modular units can fold, deploy, and reorient relative to one another, thereby altering the overall configuration and tuning the spacecraft’s inertia properties through internal momentum exchange.
To evaluate the resulting attitude-control capability, we develop a three-dimensional multibody dynamics simulation framework that models reconfigurable modular assemblies attached to a base spacecraft hub under free-floating conditions. Numerical results show that appropriately designed reconfiguration sequences of the modular units can generate desired counter-translations and rotations of the base hub. We demonstrate that the achievable attitude-control maneuvers are strongly influenced by both the sequence and the combination of modular reconfigurations. Moreover, navigating alternate morphing paths to the same final configuration yields distinct inertia-evolution trajectories, each imparting a unique influence on the spacecraft orientation. We further investigate the influence of inter-module connectivity on the attainable attitude-control range, highlighting the potential of reconfigurable modular structures to serve as an auxiliary attitude-control system and to support extended operational lifetimes for advanced space missions.
Bio :Youngkwon “YK” Kim is a second-year Ph.D. candidate in Mechanical Engineering at Seoul National University, working with Prof. Jinkyu Yang (formerly in the Department of Aeronautics & Astronautics at the University of Washington, 8/2013–9/2022). His research focuses on shape-morphing and transformative structures for space systems and wearable devices. Parts of this work will also be presented at the AIAA (American Institute of Aeronautics and Astronautics) SciTech 2026 Forum. YK is passionate about sharing knowledge and supporting students’ growth: he has written a book on effective learning in university (“How Can We Study Effectively in the University System? – Key Factor: Proactive Questions”) and serves as the head of the department’s largest community (about 640 students) in the Department of Mechanical Engineering at Inha University. He also actively collaborates with international research groups at the University of Washington (USA), BITS Pilani (India), and KAIST (Korea).
2. Myeonggyun JooExpandability of Simple Linkage: Localization and Topology
Abstract: In this talk, I will introduce an asymmetric linkage system that is structurally simple yet exhibits remarkably unique behavior. Inspired by natural mechanisms, this system consists of two slanted bars connected by a rail and a torsional spring. Depending on where the torsional spring is placed—at the top hinge or the bottom hinge—the system can be modeled in two distinct ways.Modeling 1 places the torsional spring at the top hinge and is more straightforward to analyze. Using mathematical techniques, the system matrix can be simplified, enabling eigen analysis that reveals the presence of edge modes. By tuning geometric parameters, this model demonstrates an extremely localized phenomenon known as the Singular Edge Mode, in which only the first unit cell oscillates.Modeling 2 places the torsional spring at the bottom hinge and exhibits a more robust form of edge localization. Due to the system’s inherent asymmetry, this configuration has nontrivial topological characteristics. Regardless of where the system is excited, the edge cell consistently shows accumulated localized behavior, serving as evidence of a Topological Edge Mode.Because of its simplicity, tunability, and the richness of its edge phenomena, this asymmetric linkage system offers strong potential for applications in various mechanical and metamaterial design contexts.
Bio :Myeonggyun Joo is a second-year Ph.D. candidate in Mechanical Engineering at Seoul National University, working under the supervision of Prof. Jinkyu Yang (formerly in the Department of Aeronautics & Astronautics at the University of Washington, 2013–2022). His research focuses on wave dynamics, topological mechanical metamaterials, and the design and fabrication of architected structures. Portions of this work were presented at the PHONONICS 2025 conference. Publications on this topic are currently in preparation in collaboration with international research groups at IIS (India) and CNRS (France). He aims to uncover new physical mechanisms in mechanical systems that enable programmable dynamics and multifunctional responses. He is also deeply interested in cross-disciplinary collaborations that bridge metamaterials with fields such as robotics and aerospace engineering.

UW ECE Research Colloquium Series: Milad Koohi, Texas A&M University
2026-01-08 10:30 - 11:30 | Washington
Event interval: Single day eventCampus room: ECE 037Accessibility Contact: events@ece.uw.eduEvent Types: Academics,Lectures/SeminarsLink: https://www.ece.uw.edu/colloquia/milad-koohi/
Towards Agile Radios for NextG Wireless Communications and Sensing
AbstractAs the demand for higher data capacity persists and wireless technologies advance, current front-end circuitry in communication systems requires transformative changes. Multifunctional materials, such as ferroelectrics and ferromagnetics, are increasingly vital in providing critical solutions for communication, computation, and sensing. Integrating such materials into the development of reconfigurable components promises reduced complexity, smaller size, and high performance for future radios, enabling them to transcend beyond 5th generation (5G) wireless technologies.
In this talk, Dr. Koohi will present his research focusing on ferroelectric-based radio frequency (RF) acoustic wave (AW) devices that facilitate efficient spectrum access for future wireless systems. First, he will describe how the electrostriction phenomenon in thin-film paraelectric barium strontium titanate (Ba(1-x)SrxTiO3) is utilized to develop a framework for building intrinsically reconfigurable AW filter modules. This technology increases the functional density of RF front-ends by combining switching and filtering functionalities onto a single device, remarkably reducing the size and complexity of future radios. Next, he will introduce inhomogeneous piezoelectricity as a new paradigm to overcome the fundamental frequency and bandwidth limitations of traditional piezoelectric RF AW technologies. Dr. Koohi will present the first realization of inhomogeneous piezoelectricity in multilayer ferroelectric heterostructures, providing a fundamentally new approach to synthesize next-generation RF AW devices that are programmable and have the capability to selectively operate across multiple frequency bands. The second part of the talk will explore the ferroelectricity in scandium-doped aluminum nitride (Al(1-x)ScxN) to enable mm-Wave acoustics. He will demonstrate how polarization switching in ferroelectric AlScN allows the realization of mm-Wave acoustic devices with record electromechanical coupling and quality factor values required for the demployment of future 5G+ and 6G radios.
Bio Prof. Milad Koohi received his Ph.D. in Electrical Engineering from the University of Michigan, Ann Arbor, in 2020. Following his doctoral studies, he joined Qorvo Inc. as an R&D Technical Lead at the BAW Research Center in FL, where he led the integration of ferroelectric nitrides into acoustic wave devices for microwave and mm-wave frequencies. In January 2025, he transitioned to academia, joining the Department of Electrical Engineering at Texas A&M University. Prof. Koohi’s research focuses on understanding multiphysical domain interactions, particularly in the electromagnetic, acoustic, and optical domains, within emerging material systems and integrating them into innovative devices, microsystems, and integrated circuits, advancing the frontiers of communication, computation, and sensing technologies. He has received several awards, including the Qorvo Best New Technology Award and the IEEE MTT-S Graduate Fellowship. Dr. Koohi has authored or coauthored more than 40 peer-reviewed publications and patents on ferroelectric nitrides, complex oxides, and their incorporation into novel devices and integrated circuits.

UW ECE Research Colloquium Series: Talia Moore
2025-12-02 14:30 - 15:30 | Washington
Event interval: Single day eventCampus room: ECE 037Accessibility Contact: dso@uw.eduEvent Types: Academics,Lectures/Seminars

The Dean W. Lytle Electrical & Computer Engineering Endowed Lecture Series: Anima Anandkumar
2025-11-18 14:30 - 17:00 | Student Union Building (HUB)
Event interval: Single day eventCampus location: Student Union Building (HUB)Campus room: HUB LyceumAccessibility Contact: dso@uw.eduEvent Types: Academics,Lectures/Seminars
Neural Operators for AI+Science: Pushing the Frontiers of Scientific Discovery
AbstractThe main bottleneck in doing scientific research is the need for physical experiments in many areas. This means risky ideas are often discarded and the hypothesis space is traditionally restricted to regions of prior success. AI is disrupting this status quo by enabling physically-valid digital twins that reduce or even completely remove the need for physical experiments. AI models are orders of magnitude faster than traditional simulations, and often more accurate, since they can directly adapt to experimental and observational data. Since AI models are differentiable, they can be directly used for inverse design, enabling exploration and design optimization subject to diverse constraints, that was not possible before. Neural Operators enable multiscale and physics-informed learning for achieving high fidelity and training data efficiency in many areas. They have been successfully applied in weather and climate modeling, plasma evolution in nuclear fusion, designing novel medical devices and enabling autonomous flights under turbulence.
BiographyAnima Anandkumar has done pioneering work in AI for scientific modeling and discovery, including extreme weather forecasting, drug discovery, scientific simulations, and engineering design. She invented Neural Operators, a deep learning framework for learning multiscale physical phenomena and used it to train the first AI-based high-resolution weather model, tens of thousands of times faster than current forecasting systems, that is running at weather agencies and created the field of AI-based weather and climate modeling. Her AI algorithms have enabled many other scientific advances such as designing a novel medical device, inventing an anti-cancer drug currently in clinical trials, and safer autonomous drone flights.Anima is currently a Bren professor at Caltech and a fellow of the IEEE, ACM, and AAAI. She has received several awards, including the Time 100 Impact Award, IEEE Kiyo Tomiyasu Award, the Schmidt Sciences AI2050 senior fellow, awards from the Guggenheim, Alfred P. Sloan and Blavatnik Foundations, the NSF Career Award, the Distinguished Alumnus Award by the Indian Institute of Technology Madras, and best paper awards at venues such as Neural Information Processing and the ACM Gordon Bell Special Prize for HPC-Based COVID-19 Research. She recently presented her work on AI+Science to the White House Science Council (PCAST), the National AI Advisory Committee, and at TED 2024.Anima received her B. Tech from the Indian Institute of Technology Madras and her Ph.D. from Cornell University and did her postdoctoral research at MIT. She was previously principal scientist at Amazon Web Services and senior director of AI research at NVIDIA.
