TPC26: Toward Scientific AI Platforms at HPC Facilities AI is creating a new set of demands for HPC centers. Researchers are no longer... A year ago, AWS’s Thierry Pellegrino estimated that quantum computing was still four to five years away from broad commercial relevance. Speaking at TPC26 last week, however, he suggested the timeline may be accelerating. “I think 2027 is going to see a lot of advancements,” Pellegrino said, arguing that some quantum computing modalities are making progress toward the logical qubit counts needed to tackle meaningful scientific problems. The prospect of quantum computing becoming useful sooner than expected formed a central theme of Pellegrino’s keynote, which examined how advances in quantum computing, artificial intelligence and high performance computing are reshaping scientific discovery. Pellegrino also argued that researchers increasingly need access to both cloud and on-premises computing resources, depending on the scale, urgency and nature of their workloads. The result, he said, is a more flexible computing environment in which advanced computing capabilities are no longer limited to organizations that can build and operate dedicated supercomputers. “There used to be a time that a lot of us remember where building a supercomputer would take years,” Pellegrino said. Today, he added, researchers can access supercomputing resources “with the click of a button,” enabling a level of flexibility and scale that was previously available only to a handful of large national laboratories. Against that backdrop, Pellegrino outlined four areas where he believes quantum computing is most likely to deliver practical value first: physics and chemistry, cryptography, materials science, and optimization. In physics and chemistry, quantum computers could help researchers simulate molecules and other quantum systems that are difficult to model accurately using conventional computers. Materials science represents a related opportunity, with researchers exploring how quantum systems might be used to engineer
Jun 9, 2026 · via hpcwire.com
Quantum materials are a class of exotic materials with special properties that are governed by quantum mechanics rather than classical physics. Those properties — like superconductivity, entanglement and unusual forms of magnetism — often originate in the tiny repeating patterns of atoms inside crystals, but through clever engineering they can be observed and controlled at a more human scale. Quantum materials are helping to power the quickly growing field of quantum computing, and could find their way into future generations of energy-efficient electronics. Designing new materials from the atomic scale up, however, requires intense modeling and simulation. Some materials may appear ordinary when viewed as small clusters of atoms, yet reveal new and useful properties when their atomic building blocks repeat and interact over larger distances. Researchers must be able to accurately predict behaviors at large scales in order to find materials with practical applications — otherwise designing new materials is a slow and costly trial-and-error process. In the past 50 years, supercomputers have helped materials scientists solve some of those thorny prediction problems, but two recent studies from the University of Washington demonstrate how newer computing techniques can help researchers sniff out promising quantum materials to pursue. The first study, published June 2 in the Proceedings of the National Academy of Sciences, shows how researchers can use artificial intelligence to simulate dozens of sheets of atoms stacked in intricate patterns, a process that produces complex and potentially useful quantum behaviors. The second study, published June 8 in Nature Communications, shows how quantum computers can create a self-improving design loop by discovering new materials that could themselves be components of future quantum computers. “What is exciting is that AI and quantum computing are beginning to change not just what problems we can solve, but how we do research,” said Ting
Jun 9, 2026 · via washington.edu
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Jun 9, 2026 · via youtube.com
Avoid the stress of overpaying for a stock or missing an opportunity by using the right tools and insights to evaluate D-Wave Quantum Inc. before investing. In this article, we go over a few key elements for understanding D-Wave Quantum Inc.’s stock price such as: - D-Wave Quantum Inc.’s current stock price and volume - Why D-Wave Quantum Inc.’s stock price changed recently - Upgrades and downgrades for QBTS from analysts - QBTS’s stock price momentum as measured by its relative strength About D-Wave Quantum Inc. (QBTS) Before we jump into D-Wave Quantum Inc.’s stock price, history, target price and what caused it to recently dip, let’s take a look at some background. D-Wave Quantum Inc. engages in the development and delivery of quantum computing systems, software, and services worldwide. It provides Advantage and Advantage 2 quantum computers; Ocean, a suite of open-source tools; and Leap quantum cloud service, a cloud-based service that provides real-time access to quantum computers and quantum hybrid solvers; and secure access and data protection services, as well as Ocean software development kit (SDK), a Python-based SDK for developers to learn and build applications on company’s server. The company also provides Leap hybrid solver service that offers a combination of quantum and classical computation resources and advanced algorithms to solve problems of enterprise scale; and D-Wave Launch, a phased approach to identify and build in-production quantum hybrid applications, including training sessions and quantum computing access. In addition, the company offers D-Wave Advantage annealing quantum computing systems; and Ocean developer tools. Its quantum solutions are used in allocation, resource scheduling, factory scheduling, vehicle routing, logistics optimization, drug discovery, industrial construction design, portfolio optimization and maintenance, repair, and overhaul optimization. D-Wave Quantum Inc. was founded in 1999 and is based in Palo Alto, California. Want to learn more
Jun 9, 2026 · via aaii.com
After selecting the founders, CEOs, investors, and other change-makers who comprise the 2026 Tech Power Players list, the Globe asked each of them to answer a question: What’s the most promising thing about the Boston tech scene right now?
Their answers ranged from — no surprise — prowess in the exploding field of artificial intelligence, to a robust higher education landscape, to the strength and depth of the talent pool.
Below are what 25 of this year’s honorees had to say about how the region’s innovation community shines.
Some responses have been edited for style, clarity, and brevity.
Dana Gerber can be reached at dana.gerber@globe.com. Follow her @danagerber6.
Jun 9, 2026 · via bostonglobe.com
Off the Wire Press Releases TEL AVIV, Israel, June 9, 2026 — Quantum X Labs Inc., an advanced quantum technologies company, and IQCC, a Quantum Machines company, today announced the signing of a strategic cooperation agreement, under which Quantum X Labs will evaluate its AI-based quantum error-correction technology on Quantum Machines’ quantum control infrastructure. The primary objective is to run Quantum X Labs’ proprietary AI-driven error correction algorithm in a fully integrated hardware-software environment. The collaboration will test Quantum X Labs’ AI-based decoding technology using IQCC’s quantum computing infrastructure, with the goal of exploring its applicability to future quantum error-correction workflows. IQCC will provide access to its quantum control and orchestration infrastructure, including the OPX1000 real-time quantum controller, used by leading quantum research institutions and commercial quantum-computing programs worldwide. IQCC’s systems are designed to support future low-latency feedback and quantum error-correction workflows. “This collaboration represents an important milestone in our roadmap toward validating our AI-based decoder on real quantum-hardware data,” said Prof. Nir Sharon, Chief Quantum Technology Scientist at Quantum X Labs. “By working with IQCC and Quantum Machines, we gain access to a highly respected quantum-computing environment that enables us to evaluate our technology under realistic operating conditions and accelerate its path toward practical deployment.” “Quantum error correction is widely recognized as one of the key challenges on the path to large-scale quantum computing,” said Dr. Nir Alfasi, GM of IQCC. “By providing access to advanced quantum infrastructure, IQCC enables researchers and companies to explore new approaches under realistic conditions. We look forward to working with Quantum X Labs as they assess their AI-based decoder on real quantum hardware.” Quantum X Labs Inc. Quantum X Labs Inc. and its subsidiaries are focused on quantum technology, digital advertising and computing and enterprise artificial intelligence (AI) solutions. Quantum X Labs Ltd.
Jun 9, 2026 · via hpcwire.com
Off the Wire Press Releases June 9, 2026 — Building useful quantum technologies—from sensors to computers—requires generating highly complex entangled states, in which the properties of particles are deeply intertwined. Producing such states has traditionally required complex tools and carefully engineered setups with many parts. Now, researchers at the University of Chicago Pritzker School of Molecular Engineering (UChicago PME) have found a surprisingly simple method to create and control a broad variety of entangled quantum states. Their theoretical approach, described in the journal Physical Review X, begins with experimental tools already common in quantum physics laboratories and has immediate applications for ultraprecise sensing technologies and fundamental physics. “We wanted to take simple ingredients that you find in a lot of physical platforms and put these together in a minimal way to get something interesting, complex and powerful,” said Aashish Clerk, professor of molecular engineering at UChicago PME and senior author of the new study. The study is supported by Q-NEXT, a U.S. Department of Energy (DOE) National Quantum Information Science Research Center led by DOE’s Argonne National Laboratory. An Optical Cavity with a Twist The starting point for the new entangled states is a well-established experimental platform called cavity quantum electrodynamics, or cavity QED. In these systems, atoms or other particles are placed inside an optical cavity — a chamber formed by two mirrors. The particles interact with light that is confined in the optical cavity. In most cavity QED systems, all atoms interact with the confined light identically, making them indistinguishable from one other. This symmetry limits the range of quantum states the system can produce. “The challenge has always been that these systems have too much symmetry. All the atoms are talking to light in the same way,” Clerk said. “That really restricts what kind of entangled states
Jun 9, 2026 · via hpcwire.com
Advanced quantum hardware and algorithms developer Quantum X Labs Inc. has entered into a strategic cooperation agreement with the Israeli Quantum Computing Center (IQCC), an open-access research and development testbed operated by Quantum Machines. The collaborative agreement establishes a practical testing framework under which Quantum X Labs will integrate and evaluate its proprietary, AI-based quantum error-correction (QEC) technology within a live classical-quantum hardware loop. By shifting away from purely theoretical software simulations and migrating its algorithm to physical infrastructure, the company intends to study real-time decoding efficiency and analyze how machine learning models adapt to the native noise profiles of physical quantum processors. Deep Transformer Decoders Combined with Low-Latency Control Hardware The technical core of the evaluation program centers on compiling Quantum X Labs’ patented Deep Transformer Decoder algorithm directly into Quantum Machines’ commercial OPX1000 real-time quantum controller. Standard error-correction workflows rely on classical decoding heuristics, such as minimum-weight perfect matching, to process the data syndrome flags collected by physical readout pulses. However, these traditional techniques often face computing latency constraints as physical systems scale. Quantum X Labs’ approach replaces these heuristics with a trained transformer neural network designed to track error propagation patterns. To execute this algorithm fast enough to outpace the natural decoherence time of superconducting qubits, the software requires direct, low-latency integration with the hardware abstraction layer. The programmable orchestration architecture of the OPX1000 provides the precise classical-quantum feedback speed necessary to run the deep transformer model alongside active qubit control lines. Multi-Vendor Benchmarking and Algorithmic Roadmap Validation Headed by Chief Quantum Technology Scientist Professor Nir Sharon and IQCC General Manager Dr. Nir Alfasi, the evaluation project leverages the unique, multi-vendor ecosystem hosted at the Tel Aviv testbed. Rather than restricting algorithmic benchmarking to a isolated hardware setup, the IQCC environment enables multi-modal evaluations across distinct quantum
Jun 9, 2026 · via quantumcomputingreport.com
Yuval interviews Robert Wille, a computer scientist and co-founder focused on quantum computing software. They discuss the field’s transition from research to practical deployment, the need for heterogeneous and hardware-agnostic software stacks, and the integration of quantum into HPC environments. Robert explains the importance of design automation, open-source strategy, and AI-assisted development, arguing that quantum’s complex optimization challenges resemble those long solved in classical computing. Transcript Yuval: Hello Robert and thank you for joining me today. Robert: Hello Yuval, happy to be here. Yuval: So who are you and what do you do? Robert: Yeah, I’m Robert, I’m a computer scientist by training and for more than 15 years now actually involved in quantum computing. We are building software for quantum computing, which is important because it is awesome to have great quantum computing hardware and have great quantum computing applications, but you definitely need software to connect the end users to the hardware and to make these applications work. Yuval: I think you have at least two hats, right? One is an academic and the other is an entrepreneur. Could you tell us a little bit about that, please? Robert: Absolutely. By heart, I’m an academic and a researcher. I work at the Technical University of Munich and at the same time we also figured out it is great to do innovation at the university, in an academic setting, but for real impact we also need production-ready software and this cannot really be done at the university environment anymore. That’s why a little bit more than a year ago we also founded a company, and there we now take all the innovation from the academic developments and make it production-ready in a commercial context. Yuval: Since you’ve been doing it for so long, software in the context of quantum,
Jun 9, 2026 · via quantumcomputingreport.com
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Jun 9, 2026 · via youtube.com
How China’s ‘Hefei model’ spawned a chipmaker’s multibillion-dollar IPO plan
City government has leveraged over 220 billion yuan in state-owned capital to drive project investment in emerging industrial clusters
At Changxin Memory Technologies’ headquarters in Hefei, the capital of central China’s Anhui province, the sprawling production facilities seem to reflect a broader, citywide optimism.
While pre-IPO estimates value the chipmaker at roughly 150 billion yuan (US$22.2 billion), market analysts project that once public trading begins, CXMT’s market capitalisation could exceed 1 trillion yuan, providing the ultimate validation of the “Hefei model”.
Formerly dubbed “China’s most aggressive venture capitalist”, Hefei’s city government provided enormous backing for CXMT’s quest to become a top memory chipmaker, helping to make the city a poster child for China’s technological progress.
A magnet for tech firms
Hefei, however, stepped in with conviction. In 2016, the city took an 80 per cent stake in the first phase of a 12-inch memory wafer manufacturing base project that cost 150 billion yuan – Anhui’s largest single industrial investment project at the time.
Jun 8, 2026 · via amp.scmp.com
Quantum computing isn't as far away as it seems. Every month, there seems to be a new breakthrough with the technology, and it makes the possibility of commercially viable quantum computing inch closer and closer. Most of the money in the quantum computing space will be made years before it becomes widely available, so it's imperative that investors devote a small amount of their portfolio to this rising industry. Three stocks I'm bullish on in the quantum sector are Alphabet (GOOG 1.14%) (GOOGL 1.26%), IonQ (IONQ +10.60%), and Nvidia (NVDA +1.70%). Each of them represents a unique way to invest in quantum computing, and all have major upside. 1. Alphabet Alphabet is one of the most potent competitors in the quantum computing space. It has some of the most resources of anyone competing, fueled by growing cash flows from its advertising, cloud computing, and artificial intelligence (AI) business segments. Furthermore, because it is self-funding, it doesn't need to advertise every breakthrough it achieves with its technology. This makes it a bit more secretive, but from what has been announced, it's clear that Alphabet is both a frontrunner in quantum technology and applications. Alphabet was one of the first to announce a quantum algorithm that actually provides a notable step forward toward real-world applications, like MRI scans. It has also developed the algorithm necessary to break cryptocurrency blockchain encryption with its quantum computing technology. All of this is made possible with its Willow quantum computing chip, which has 105 qubits, making it one of the more powerful quantum computing chips today. NASDAQ: GOOGL Key Data Points I think Alphabet is one of the biggest no-brainer investments in the quantum computing space, and will continue to impress investors with each announcement it makes. If quantum computing turns out to be a flop,
Jun 8, 2026 · via fool.com
Scientists have developed a new hybrid quantum-classical algorithm to solve large-scale eigenvalue problems, a crucial requirement in nuclear many-body theory where Hamiltonian matrices often reach exceptionally large dimensions. The method combines quantum annealing and classical deflation to iteratively determine the complete eigenspectrum of both standard and generalised eigenvalue problems. This approach was benchmarked using problems originating from the Equation of Motion Phonon Method, performing calculations on actual quantum hardware and illuminating both the potential and current limitations of near-term quantum devices for tackling complex nuclear physics calculations. Hybrid quantum-classical deflation extracts complete eigenspectra for nuclear structure modelling A significant performance boost is achieved with a hybrid quantum-classical algorithm, attaining machine-precision accuracy on eigenvalue calculations within approximately 30 iterations. Classical Simulated Annealing often failed to converge or required over 450 iterations for comparable results, highlighting a clear advantage. This breakthrough addresses a key limitation in nuclear physics, where solving large eigenvalue problems, essential for modelling atomic nuclei, was previously hampered by the coherence and error correction demands of algorithms like Quantum Phase Estimation. The computational complexity of these problems scales rapidly with the number of nucleons within the nucleus, quickly exceeding the capabilities of even the most powerful classical supercomputers. Traditional methods struggle to accurately determine the energy levels and wavefunctions of these complex systems. This hinders our understanding of nuclear structure and reactions. Dr. James Maxwell and colleagues successfully extracted complete eigenspectra from problems originating from the Equation of Motion Phonon Method, utilising actual quantum hardware for the first time and demonstrating a novel computational pathway. Large-scale eigenvalue problems are commonplace in nuclear many-body theory, with Hamiltonian matrices often becoming extremely large. For instance, matrices with dimensions exceeding 1000×1000 are not uncommon in realistic nuclear structure calculations. Quantum computing presents new approaches to tackle these demanding problems, but the Quantum
Jun 8, 2026 · via quantumzeitgeist.com
Improved quantum algorithms for performing element-wise transforms on matrices have been created by Zane M. Rossi and Rahul Sarkar at The University of Tokyo, in collaboration with University of California and UC Berkeley. The algorithms sharply reduce the computational space needed for these transforms, achieving an exponential decrease compared to previous methods when applying polynomial functions. This advancement fills a gap in existing quantum linear algebra techniques, potentially benefiting applications including machine learning, simulation, and signal processing. The team also identified and corrected inaccuracies within earlier constructions of these algorithms, solidifying the foundation for more efficient quantum computation Exponential scaling reduction enables efficient quantum element-wise function computation The space required to compute quantum element-wise transforms has been reduced exponentially in the degree of the applied function, a gain previously unattainable with existing methods. Achieved by researchers at The University of Tokyo and collaborating institutions, this breakthrough overcomes limitations in prior quantum linear algebra techniques. Earlier algorithms struggled with the computational demands of applying functions to each matrix element individually, often requiring resources that scaled poorly with the size of the matrix and the complexity of the function. This new work addresses a critical bottleneck in translating complex computational problems into a quantum framework. This advance unlocks the potential for more efficient quantum computation across diverse fields including machine learning, simulation, and signal processing, enabling calculations on larger and more complex datasets. A substantial reduction in the computational space needed for quantum element-wise transforms has been demonstrated, achieving gains beyond those of previous techniques. For instance, a function with a higher degree, say, a polynomial of degree 10, now requires significantly less quantum space to compute its element-wise application to a matrix than would have been possible with earlier algorithms. This is particularly important as many machine learning algorithms rely on
Jun 8, 2026 · via quantumzeitgeist.com
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Jun 8, 2026 · via youtube.com
Elon Musk’s most fundamental advice to entrepreneurs looking to get investors to launch a new technology was to build a working prototype. Musk succinctly separated theory from tangible reality when he said: “Everything looks great on PowerPoint” as where everyone may start, but only those theories that can be shown to actually work as purported, no matter how crudely they may appear, will stand a much better chance of generating investment capital. Quantum Computing spent nearly two decades on the drawing board and as a theory until the first prototype debuted in 1998 at Oxford University. That set the stage for genuine R&D investment to truly commence. Fast forward to 2026: Quantum Computing has made great strides and now sits roughly where A.I. was three years ago. The excitement is palpable; even though the bulk of pure-play quantum computing companies are pre-revenue with some still deeply in the red, there are enough major tech companies now involved to make investors willing to roll the dice. The $6 billion AUM Defiance Quantum ETF (NASDAQ: QTUM)’s +54.20% YTD and +98.72% 1-year gain is evidence of this confidence. With A.I. ETFs like iSharesUS Technology ETF (NYSE: IYW) with $25 billion AUM and Fidelity MSCI Information Technology Index ETF (NYSE: FTEC) at $21 billion AUM, can QTUM be far behind? Defiance Quantum ETF Sporting a 5-star Morningstar rating, QTUM tracks the The BlueStar® Machine Learning and Quantum Computing Index. This index focuses on companies specifically involved with quantum computing and machine learning. With a 0.4% expense ratio, QTUM maintains appeal to ETF investors, and its portfolio treads the line between: - Pure-play quantum companies like Righetti, D-Wave and IonQ; - Tech companies that may either explode with profits or collapse in red ink down the road, like Snowflake CoreWeave, or MongoDB; - Stalwarts like
Jun 8, 2026 · via 247wallst.com
Longtime Broadcom CEO brings deep semiconductor expertise to Diraq’s leadership as the company ramps for significant US expansion PALO ALTO, Calif., June 08, 2026 (GLOBE NEWSWIRE) -- Diraq, the quantum computing pioneer, today announced the appointment of Scott A. McGregor as Chairman of the Board of Directors. McGregor, known for leading development of Microsoft Windows 1.0 and serving as CEO and President for both Broadcom and Philips Semiconductors (now NXP), brings a strong background in CMOS technology that complements Diraq’s silicon-based quantum approach. The previous chairman, the Hon. William Jeffrey (former Director of NIST, and former CEO of SRI International and HRL), will remain a member of the board. "We’ve shown that silicon chips are the best path forward for economical and ubiquitous quantum computing, and our focus is now entirely on delivering our first product by 2029, capable of outperforming existing supercomputers for tasks in finance, health and energy. Scott’s deep experience in leading global semiconductor giants matches this moment,” said Diraq CEO and Founder Andrew Dzurak. "His commercial leadership will be critical as we accelerate the roll out of our technology and scale the global deployment of Diraq’s quantum computers.” "Bill has been instrumental in building Diraq's systems engineering rigor and governance frameworks and preparing the team for DARPA's Quantum Benchmarking Initiative. I'm delighted that he will remain on the board and continue to provide strategic guidance as we enter this next chapter," added Dzurak. "The quantum computing race will closely mirror the evolution of the classic semiconductor industry, and the ultimate winners will have the most scalable manufacturing economics. Diraq’s silicon-based approach is the most promising path forward,” said incoming Diraq Chairman Scott A. McGregor. "Cost and manufacturability are the key factors in determining which infrastructure becomes the dominant computing layer, and Diraq’s fully CMOS-compatible method is
Jun 8, 2026 · via manilatimes.net
PALO ALTO, Calif., June 08, 2026 (GLOBE NEWSWIRE) -- Diraq, the quantum computing pioneer, today announced the appointment of Scott A. McGregor as Chairman of the Board of Directors. McGregor, known for leading development of Microsoft Windows 1.0 and serving as CEO and President for both Broadcom and Philips Semiconductors (now NXP), brings a strong background in CMOS technology that complements Diraq’s silicon-based quantum approach. The previous chairman, the Hon. William Jeffrey (former Director of NIST, and former CEO of SRI International and HRL), will remain a member of the board. “We’ve shown that silicon chips are the best path forward for economical and ubiquitous quantum computing, and our focus is now entirely on delivering our first product by 2029, capable of outperforming existing supercomputers for tasks in finance, health and energy. Scott’s deep experience in leading global semiconductor giants matches this moment,” said Diraq CEO and Founder Andrew Dzurak. “His commercial leadership will be critical as we accelerate the roll out of our technology and scale the global deployment of Diraq’s quantum computers.” “Bill has been instrumental in building Diraq's systems engineering rigor and governance frameworks and preparing the team for DARPA's Quantum Benchmarking Initiative. I'm delighted that he will remain on the board and continue to provide strategic guidance as we enter this next chapter," added Dzurak. “The quantum computing race will closely mirror the evolution of the classic semiconductor industry, and the ultimate winners will have the most scalable manufacturing economics. Diraq’s silicon-based approach is the most promising path forward,” said incoming Diraq Chairman Scott A. McGregor. “Cost and manufacturability are the key factors in determining which infrastructure becomes the dominant computing layer, and Diraq’s fully CMOS-compatible method is the most powerful, scalable, and economical option. I look forward to helping the team accelerate this phase of
Jun 8, 2026 · via markets.businessinsider.com
- The next revolution will be physical - Five discoveries could invert the course of civilization - The greatest scientists may not yet be born The technology industry has spent the past three years obsessing over what AI might do to jobs, search engines, software development and customer service. Those are important questions, but they may ultimately prove mundane compared to the far greater opportunities beginning to appear over the computational horizon. The real story may not be what AI does to information. The real story may be what AI and quantum computing do to physics. For decades, scientists have understood the broad outlines of many world-changing technologies. The problem has never been imagination. The problem has been complexity. Materials science, chemistry, molecular biology and fundamental physics involve search spaces so vast that even the world's largest classical computers — including today's AI supercomputers — struggle to explore them effectively. AI changes that equation by helping researchers identify promising pathways. Quantum computing, if it achieves its potential, could help model nature itself at a level of precision that conventional computers simply cannot match. The distinction is important. Classical computers, including the vast GPU clusters powering today's AI revolution, simulate reality through approximation. Quantum computers operate according to many of the same quantum principles that govern the molecules, atoms and materials they are attempting to model. Put simply, AI may help us decide where to look. Quantum computing may help us understand what we find. The result could be the greatest acceleration in scientific discovery since the Industrial Revolution. I'm particularly interested in this because of my work around the emerging Integrated Infrastructure Era and its corresponding six-layer model. We can already see parts of that future emerging today. Energy, connectivity, compute, AI and sovereignty are increasingly converging into a single
Jun 8, 2026 · via fierce-network.com
John Kelly III '76, a technology pioneer and one of the driving forces behind advanced computing and artificial intelligence research, will be the featured speaker at Union’s 232nd Commencement. More than 500 students will receive degrees during the ceremony, scheduled for 10 a.m. Sunday, June 14, on Roger Hull Plaza. In the event of inclement weather, Commencement will be held indoors nearby at the M&T Bank Center. Due to limited seating capacity, rain tickets will be required. During his 40-year career at IBM, Kelly was instrumental in establishing and maintaining IBM’s industry leadership across myriad technologies, ranging from semiconductors to supercomputers, from artificial intelligence to quantum computing. He will receive an honorary doctorate of engineering. “We are so honored and delighted to have one of Union’s most distinguished and loyal alumni, Dr. John Kelly, speak at Commencement,” said President Elizabeth Kiss. “John has been at the forefront of technological innovation throughout his career and is a wonderful example of how Union nurtures leaders who are able to combine technical brilliance with essential liberal arts skills. It is also particularly fitting to have John share his reflections with the Class of 2026, exactly 50 years after his own Union graduation.” At Union, Kelly majored in physics. He has credited his Union education with preparing him for his career. “Union College was the perfect fit for me. The unique combination of STEM and liberal arts taught me to think deeply, but also broadly,” Kelly said in an interview in 2024. “My physics education set the base for my career building computers, while my courses in psychology, political science and philosophy gave me the human perspective to lead and be a responsible technologist. Kelly joined IBM in research and development after earning a Ph.D. from Rensselaer Polytechnic Institute. Kelly held many positions during
Jun 8, 2026 · via union.edu