UK quantum computing company OQC raises $350 million The funding will be used to expand OQC’s operational presence in priority markets and accelerate its roadmap toward commercially useful, fault-tolerant quantum computing. UK-based quantum computing company OQC has announced it has closed an oversubscribed £260 million (USD 350 million) Series C funding round, according to a press release. Bullhound Capital led the round, which included investment from the British Business Bank, Fynveur (advised by Invus), COFIDES, RCM Private Markets Fund managed by Rokos Capital Management (US) LP, Alpha Edison, Fulcrum Asset Management, Pentland Ventures, Magdalen College Oxford, Adaptive Capital Partners, Firgun Ventures, 18 West and Oxford Capital. Existing investors including Oxford Science Enterprises, SBI, Chevron Technology Ventures, The University of Tokyo Edge Capital Partners Co. and OTIF Ventures also participated, reflecting continued support for OQC’s technology roadmap and international expansion. J.P. Morgan acted as exclusive placement agent on the transaction. OQC develops and operates superconducting quantum computers designed for deployment in data-centre environments serving enterprise and government customers. The company has established a global quantum computing platform across Europe, North America and Asia, with systems deployed in the UK, US, Japan and Spain. The funding will be used to expand OQC’s operational presence in priority markets and accelerate its roadmap toward commercially useful, fault-tolerant quantum computing, the press release said. “This is a coming-of-age moment for British quantum computing. It shows that British companies can play a leading role in a technology that will shape all our futures. Globally, it represents a clear shift in the market — from long-term promise to near-term delivery in quantum computing,” Gerald Mullally, CEO of OQC, said. “For OQC, this gives us the capital to scale internationally, advance our technology roadmap, and meet increasing demand from customers seeking secure, scalable access to quantum computing infrastructure.”
Jun 10, 2026 · via evertiq.com
Gonzales wins poster award at Seed LDRD session MCS Menu Alvin Gonzales, a postdoctoral appointee in the Mathematics and Computer Science division at the U.S. Department of Energy’s Argonne National Laboratory, received an Outstanding Poster Presentation Award at the lab’s 2026 Seed LDRD Poster Session in January. The annual event gives early-career scientists a chance to share their work with the lab community. Participants are judged based on presentation skills and the quality, clarity and organization of their poster. Gonzales’s poster introduced a new method called quantum distribution error mitigation (DEM) that corrects the output distribution of a quantum circuit by classical postprocessing. Most error mitigation techniques focus on improving the estimated value of an observable averaged over many runs. Gonzales instead focuses on correcting the full measured output distribution. A key part of his approach is estimating what’s called the noise vector. “Quantum circuits are noisy, and the error channels are generally difficult to characterize,” Gonzales said. To tackle this difficulty, he devised a tomography scheme that estimates the noise vector using just a single logical circuit. He then uses that scheme in DEM to classically adjust the noisy output distribution so that it better matches the ideal one. DEM also takes advantage of the circulant matrix structure, avoiding the need for expensive matrix inversion. “It opens new avenues of research, such as integration with error correction,” Gonzales said. Tests on quantum hardware with 5 to 30 qubits showed clear improvements in output distribution accuracy. In a 30-qubit GHZ test, for example, the method boosted the distribution fidelity to 97.7%, up from 23.2% without DEM correction. While this does not prepare the actual state, it demonstrates the effectiveness of DEM. “DEM dramatically increases the utility of pre-fault-tolerant quantum computers,” Gonzales said. “I think this work is a significant step
Jun 10, 2026 · via anl.gov
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Jun 10, 2026 · via youtube.com
The Technology Innovation Institute (TII), the applied research pillar of Abu Dhabi’s Advanced Technology Research Council (ATRC), has established a hardware program to construct the United Arab Emirates’ first domestic quantum computer. Operating from the dedicated laboratories of TII’s Quantum Research Centre (QRC), the project is being executed through a strategic international collaboration with Barcelona-based deep-tech startup Qilimanjaro Quantum Tech. The engineering initiative is directed by QRC Chief Researcher Professor José Ignacio Latorre and aims to establish localized hardware sovereignty while supporting the UAE’s broader macroeconomic shift away from oil dependency and toward a structured, knowledge-based digital economy. Superconducting Platform Selection and Cleanroom Infrastructure To establish baseline processing capabilities, the QRC engineering team opted for a solid-state superconducting qubit architecture, matching the fundamental hardware pathways utilized by primary industrial developers like Google and IBM. This topology was selected due to its established manufacturing reproducibility and clear engineering scaling pathways relative to alternative platforms such as trapped ions or neutral atoms. Initial program phases prioritize the construction, calibration, and stabilization of a localized laboratory facility in the UAE capital, including the installation of high-precision cleanroom fabrication machinery and cryogenic dilution refrigerators. Following foundational equipment calibration, on-site teams intend to transition directly into fabricating, characterizing, and benchmarking simple, native quantum chips to validate baseline coherence parameters on the Abu Dhabi substrate. Macroeconomic Capital Allocation and Cross-Disciplinary Research Framework The quantum computing hardware roadmap is anchored by a broader regional capital network, leveraging state-backed technology investments from the Abu Dhabi Investment Office (ADIO), Mubadala Investment Company, and strategic development initiatives organized under the 13.6 billion USD Ghadan 21 accelerator fund. These state vehicles focus on expanding local digital ecosystems like Hub71 and migrating venture capital assets to sovereign holding entities such as ADQ to accelerate early-stage commercialization pipelines. Within TII’s multidisciplinary research structure,
Jun 10, 2026 · via quantumcomputingreport.com
Galan Moody leads successful effort to secure $1.3 million for advanced 3D printing A new age of 3D printing is here, even though the initial technology for what is also known as additive manufacturing arrived less than 20 years ago. UC Santa Barbara is stepping into the era with a $1.15 million grant from the National Science Foundation (NSF) to purchase the most cutting-edge 3D printing technology available: a 3D rapid nanoprinting system based on two-photon photolithography. The equipment will enhance the capabilities of the already widely recognized UCSB Nanofabrication Facility (aka the “Nanofab” or “Nanotech”). “The unique capabilities of this system open the door to new approaches to nano- and micro-manufacturing of complex structures and devices that are no longer constrained by geometry nor confined to two-dimensional planes,” the authors wrote in their proposal. By securing the grant, lead PI Galan Moody, UCSB professor of electrical and computer engineering, and four co-PIs — Marley Dewey (bioengineering), Andrew Jayich (physics), Sumita Pennathur (chemical engineering) and Andrea Young (physics) — are ensuring that UCSB can take a leadership role in pushing the boundaries of what the new technology can do. “There are just a few universities in the U.S. that have tools with these capabilities,” said Moody. Recent advances have brought 3D printing to the realm of the very small, supporting an array of applications by enabling on-chip 3D printing of microstructures, a capacity that will benefit researchers in many disciplines. “Ten-nm-resolution lithography is available at off-campus commercial foundries,” Moody said, “but none is capable of creating complex 3D structures with nanoscale resolution and high speed for high-throughput prototyping, which are required for next-generation devices. Being able to make structures in true three dimensions opens new capabilities.” Five researchers, five uses The principal investigators’ research reflects the diverse focuses of potential
Jun 9, 2026 · via news.ucsb.edu
The leading top silicon spin quantum computing companies in 2026 build their qubits from single electron spins. Each electron sits in a tiny quantum dot etched into silicon or germanium. These dots are made on the same 300mm CMOS production lines that turn out ordinary logic chips. This shared manufacturing path gives silicon spin the deepest route to scale of any quantum architecture. The eight commercial vendors split into three design families. Five build gate-defined quantum dots on silicon-28 (Intel, Diraq, Quantum Motion, Equal1, Quobly). One uses atomic-precision donor qubits (Silicon Quantum Computing), one uses germanium quantum dots (Groove Quantum), and one uses silicon T-centre spin-photon qubits (Photonic Inc). The pace of progress turned sharp in late 2025. In December 2025 SQC reached 99.99% two-qubit fidelity, which matched the trapped-ion industry record. Diraq separately showed working qubits at 1 Kelvin, which means silicon spin can scale without a full dilution refrigerator. These vendors are the youngest slice of the quantum-hardware industry, yet also the fastest growing. DARPA QBI Stage B picked four of the eight in November 2025, and total funding across the field crossed $900M entering 2026. Why silicon spin is the late-arriving modality that may scale fastest Silicon spin has the shortest commercial history of any major modality. The first commercial pure-plays date only to 2016 and 2017. Despite that late start, it has the steepest path to large-scale manufacturing. Superconducting and trapped-ion still lead on raw qubit counts and gate-fidelity records. Silicon spin counters with three structural advantages that neither rival can match. First, it runs on 300mm CMOS foundry lines at GlobalFoundries, IMEC, Intel, and STMicroelectronics, which means it reuses existing chip factories instead of building bespoke ones. Second, its qubits are roughly 1,000 times smaller than the transmons used in superconducting machines, so far more
Jun 9, 2026 · via quantumzeitgeist.com
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