Google Uses AI to Manage Quantum Processor Willow
Google uses AI to manage quantum processor Willow.
Researchers at Google Quantum AI have employed reinforcement learning to manage the quantum processor Willow. The AI continuously adjusts the chip’s operational parameters, compensating for errors and helping maintain computational stability. This approach reduces the need for manual tuning and could be a significant step toward creating fault-tolerant quantum computers, where software automatically supports the operation of complex quantum systems.
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Jul 12, 2026 · via forklog.com
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Jul 12, 2026 · via youtube.com
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Jul 11, 2026 · via youtube.com
World Two-Stage Pulse Tube Cryocoolers - Market Analysis, Forecast, Size, Trends and Insights - Full report in PDF · Excel data package · Word document · Executive presentation - Email delivery 24/7 any day, weekends and holidays included - Content copy-paste enabled · printable format - Unlimited clarification rounds after delivery Two-Stage Pulse Tube Cryocoolers Market Forecast Points Higher Toward 2035, Driven by Quantum Computing and Semiconductor Expansion Abstract According to the latest IndexBox report on the global Two-Stage Pulse Tube Cryocoolers market, the market enters 2026 with broader demand fundamentals, more disciplined procurement behavior, and a more regionally diversified supply architecture. The world Two-Stage Pulse Tube Cryocoolers market is entering a period of sustained expansion, with demand projected to grow at a compound annual rate of 6–9% between 2026 and 2035. These closed-cycle cryogenic devices, capable of reaching temperatures below 10 K without moving displacers, are increasingly critical in high-value applications where low vibration, long maintenance intervals, and high reliability are non-negotiable. The market is being reshaped by the rapid commercialization of quantum computing, the scaling of semiconductor wafer inspection tools, and the proliferation of advanced scientific instrumentation in fields such as radio astronomy and materials research. System-level pricing for standard configurations ranges from USD 80,000 to USD 250,000 per unit, with integrated systems representing roughly 60–70% of market value. Import dependence remains structurally high outside the main manufacturing hubs—the United States, Japan, Germany, and Finland—with an estimated 60–70% of global procurement sourced across borders, making supply chain resilience a critical strategic factor. Aftermarket services, including preventive maintenance, helium reclamation, and spare-parts provisioning, are growing at 8–10% per year as the installed base matures. Key challenges include high upfront capital expenditure, long qualification cycles of 6–18 months, and a shortage of qualified cryogenic engineers in emerging markets. This report
Jul 11, 2026 · via indexbox.io
A new quantum low-density parity-check (LDPC) code offers promising advancements in quantum error correction. Koki Okada and Kenta Kasai from Institute of Science Tokyo have constructed a rate-2/3 code with parameters [[34542, 23032, d ≤ 310]] using a (3,18)-regular two-branch finite-field base and a circulant-permutation-matrix (CPM) lift. This construction yields a Calderbank-Shor-Steane (CSS) code with a girth of 8, demonstrating improved structural properties and potential for efficient decoding. Decoder experiments reveal no failures in a substantial number of trials, and finite-length frame error rate sweeps suggest a strong performance threshold, signifying a step towards more reliable quantum communication and computation. Enhanced stability in quantum error correction via a high-performance LDPC code Decoder experiments revealed no failures in 108 trials at a perturbation level of p=0.01, a substantial improvement over previous quantum low-density parity-check (LDPC) codes. Prior designs struggled to maintain stability at this scale, but this success indicates a strong performance threshold. A rate-2/3 CSS construction with a length of 34542, utilising a two-branch finite-field base and a circulant-permutation-matrix lift, achieves this enhanced stability and represents a major leap in error durability. Quantum error correction is crucial for building practical quantum computers, as qubits are inherently susceptible to decoherence and other noise sources. LDPC codes, known for their effectiveness in classical communication, are increasingly being adapted for quantum applications due to their ability to correct errors with relatively low overhead. The development of codes with higher thresholds, like this rate-2/3 code, is essential for mitigating the effects of noise and enabling fault-tolerant quantum computation. The rate-2/3 CSS construction, with a length of 34542, employs a two-branch finite-field base and a circulant-permutation-matrix lift of degree 101 to ensure durability. Base matrices possess a row weight of 18 and a column weight of 3, and the associated Tanner graphs exhibit a girth
Jul 11, 2026 · via quantumzeitgeist.com
11 Jul PRODUCT REVIEWS CONTENT CREATOR, TESTER AND INFLUENCER – TOP THOUGHT LEADER & SPEAKER Product reviews content creators are influencers, journalists, bloggers, YouTubers, podcasters, social media personalities, and industry experts who evaluate products and share their experiences with audiences. Output helps consumers make informed purchasing decisions, with the best product reviews content creators also providing brands with valuable exposure, credibility, and customer feedback. Today’s consumers rarely make significant purchases without first researching evaluations. Whether buying software, electronics, beauty products, automobiles, kitchen appliances, business services, or consumer goods, people increasingly rely on trusted top product reviews content creators who offer honest evaluations, demonstrations, comparisons, and recommendations. For brands, partnering with UGC pros and influencers has become a big part of modern marketing. Authentic work can generate awareness, improve search visibility, build trust, and influence purchasing decisions across multiple platforms. What Is a Product Review Content Creator? Famous product reviews content creators are individuals or media organizations that independently or collaboratively evaluate items and share insights through digital content. Reviews may include: - Written articles - Blog posts - Video reviews - Social media posts - Podcasts - Live streams - Product demonstrations - Comparison guides - Buying guides - Unboxing videos - Tutorials - Long-term product testing The aim of celebrity product reviews content creators is to educate consumers by highlighting features, benefits, drawbacks, performance, and overall value. Why Product Review Content Matters Consumers trust independent opinions more than traditional advertising. A well-produced product review provides transparency that helps buyers feel confident before making a purchase. Benefits include: - Increased consumer confidence - Better-informed purchasing decisions - Greater brand awareness - Higher search engine visibility - Authentic customer engagement - Improved product credibility - Stronger social proof - Increased conversion rates Work by global product reviews content creators also provides
Jul 11, 2026 · via futuristsspeakers.com
Researchers at the Korea Research Institute of Standards and Science (KRISS) have pinpointed how coherent photons directly erode quantum coherence in superconducting qubits, revealing a degradation pattern tied to the qubit’s interaction with its resonator. Their measurements demonstrate the dephasing profile closely follows the resonator’s spectral characteristics, establishing a direct link between qubit coherence loss and the resonator itself. The team reports that measurements show the dephasing profile across photon frequencies closely follows the resonator’s spectral characteristics, highlighting the relationship between these quantum components. Dynamical decoupling proved robust in mitigating this coherence decay, performing effectively under a wide range of photon conditions and suggesting a versatile solution for multiple types of photon-induced noise. This degradation of quantum information is directly linked to the resonator’s spectral properties, and is not simply background noise. The study, published in Physics Applied, specifically investigated how both the frequency and number of coherent photons impact qubit performance, revealing that these photons directly influence quantum coherence degradation. Dynamical decoupling, a technique to protect qubits from environmental noise, proved remarkably effective. This resilience is crucial because the researchers note this technique can address ac Stark shifts and additional photon-mediated dephasing mechanisms, offering a promising pathway toward more stable and reliable superconducting quantum computers. The findings underscore the importance of carefully characterizing and controlling resonator properties in future qubit designs. This finding establishes a clear link between how the qubit interacts with its surrounding environment and the resulting loss of quantum information. This versatility extends to addressing issues like ac Stark shifts and additional photon-mediated dephasing, broadening the potential applications of this protective measure and suggesting improvements to superconducting quantum computer stability.
Jul 11, 2026 · via quantumzeitgeist.com
Researchers have demonstrated a complete, multi-stage attack against a quantum neural network, moving beyond isolated vulnerability studies to showcase a realistic threat scenario. The team, comprised of Cedric Brügmann, Daniel Herr, Daniel Ohl de Mello, and colleagues, successfully combined reconnaissance, crosstalk characterization, adversarial example generation, and a physical attack, all on a trapped-ion quantum computer. This end-to-end “kill-chain” highlights how an adversary can leverage information gathered during initial reconnaissance to refine subsequent attack stages, a critical consideration for quantum-as-a-service providers and multi-tenant environments. As the authors note, this work builds on an extensive review of the literature to align existing quantum machine learning attack vectors with the MITRE ATLAS framework, revealing the interconnectedness of hardware weaknesses and data manipulation techniques. Corresponding experiments were also reported on superconducting hardware in the appendix. Trapped-Ion Hardware for Quantum Neural Network Attacks Researchers detailed how information gleaned during initial reconnaissance phases directly improved the effectiveness of subsequent attack stages, a key departure from prior isolated vulnerability studies. This layered approach simulates a realistic threat scenario, particularly relevant for quantum-as-a-service (QaaS) environments where multiple users share hardware resources. The team specifically targeted trapped-ion quantum computers, successfully executing their “kill-chain” attack and reporting the corresponding superconducting-hardware experiments in the appendix. This focus on trapped-ion systems highlights a specific hardware vulnerability, as side-channel attacks exploiting power traces and timing have previously been demonstrated on superconducting devices. However, the researchers extended this work to a different physical platform, demonstrating broader applicability of these techniques. The authors discuss how the work builds on an extensive review of the literature, emphasizing the end-to-end nature of their demonstration. Crucially, the attack vectors employed operate within the limitations of current noisy intermediate-scale quantum (NISQ) devices, meaning they do not rely on the existence of fault-tolerant quantum computers. This makes the demonstrated threats
Jul 11, 2026 · via quantumzeitgeist.com
Researchers at the Flatiron Institute, Dries Sels (Boston University & Flatiron Institute) and Flaviano Morone (New York University), have discovered a relationship between a quantum algorithm’s performance and a fundamental parameter typically considered separate from optimization, spin. Their analysis of the quantum approximate optimization algorithm, or QAOA, reveals an optimal balance and convergence to a value similar to log(p)/p when the spin value (S) is approximately equal to the QAOA depth (p). This challenges the assumption that maximizing spin always improves performance, suggesting an efficient balance instead. The semiclassical approach slightly outperforms the true spin-1/2 QAOA, implying that a classical approximation can achieve comparable results. Removing initial noise and re-optimizing parameters then yields a convergence rate of 1/p. Sherrington-Kirkpatrick Model for QAOA Benchmarking The pursuit of quantum advantage in optimization problems has encountered a surprising challenge; recent analysis using the Sherrington-Kirkpatrick (SK) model reveals that a semiclassical approximation of the Quantum Approximate Optimization Algorithm (QAOA) can, in certain scenarios, outperform the true quantum version. Researchers Dries Sels and Flaviano Morone explored this result, questioning the assumption that maximizing quantum effects always yields superior performance. Their work, detailed in a recent preprint, centers on benchmarking QAOA against the notoriously difficult SK spin glass model, a system with all-to-all interactions between spins. The SK model’s well-defined lowest energy state, a value of -0.7631…, provides an ideal testing ground. The researchers employed the truncated Wigner approximation (TWA), a semiclassical method, to simulate QAOA’s dynamics. The study explains that the method is semiclassical because it is a saddle-point expansion of a path integral, relying on classical evolution with quantum fluctuations controlled by the spin value, S. This relationship is reflected in a convergence of the final energy to the Parisi value, following a pattern similar to log(p)/p. The team found that at small spin
Jul 11, 2026 · via quantumzeitgeist.com
A former Meta engineer has publicly identified two structural vulnerabilities he believes could undermine Bitcoin’s long-term viability: the potential threat of quantum computing to the cryptocurrency’s encryption, and the economic challenge posed by declining block rewards for miners. The analysis, shared by TechLeadHD and reported by Wu Blockchain, adds a critical voice to ongoing debates about Bitcoin’s security model and its future as a decentralized financial system. Quantum Computing: A Looming Encryption Risk TechLeadHD, who previously worked as a software engineer at Meta, highlighted the advancement of quantum computers as a direct threat to the cryptographic security of Bitcoin wallets. Bitcoin relies on elliptic curve digital signature algorithms (ECDSA) to secure transactions and prove ownership. A sufficiently powerful quantum computer could theoretically break this encryption, allowing an attacker to derive private keys from public keys and potentially steal funds from active wallets. While practical quantum computers capable of such attacks are not yet a reality, the timeline for their development remains a subject of intense speculation within both the cryptography and cryptocurrency communities. The concern is not immediate, but the potential for a sudden, disruptive technological leap represents what TechLeadHD calls a ‘time bomb’—a risk that could detonate with little warning once the underlying technology matures. The Miner Incentive Problem: Beyond Block Rewards The second vulnerability identified by the former engineer is more immediate and economic in nature. Bitcoin’s security model depends on a decentralized network of miners who validate transactions and secure the blockchain. These miners are compensated through two mechanisms: newly minted bitcoins (the block reward) and transaction fees paid by users. Bitcoin’s supply is capped at 21 million coins, and the block reward is halved approximately every four years in an event known as the ‘halving.’ As the block reward shrinks, miners become increasingly dependent on transaction
Jul 11, 2026 · via cryptonews.net
Price movement over the last 24 hours Aegon Ltd. vs D Wave Quantum Inc — how do they compare? Aegon Ltd. trades at $8.8 (market cap $12.97B), while D Wave Quantum Inc trades at $20.08 (market cap $7.44B). The key difference: Aegon Ltd. is the larger of the two by market cap, and Aegon Ltd. pays a 5.27% dividend while D Wave Quantum Inc pays none. Which is the better fit depends on your goals. | AEG | QBTS | | |---|---|---| | Market Cap | $12.97B | $7.44B | | Sector | Financials | Technology | | 52-Week High | $8.80 | $44.78 | | 52-Week Low | $6.79 | $12.98 | | Enterprise Value | $14.10B | $6.90B | | Dividend Yield | 5.27% | — | Trailing returns across standard periods Aegon is a Netherlands-headquartered insurance company with core operations that stretch across the U.S., Netherlands, and United Kingdom. The business also holds peripheral ventures in Spain, Portugal, Brazil, and China. Read more on AEG →D-Wave Quantum Inc. is a global leader in the development and delivery of quantum computing systems, software, and services. The company specializes in annealing quantum computers designed to solve complex optimization problems across industries such as logistics, materials science, and financial modeling. D-Wave offers its technology through the cloud, allowing customers to build and run real-world quantum applications today, making it a key player in the commercialization of quantum computing. Read more on QBTS →
Jul 11, 2026 · via pluang.com
IonQ, D-Wave Quantum, Quantinuum, Quantum Computing, and Horizon Quantum Computing Pte. are the five Quantum Computing stocks to watch today, according to MarketBeat's stock screener tool. Quantum computing stocks are shares of publicly traded companies that develop quantum computing hardware, software, or related services, or that are expected to benefit from advances in quantum computing. For stock market investors, the term usually refers to speculative or growth-oriented investments tied to the potential commercialization of quantum technologies rather than companies with mature, widely deployed products. These companies had the highest dollar trading volume of any Quantum Computing stocks within the last several days. IonQ (IONQ) IonQ, Inc. engages in the development of general-purpose quantum computing systems in the United States. It sells access to quantum computers of various qubit capacities. The company makes access to its quantum computers through cloud platforms, such as Amazon Web Services (AWS) Amazon Braket, Microsoft's Azure Quantum, and Google's Cloud Marketplace, as well as through its cloud service. Read Our Latest Research Report on IONQ D-Wave Quantum (QBTS) D-Wave Quantum Inc. develops and delivers quantum computing systems, software, and services worldwide. The company offers Advantage, a fifth-generation quantum computer; Ocean, a suite of open-source python tools; and Leap, a cloud-based service that provides real-time access to a live quantum computer, as well as access to Advantage, hybrid solvers, the Ocean software development kit, live code, demos, learning resources, and a vibrant developer community. Read Our Latest Research Report on QBTS Quantinuum (QNT) Quantum computing is quickly evolving from research to early commercial adoption to address the insatiable need for computing power in the digital age. Even as classical computing continues to advance in energy-efficient performance, the huge computational demands of new applications such as artificial intelligence (“AI”) are making it challenging for classical computing to keep
Jul 11, 2026 · via marketbeat.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
Jul 11, 2026 · via aaii.com
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Jul 11, 2026 · via moomoo.com
Quantum computers are coming for crypto. The industry is racing to prepare Blockchain networks built on decades-old cryptography face a potential existential threat as developers begin planning a costly and complex security overhaul. The cryptocurrency industry is beginning to prepare for a new and potentially existential challenge: the possibility that quantum computers could eventually break the cryptographic systems that protect digital assets, transactions and wallets. Quantum computers are designed to solve certain complex mathematical problems far faster than today’s most advanced conventional computers. If the technology reaches the required level of capability, it could undermine traditional encryption methods that secure digital information, posing a major risk to the roughly $2 trillion global cryptocurrency market, which relies on blockchain networks protected by decades-old cryptography. While practical quantum computers capable of breaking current encryption remain experimental, concerns have intensified following recent advances in the field. Research from Google, one of several technology companies developing quantum systems, suggested that the timeline for such attacks could be shorter than previously believed. Google has said that quantum computers capable of breaking encryption could arrive by 2029, whereas previously many experts expected such capabilities to remain at least a decade away. Research from Citigroup and other institutions has also suggested that advances in quantum computing, alongside breakthroughs in artificial intelligence, are accelerating the timeline in which cryptocurrencies could become vulnerable to attacks. Recognizing the broader national security implications of quantum technology, U.S. President Donald Trump recently issued executive orders aimed at strengthening American quantum capabilities. Some cryptocurrency companies and blockchain developers are already preparing plans to upgrade their networks with quantum-resistant cryptography, a potentially years-long process that could require significant changes to the infrastructure underlying digital assets. “It’s the most direct and existential threat towards cryptocurrencies and crypto networks,” said Chris Tam, head of quantum innovation
Jul 11, 2026 · via calcalistech.com
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Jul 11, 2026 · via youtube.com
Quantum computing may be getting closer to solving one of its biggest challenges. According to Atom Computing’s Kristen Pudenz, advances in scaling are making practical error correction possible, opening a path toward fault-tolerant systems and utility-scale quantum computing. Pudenz, vice president of research programs at Atom Computing, has been tracking that shift for much of her career. She began her doctorate in 2008, when utility-scale quantum computing still felt like a distant possibility. Today, she sees a clearer path forward. The reason, she says, comes back to two themes: scaling and error correction. “They relate intimately to one another. The scaling that is happening allows error correction to begin to be implemented at a useful level,” Pudenz said in a recent interview with MeriTalk. “I think that’s a really exciting moment for quantum computing.” “People should be taking quantum technology very seriously right now,” Pudenz said. “Now is when the rubber meets the road.” Scaling makes error correction possible Error correction has long been viewed as a prerequisite for practical quantum computing. Quantum systems are highly sensitive to noise and errors, making it difficult to run complex calculations reliably. Until recently, however, many quantum platforms lacked the scale necessary to implement error correction in a meaningful way. That is beginning to change, according to Pudenz. “What error correction gives to the quantum user community, why people should care about error correction, is that it means that the applications that you put on the quantum computer can go all the way,” Pudenz said. “They can go all the way to the promise of utility-scale quantum computing.” The shift has significant implications for organizations already exploring quantum applications. Rather than developing algorithms that eventually run into hardware limitations, users can begin designing software with future fault-tolerant systems in mind. Pudenz said that
Jul 11, 2026 · via meritalk.com
Abstract We consider the problem of preparing thermal equilibrium states at finite temperature on quantum computers. Assuming thermalization, we show that states that are locally at thermal equilibrium can be prepared by evolving adiabatically an initial thermal Gibbs state of a simple Hamiltonian with an interpolating time-dependent Hamiltonian, identically to adiabatic ground state preparation. We argue that the entropy density of local density matrices is conserved during the adiabatic evolution in the thermodynamic limit, so that both the entropy and energy of the final state can be computed, and thus the final temperature too. We show that in the presence of hardware noise, the entropy created by the noisy evolution can be precisely benchmarked with mirror circuits. We give numerical evidence that the resulting thermal state preparation protocol is noise-resilient for depolarizing noise, in the sense that the energy-temperature curve measured on a noisy quantum computer is remarkably insensitive to the amplitude of depolarizing noise in the state preparation. We finally propose a protocol to estimate the lack of adiabaticity in a given actual Trotter implementation of the dynamics. We test our protocol on Quantinuum’s H1-1 ion-trap device. We measure that a circuit with 640 two-qubit gates implemented on hardware generates an entropy per site of 0.166 ± 0.0045, giving a benchmark metric for this state preparation. We report the preparation of a thermal state with temperature 2.56 ± 0.26 of the Ising model in size 5 × 4. Similar content being viewed by others Introduction The simulation of materials and condensed matter systems is expected to be one of the first applications of quantum computers1,2. While low-entanglement ground state physics can be studied classically in a number of cases albeit with extensive numerical effort3,4,5, any settings that involve higher-excited states is considerably more challenging to classical computers. This includes
Jul 10, 2026 · via nature.com
There are some obvious big picture issues that stand between us and useful quantum computing. Issues like whether we can make enough high-quality hardware qubits to connect into the error-corrected logical qubits we need, and how we generate the states needed to perform universal computation on those logical qubits. But there are also many less prominent challenges that will need to be solved before we can perform calculations. One of those challenges, which only affects some types of hardware, is calibration. For devices we manufacture, like superconducting qubits, there are always subtle variations among individual qubits. (This is not true when we use something like an atom to hold the qubit, but the lasers that control them can drift.) As a result, this hardware is put through a process called calibration, where we test different frequencies and amplitudes of the microwave pulses that control them to find the combination that produces the lowest error rates, and then save those settings for use in calculations. However, you can’t perform the typical calibration process while you’re doing calculations, which means drift becomes an issue for long and complicated algorithms. Google, though, has figured out that it’s possible to do calibration using the same data that’s used for error correction. Reinforcement learning The hardware that Google and a number of other companies rely on are transmons. They consist of a loop of superconducting wire connected to a resonator, and they’re controlled by pulses of microwave photons. Those pulses are controlled by hardware that is kept outside of the refrigeration, including classical computers and the microwave sources they control. This hardware is used to test different combinations of wavelengths and amplitudes during calibration.
Jul 10, 2026 · via arstechnica.com
Eric Moskowitz Harvard Staff Writer Harvard Staff Writer Pictured above, left to right: Kiyoul Yang, Assistant Professor, Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS); Giulia Semeghini, Assistant Professor of Applied Physics, SEAS; Xing Fan, Assistant Professor of Physics; Victoria D’Souza, Professor of Molecular and Cellular Biology; Doeke Hekstra, Associate Professor of Molecular and Cellular Biology, Associate Professor of Applied Physics, SEAS; donor James A. Star ’83; donor Josh Friedman ’76, M.B.A. ’80, J.D. ’82; Mahmoud Mikdar (research associate representing Professor Manoj Duraisingh, John LaPorte Given Professor of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health); Ahmad “Mo” Khalil, Hok Lam and Kathleen Kam Wong Professor of Bioengineering, SEAS, Professor of Molecular and Cellular Biology; Maxim Prigozhin, Assistant Professor of Molecular and Cellular Biology and of Applied Physics, SEAS; Kathy Liu (front; graduate student representing Joanna Aizenberg, Amy Smith Berylson Professor of Materials Science, SEAS, Professor of Chemistry and Chemical Biology); Donhee Ham John A. and Elizabeth S. Armstrong Professor of Engineering and Applied Sciences, SEAS (rear); Chenghua Gu, Professor of Neurobiology, Harvard Medical School; Quan Lu, Cecil K. and Philip Drinker Professor of Environmental Physiology, Harvard T.H. Chan School of Public Health; selection committee chair Michael Desai. The 2026 Star-Friedman Challenge grants will support seven “high risk, high reward” projects pursued by Harvard researchers, including engineering red blood cells to patrol the body as disease detectors, designing microchip-sized lasers for molecular experiments and quantum computing, and developing tactile-sensing “skins” to help robots achieve more human-like touch in assisting with senior care, surgery, or disaster recovery. “I’m really struck by the ambition and range of the work,” said Hopi Hoekstra, Edgerley Family Dean of the Faculty of Arts and Sciences, at a University Hall celebration for the winners and their projects. “It really reflects
Jul 10, 2026 · via current.fas.harvard.edu