NPL collaborates with NVIDIA to use AI for automating quantum device calibration, improving qubit stability analysis and benchmarking methods.
Category: robotics/AI – Page 99
Introducing MirrorBot, a robot designed to foster human connection
While technology has made the world “smaller,” it has also pulled individuals apart, thanks to mobile phones and other devices that command our attention. Cornell University researchers are using technology, in the form of a mirror-equipped robot, to help bring people together. Members of the Architectural Robotics Lab, led by Keith Evan Green, have built a four-foot-tall robot—dubbed MirrorBot—with dual mirrors that, when placed in front of a pair of strangers, let each participant see themself in one mirror and the other person in the other.
In a study involving participants in a waiting-room setting, MirrorBot spurred conversations, playful exchanges and other interactions between strangers. The findings suggest that robots can act not only as conversational partners, but also as spatial mediators. The research is published in the journal Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction.
“We weren’t just trying to trigger conversations, but to support the very first moment of social connection, which is the eye contact,” said Serena Guo, lead author of the paper.
Sovereign AI: Why Owning The Full Stack Is The New Strategic Imperative
By Chuck Brooks
Artificial intelligence has entered a new phase of strategic consequence, and executives, policymakers, and small business owners can no longer afford to treat it as a back-office technology decision. The central question is no longer whether an organization will use AI. It is how much of that AI the organization will actually own.
Sovereign AI—the end-to-end ownership of the data, the model, and the interaction layer that connects them to the people who depend on them—is rapidly moving from a geopolitical discussion into a board-level and Main Street requirement.
Sovereign AI has largely been framed as a national concern, but that framing is incomplete. The same logic that compels a nation to own its AI stack compels a hospital system, a regional bank, a defense supplier, and a mid-sized manufacturer to do the same.
Nuclei Limit Neural Network Quantum Simulations
For a fixed number of configurations, representing quantum states becomes less accurate as their non-stabilizerness increases. This demonstrates a clear limit to how well restricted Boltzmann machines can compress and represent highly entangled systems. Calculations using ground states of medium-mass atomic nuclei reveal non-stabilizerness as a key property governing neural network performance.
Efficacy and Safety of VMAT2 Inhibitors in the Treatment of Huntington DiseaseA Meta-Analysis of Randomized Clinical Trials
In patients with Huntington disease, vesicular monoamine transporter 2 inhibitors (VMAT2is) treatment improved chorea without significant changes in adverse effects or depressive symptoms.
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Influence of Decreased Kidney Function on Plasma Biomarkers of Neurodegenerative Disorders in Routine Care: Confirmation of the Interest of Ratios
This study found that impaired kidney function was linked to increased plasma cerebral amyloidosis biomarkers, but ratio-based measures showed stable sensitivity and specificity for detecting cerebral amyloidosis across all eGFR groups.
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AI automates quantum dot voltage tuning for scaling up quantum computing
Semiconductor spin qubits are a promising candidate for the building blocks of next-generation quantum computers due to their high potential for integration and compatibility with existing semiconductor technologies. Qubits—like the 0s and 1s of a traditional computer—serve as a basic unit of information for quantum computers. However, the practical realization of these computers requires a massive number of qubits, making the development of more efficient adjustment methods a critical challenge for the field.
A research group including Yui Muto from Tohoku University’s Graduate School of Engineering, Assistant Professor Motoya Shinozaki and Associate Professor Tomohiro Otsuka from the Advanced Institute for Materials Research (WPI-AIMR), and their colleagues have successfully demonstrated a method that may help make this massive number of qubits much more manageable, moving us one step closer toward scaling up quantum computing. The findings are published in Scientific Reports.
AI accelerators deliver accurate models for challenging quantum chemistry calculations
The most demanding calculations in quantum chemistry can now be solved with graphics processing unit (GPU) supercomputers. A recently published study shows that software adapted to use GPU hardware can provide not just speed, but also the accuracy needed to solve complex chemistry problems. The work solved the two chemical structures often seen as too complex and expensive to tackle. The advance, published in the Journal of Chemical Theory and Computation, could allow researchers to make meaningful progress in designing new catalysts and improve predicted behaviors of magnetic and electronic materials.
Specifically, the research team—led by computational chemists from NVIDIA, Sandbox AQ, the Wigner Research Centre in Hungary, the Institute for Advanced Study of the Technical University of Munich in Germany, and the Department of Energy’s Pacific Northwest National Laboratory—showed that NVIDIA Blackwell architecture effectively tackles complex simulations. Here, the researchers used a mixture of mathematically precise and approximated approaches to accomplish their goal.
“Our study shows that AI-oriented hardware can do more than provide speed—it can also power chemically accurate, strongly correlated quantum chemistry at the frontier of what is computationally feasible,” said Sotiris Xantheas, a computational chemist at PNNL and study author. Xantheas also serves as the principal investigator of Scalable Predictive methods for Excitations and Correlated phenomena (SPEC), a Department of Energy initiative.
