My third installment of interesting research papers that I have read over the past few weeks and would like to share with my community.
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Theoretical particle physicist tackles machine learning’s black box
From self-driving cars to facial recognition, modern life is growing more dependent on machine learning, a type of artificial intelligence (AI) that learns from datasets without explicit programming.
Despite its omnipresence in society, we’re just beginning to understand the mechanisms driving the technology. In a recent study, Zhengkang (Kevin) Zhang, assistant professor in the University of Utah’s Department of Physics & Astronomy, demonstrated how physicists can play an important role in unraveling its mysteries.
“People used to say machine learning is a black box—you input a lot of data and at some point, it reasons and speaks and makes decisions like humans do. It feels like magic because we don’t really know how it works,” said Zhang. “Now that we’re using AI across many critical sectors of society, we have to understand what our machine learning models are really doing—why something works or why something doesn’t work.”
Ultrafast untethered levitation device offers frictionless design for omni-directional transport
Advances in technology have led to the miniaturization of many mechanical, electronic, chemical and biomedical products, and with that, an evolution in the way these tiny components and parts are transported is necessary to follow. Transport systems, such as those based on conveyor belts, suffer from the challenge of friction, which drastically slows the speed and precision of small transport.
Researchers from Yokohama National University addressed this issue by developing an untethered levitation device capable of moving in all directions. The frictionless design allows for ultrafast, agile movement that can prove to be very valuable in machine assembly, biomedical and chemical applications via contactless transport.
The results are published in the journal Advanced Intelligent Systems.
Discover How AI is Transforming Quantum Computing
Quantum technologies have had a meteoric rise and become a key area of prioritization for governments, academics, and businesses. Government funding commitments total almost $40 billion, while private investments since 2021 total nearly $8 billion. The US agency, National Institute of Standards and Technology, released this year three new post-quantum security standards, which governments classify as ‘critical resources’ for the economy and national defense. Meanwhile, users of quantum technologies experiment with them, from industry applications in drug development and materials science to energy grid optimization and logistics efficiency.
Yet, besides a few areas, such as quantum sensing, practical and impactful quantum technologies haven’t matured for widespread use. However, when combined with classical machine learning, practical use cases emerge.
This article delves into the impact and potential of artificial intelligence and quantum technologies with QAI Ventures, a financial partner and ecosystem builder in quantum technologies and AI, as a potential collaborator for startups to deliver investment, resources, global networks, and tailored accelerator and incubator programs.
This article covers AI and quantum technologies with QAI Ventures, a financial partner and ecosystem builder in emerging technologies.
Science Fiction? Think Again. Scientists Are Learning How to Decode Inner Thoughts
To protect users’ privacy, they chose a passphrase to activate the device that was unlikely to come up in everyday speech: “Chitty Chitty Bang Bang,” the title of the 1964 Ian Fleming novel and 1968 movie. The technology would start translating thoughts when it detected the phrase, which, for one participant, it did with 98.75 percent accuracy.
In the tests, the researchers asked the participants—all four of whom have some trouble speaking—to either attempt saying a set of seven words or to merely think them. They found the patterns of neural activity and regions of the brain used in both scenarios were similar, but the inner thoughts produced weaker signals.
Then, the team trained the computer system on the signals produced when participants thought words from a 125,000-word vocabulary. When the users then thought sentences with these words, the device translated the resulting brain activity. The technology produced words with an error rate of 26 to 54 percent, making it the most accurate attempt to decode inner speech to date, Science reports.
In 2050, pets won’t be adopted… they’ll be designed — Glowing cats, Winged dogs!
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DARPA’s Laser Leap Proves “Energy can fly like data” as 800-Watt Beam Sets Distance Record and Opens Door to UAV and Space Uses
IN A NUTSHELL 🚀 A DARPA-led team set a new record by transmitting 800 watts over 5.3 miles using optical power beaming. ⚡ Power beaming could revolutionize energy delivery to remote locations and reduce logistical challenges. 🔬 The breakthrough involved a customized receiver and a high-energy optical laser to maximize efficiency. 🌍 Future phases aim
Passive Demultiplexed Two-photon State Generation from a Quantum Dot
High-purity multi-photon states are essential for photonic quantum computing. Among existing platforms, semiconductor quantum dots offer a promising route to scalable and deterministic multi-photon state generation. However, to fully realize their potential we require a suitable optical excitation method. Current approaches of multi-photon generation rely on active polarization-switching elements (e.g., electro-optic modulators, EOMs) to spatio-temporally demultiplex single photons. Yet, the achievable multi-photon rate is fundamentally limited by the switching speed of the EOM. Here, we introduce a fully passive demultiplexing technique that leverages a stimulated two-photon excitation process to achieve switching rates that are only limited by the quantum dot lifetime. We demonstrate this method by generating two-photon states from a single quantum dot without requiring any active switching elements. Our approach significantly reduces the cost of demultiplexing while shifting it to the excitation stage, enabling loss-free demultiplexing and effectively doubling the achievable multi-photon generation rate when combined with existing active demultiplexing techniques.
I Introduction.
Photonic quantum computing offers a unique advantage over other quantum platforms due to the long coherence time of photons, enabling robust quantum communication, quantum information processing, and quantum simulations. A critical requirement for these applications is the reliable generation of high-purity multi-photon states, i.e., nn indistinguishable photons in nn spatial modes – which serve as fundamental building blocks for quantum algorithms, error correction, quantum simulations, and advanced photonic networks. Multi-photon states are also essential for probing quantum optical phenomena such as multi-photon interference. The most widely used sources to produce multi-photon quantum states are the ones relying on parametric down-conversion or four wave mixing in nonlinear crystals. However, the scalability here is limited, due to the probabilistic nature of photon emission and the required resource overhead for computing and boson sampling applications.
Constructor Theory Explains Origin of Time
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Most physicists believe that time fundamentally doesn’t exist, because the concept of time is incompatible with a model of physics where quantum mechanics and general relativity coexist. David Deutsch and Chiara Marletto have now shown that “constructor theory” can be used to construct time. Let’s take a look.
Paper: https://arxiv.org/pdf/2505.
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Physicists unlock hidden order in twisted graphene, paving way for supermoiré engineering
A new study by applied physicists used a custom-designed microscope to examine supermoiré patterns in trilayer graphene.