Researchers from Harvard SEAS and Boston University reveal its transformative effects, offering newfound mobility and independence for individuals with this debilitating condition.
The wearable tech successfully eliminates a common symptom called ‘gait freezing’ to restore smooth strides for Parkinson’s disease sufferers.
Researchers claim they have achieved the unachievable – the discovery of a near-room-temperature superconductor.
There’s a new group of scientists who claim to have discovered a near-room-temperature superconductor, a claim that garnered much social media and tech nerds’ attention back in August 2023.
Continental and Aurora unveiled a four-year plan for scalable autonomous trucks, emphasizing collaboration, safety, and production readiness by 2027.
Joining forces, Continental and Aurora map out a 4-year journey to autonomous trucking, promising safety, scalability, and production in 2027.
A team of researchers has achieved a milestone by developing lightweight and highly efficient stretchable solar cells for energy generation in electronic gadgets.
Researchers from the Korea Advanced Institute of Science and Technology (KAIST) declare it as the “world’s highest-performing stretchable organic solar cell.”
The development is unique as it utilizes organic material to build the photoactive layer of the solar cell, which turns light into energy.
Chip likely designed for Samsung, Google mixed reality headset.
To delve into the technical specifications, Apple’s Vision Pro boasts an impressive resolution of 11.5 million pixels per eye, more than a 4K TV for each eye, with a total resolution of 23 million pixels.
Continue reading “Qualcomm unveils cutting-edge XR chipset to compete with Apple Vision Pro” »
What’s cooking in the AI labs of Google and Apple?
New rumors claim that Apple’s generative AI technology will be included in Siri not just locally on iPhones, but also integrated into other services. Whereas, Google will upgrade Bard with more ‘advanced’ capabilities.
face_with_colon_three year 2022.
One of the biggest concerns about EVs is that the batteries will need replacing after a few years, at great expense. After all, your smartphone battery is likely to have seen better days within as little as three years. But a Tesla researcher is getting ready to kick this idea into touch once and for all, after demonstrating batteries that could potentially outlive most human beings.
Tesla enthusiasts are likely to have heard of Jeff Dahn already. He’s a professor at Dalhousie University and has been a research partner with Tesla since 2016. His focus has been to increase the energy density and lifetime of lithium-ion batteries, as well as reducing their cost. Dahn appears to have hit the motherload along with colleagues on his research team. In a paper published in the Journal of the Electrochemical Society, the group claims to have created a battery design that could last 100 years under the right conditions.
Continue reading “Tesla Researcher Demonstrates 100-Year, 4-Million-Mile Battery” »
😀 They say we could even regenerate human limbs this way aswell as repair human blood vessels.
Cell tubes, made entirely from a patient’s own cells, are just as elastic as blood vessels but much stronger. Skin cells cultured into lumps are skewered on needles on a base, similar to a Kenzan, a tool used in Japanese flower arrangements, and formed into a tube. The technique, called the Kenzan Method, was made possible by a 3D bioprinter. A clinical trial is underway in Japan to transplant these tubes into humans in place of blood vessels. Studies are being done to apply them to nerves and organs.
The required precision to perform quantum simulations beyond the capabilities of classical computers imposes major experimental and theoretical challenges. The key to solving these issues are highly precise ways of characterizing analog quantum sim ulators. Here, we robustly estimate the free Hamiltonian parameters of bosonic excitations in a superconducting-qubit analog quantum simulator from measured time-series of single-mode canonical coordinates. We achieve the required levels of precision in estimating the Hamiltonian parameters by maximally exploiting the model structure, making it robust against noise and state-preparation and measurement (SPAM) errors. Importantly, we are also able to obtain tomographic information about those SPAM errors from the same data, crucial for the experimental applicability of Hamiltonian learning in dynamical quantum-quench experiments. Our learning algorithm is highly scalable both in terms of the required amounts of data and post-processing. To achieve this, we develop a new super-resolution technique coined tensorESPRIT for frequency extraction from matrix time-series. The algorithm then combines tensorESPRIT with constrained manifold optimization for the eigenspace reconstruction with pre-and post-processing stages. For up to 14 coupled superconducting qubits on two Sycamore processors, we identify the Hamiltonian parameters — verifying the implementation on one of them up to sub-MHz precision — and construct a spatial implementation error map for a grid of 27 qubits. Our results constitute a fully characterized, highly accurate implementation of an analog dynamical quantum simulation and introduce a diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.
Submitted 18 Aug 2021 to Quantum Physics [quant-ph]
Subjects: quant-ph cond-mat.quant-gas physics.comp-ph.
The required precision to perform quantum simulations beyond the capabilities of classical computers imposes major experimental and theoretical challenges. The key to solving these issues are highly precise ways of characterizing analog quantum sim ulators. Here, we robustly estimate the free Hamiltonian parameters of bosonic excitations in a superconducting-qubit analog quantum simulator from measured time-series of single-mode canonical coordinates. We achieve the required levels of precision in estimating the Hamiltonian parameters by maximally exploiting the model structure, making it robust against noise and state-preparation and measurement (SPAM) errors. Importantly, we are also able to obtain tomographic information about those SPAM errors from the same data, crucial for the experimental applicability of Hamiltonian learning in dynamical quantum-quench experiments. Our learning algorithm is highly scalable both in terms of the required amounts of data and post-processing. To achieve this, we develop a new super-resolution technique coined tensorESPRIT for frequency extraction from matrix time-series. The algorithm then combines tensorESPRIT with constrained manifold optimization for the eigenspace reconstruction with pre-and post-processing stages. For up to 14 coupled superconducting qubits on two Sycamore processors, we identify the Hamiltonian parameters — verifying the implementation on one of them up to sub-MHz precision — and construct a spatial implementation error map for a grid of 27 qubits. Our results constitute a fully characterized, highly accurate implementation of an analog dynamical quantum simulation and introduce a diagnostic toolkit for understanding, calibrating, and improving analog quantum processors.
