Researchers at the SketchX, University of Surrey have recently developed a meta learning-based model that allows users to retrieve images of specific items simply by sketching them on a tablet, smartphone, or on other smart devices. This framework was outlined in a paper set to be presented at the European Conference on Computer Vision (ECCV), one of the top three flagship computer vision conferences along with CVPR and ICCV.
Particles can move as waves along different paths at the same time—this is one of the most important findings of quantum physics. A particularly impressive example is the neutron interferometer: neutrons are fired at a crystal, the neutron wave is split into two portions, which are then superimposed on each other again. A characteristic interference pattern can be observed, which proves the wave properties of matter.
Such neutron interferometers have played an important role for precision measurements and fundamental physics research for decades. However, their size has been limited so far because they worked only if carved from a single piece of crystal. Since the 1990s, attempts have also been made to produce interferometers from two separate crystals—but without success. Now a team from TU Wien, INRIM Turin and ILL Grenoble has achieved precisely this feat, using a high-precision tip-tilt platform for the crystal alignment. This opens up completely new possibilities for quantum measurements, including research on quantum effects in a gravitational field.
Commercial scale solid-state batteries for EVs are a few years away. QuantumScape’s valuation has historically been detached from reality but has declined 92% from all-time highs.
Hypothetical interstellar civilizations could employ such signals for lossless long-distance communication.
A new technique to measure vibrating atoms could improve the precision of atomic clocks and of quantum sensors for detecting dark matter or gravitational waves.
Gravitational waves are distortions or ripples in the fabric of space and time. They were first detected in 2015 by the Advanced LIGO detectors and are produced by catastrophic events such as colliding black holes, supernovae, or merging neutron stars.
A machine-learning algorithm that includes a quantum circuit generates realistic handwritten digits and performs better than its classical counterpart.
Machine learning allows computers to recognize complex patterns such as faces and also to create new and realistic-looking examples of such patterns. Working toward improving these techniques, researchers have now given the first clear demonstration of a quantum algorithm performing well when generating these realistic examples, in this case, creating authentic-looking handwritten digits [1]. The researchers see the result as an important step toward building quantum devices able to go beyond the capabilities of classical machine learning.
The most common use of neural networks is classification—recognizing handwritten letters, for example. But researchers increasingly aim to use algorithms on more creative tasks such as generating new and realistic artworks, pieces of music, or human faces. These so-called generative neural networks can also be used in automated editing of photos—to remove unwanted details, such as rain.
The quantum vibrations in atoms hold a miniature world of information. If scientists can accurately measure these atomic oscillations, and how they evolve over time, they can hone the precision of atomic clocks as well as quantum sensors, which are systems of atoms whose fluctuations can indicate the presence of dark matter, a passing gravitational wave, or even new, unexpected phenomena.
A major hurdle in the path toward better quantum measurements is noise from the classical world, which can easily overwhelm subtle atomic vibrations, making any changes to those vibrations devilishly hard to detect.
Now, MIT physicists have shown they can significantly amplify quantum changes in atomic vibrations, by putting the particles through two key processes: quantum entanglement and time reversal.
Before quantum computers and quantum networks can fulfil their huge potential, scientists have got several difficult problems to overcome – but a new study outlines a potential solution to one of these problems.
As we’ve seen in recent research, the silicon material that our existing classical computing components are made out of has shown potential for storing quantum bits, too.
These quantum bits – or qubits – are key to next-level quantum computing performance, and they come in a variety of types.
Reimagining Nuclear Medicine — Dr. Stephen Moran, Ph.D., Global Program Head, Neuroendocrine Tumors & Other Radiosensitive Cancers, Advanced Accelerator Applications, Novartis
Dr. Stephen Moran, Ph.D., is Global Program Head, Neuroendocrine Tumors & Other Radiosensitive Cancers, for Advanced Accelerator Applications (AAA — https://www.adacap.com/), a Novartis company and also a member of the Oncology Development Unit Leadership Team at Novartis.
Prior to joining AAA, Dr. Moran was Global Head of Novartis Strategy, where he played a key role in defining the company’s strategy, prioritizing critical actions needed to deliver on the mission to discover new ways to extend and improve peoples’ lives. He also led numerous strategic initiatives, including gene therapy (AveXis, now Novartis Gene Therapies), RNA therapeutics (The Medicines Company), precision medicine and digital strategies.
