The future is now as IBM unveils its comprehensive strategy to take quantum computing from noisy to useful. property= description.
Category: quantum physics – Page 495
Implications of the existence of a ‘conscious’ quantum particle.
I know this story is going to be weird in many ways but this is something worth thinking about. Theoretical physicist Richard Feynman once stated.
“Imagine how much harder physics would be if electrons had feelings.”
In a new paper in PNAS, “Triplet-Pair Spin Signatures From Macroscopically Aligned Heteroacenes in an Oriented Single Crystal,” National Renewable Energy Laboratory (NREL) researchers Brandon Rugg, Brian Fluegel, Christopher Chang, and Justin Johnson tackle one of the fundamental problems in quantum information science: how to produce pure elements of quantum information—that is, those that start and remain in a well-defined “spin state”—at practical temperatures.
Quantum information science has the potential to revolutionize computation, sensing, and communications. But many of these applications are still beyond reach because of the challenges of producing units of quantum information, or qubits, without relying on extremely low temperatures to maintain their purity. Current approaches to identifying suitable quantum materials tend to rely on trial and error.
“The field of developing new molecules and materials [for quantum information science] sometimes progresses through ad hoc methods and serendipity. ‘This material just so happens to work better than the other one’—we saw a lot of that happening, and decided ultimately that it was not going to suffice for a project where the goal was to limit the set of possible options,” said Justin Johnson, a researcher in NREL’s Chemistry and Nanoscience Center. “We wanted to have the theory provide us with firm guidelines about what should happen.”
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.