Lifeboat Foundation

Matter-wave interference experiments provide a direct confirmation of the quantum superposition principle, a hallmark of quantum theory, and thereby constrain possible modifications to quantum mechanics¹. By increasing the mass of the interfering particles and the macroscopicity of the superposition², more stringent bounds can be placed on modified quantum theories such as objective collapse models³. Here, we report interference of a molecular library of functionalized oligoporphyrins⁴ with masses beyond 25,000 Da and consisting of up to 2,000 atoms, by far the heaviest objects shown to exhibit matter-wave interference to date. We demonstrate quantum superposition of these massive particles by measuring interference fringes in a new 2-m-long Talbot–Lau interferometer that permits access to a wide range of particle masses with a large variety of internal states. The molecules in our study have de Broglie wavelengths down to 53 fm, five orders of magnitude smaller than the diameter of the molecules themselves. Our results show excellent agreement with quantum theory and cannot be explained classically. The interference fringes reach more than 90% of the expected visibility and the resulting macroscopicity value of 14.1 represents an order of magnitude increase over previous experiments².

Andreas heinrich director of the IBS center for quantum nanoscience and distinguished professor at ewha womans university

A team of researchers affiliated with several institutions in China has succeeded in sending entangled quantum memories over a 50-kilometer coiled fiber cable. In their paper published in the journal Nature, the group describes several experiments they conducted involving entangling quantum memory over long distances, the challenges they overcame, and problems still to be addressed.

Over the past several years, scientists have been working toward the development of a quantum internet—one very much the same as the present-day network, but with much stronger security. One such approach is based on the development of quantum keys that would allow parties to a private conversation to know that an interloper is eavesdropping, because doing so would change the state of the keys. But in such systems, measurements of the quantum state of the keys is required, which can be impacted by environmental conditions, making the approach nearly impractical.

Another approach involves using entangled particles to form a network—but this has proven to be difficult to implement because of the sensitivity of such particles and their short lifespan. But progress is being made. In this new effort, the researchers in China succeeded in entangling quantum memory between buildings 20 kilometers apart and across 50 kilometers of coiled cable in their lab.

All the elements are there to begin with, so to speak; it’s just a matter of figuring out what they are capable of—alone or together. For Leslie Schoop’s lab, one recent such investigation has uncovered a layered compound with a trio of properties not previously known to exist in one material.

With an international interdisciplinary team, Schoop, assistant professor of chemistry, and Postdoctoral Research Associate Shiming Lei, published a paper last week in Science Advances reporting that the van der Waals material gadolinium tritelluride (GdTe3) displays the highest electronic mobility among all known layered magnetic materials. In addition, it has magnetic order, and can easily be exfoliated.

Combined, these properties make it a promising candidate for new areas like magnetic twistronic devices and spintronics, as well as advances in data storage and device design.

“The discovery of dark energy has greatly changed how we think about the laws of nature,” said Edward Witten, creator of string theory and one of the world’s leading theoretical physicist at the Institute for Advanced Study in Princeton, N.J. who has been compared to Newton and Einstein.

One of the great known unknowns of the universe is the nature of dark energy, a force field making the universe expand faster. Current theories range from end-of-the universe scenarios to dark energy as the manifestation of advanced alien life.

A new, controversial theory suggests that this dark energy might be getting stronger and denser, leading to a future in which atoms are torn asunder and time ends.

Circa 2012

Data transmitted 1 km using elusive particles.

Newly created artificial atoms on a silicon chip could become the new basis for quantum computing.

Engineers in Australia have found a way to make these artificial atoms more stable, which in turn could produce more consistent quantum bits, or qubits — the basic units of information in a quantum system.

The research builds on previous work by the team, wherein they produced the very first qubits on a silicon chip, which could process information with over 99 percent accuracy. Now, they have found a way to minimise the error rate caused by imperfections in the silicon.

TrackML was a Kaggle competition in 2018 with $25 000 in cash prizes where the challenge was to reconstruct particle tracks from 3D points left in silicon detectors. CERN (the European Organization for Nuclear Research) provided data over particles collision events. The rate at which they occur over there is in the neighborhood of hundreds of millions of collisions per second, or tens of petabytes per year. There is a clear need to be as efficient as possible when sifting through such an amount of data, and this is where machine learning methods may be of help.

Particles, in this case protons, are boosted to high energies inside the Large Hadron Collider (LHC) — each beam can reach 6.5 TeV giving a total of 13 TeV when colliding. Electromagnetic fields are used to accelerate the electrically charged protons in a 27 kilometers long loop. When the proton beams collide they produce a diverse set of subatomic byproducts which quickly decay, holding valuable information for some of the most fundamental questions in physics.

Detectors are made of layers upon layers of subdetectors, each designed to look for specific particles or properties. There are calorimeters that measure energy, particle-identification detectors to pin down what kind of particle it is and tracking devices to calculate the path of a particle. [1] We are of course interested in the tracking, tiny electrical signals are recorded as particles move through those types of detectors. What I will discuss is methods to reconstruct these recorded patterns of tracks, specifically algorithms involving machine learning.

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