Lifeboat Foundation

But, he added, the method took about 10 minutes to produce 1,024 random strings, whereas current cryptographic processes would need far faster number generators.

The new technique’s first real-world use will come when it’s incorporated into NIST’s randomness beacon, a public source of randomness for researchers studying unpredictability, Bierhorst said.

But he added that he hopes the experimental setup could one day be shrunk enough to fit on a computer chip and help in the creation of “unhackable” messages.

In 2019, the MAGIC telescopes detected the first Gamma Ray Burst at very high energies. This was the most intense gamma-radiation ever obtained from such a cosmic object. But the GRB data have more to offer: with further analyses, the MAGIC scientists could now confirm that the speed of light is constant in vacuum — and not dependent on energy. So, like many other tests, GRB data also corroborate Einstein’s theory of General Relativity. The study has now been published in Physical Review Letters.

Einstein’s general relativity (GR) is a beautiful theory that explains how mass and energy interact with space-time, creating a phenomenon commonly known as gravity. GR has been tested and retested in various physical situations and over many different scales, and, postulating that the speed of light is constant, it always turned out to outstandingly predict the experimental results. Nevertheless, physicists suspect that GR is not the most fundamental theory, and that there might exist an underlying quantum mechanical description of gravity, referred to as quantum gravity (QG).

Some QG theories consider that the speed of light might be energy dependent. This hypothetical phenomenon is called Lorentz invariance violation (LIV). Its effects are thought to be too tiny to be measured, unless they are accumulated over a very long time. So how to achieve that? One solution is using signals from astronomical sources of gamma rays. Gamma-ray bursts (GRBs) are powerful and far away cosmic explosions, which emit highly variable, extremely energetic signals. They are thus excellent laboratories for experimental tests of QG. The higher energy photons are expected to be more influenced by the QG effects, and there should be plenty of those; these travel billions of years before reaching Earth, which enhances the effect.

face_with_colon_three circa 2019.

Invisible to the eye, the dots glow under infrared light from modified smartphones.

New ground-breaking research from the University of Surrey could change the way scientists understand and describe lasers—establishing a new relationship between classical and quantum physics.

In a comprehensive study published by the journal Progress in Quantum Electronics, a researcher from Surrey, in partnership with a colleague from Karlsruhe Institute of Technology and Fraunhofer IOSB in Germany, calls into question 60 years of orthodoxy surrounding the principles of lasers and the laser spectral linewidth—the foundation for controlling and measuring wavelengths of light.

In the new study, the researchers find that a fundamental principle of lasers, that the amplification of light compensates for the losses of the laser, is only an approximation. The team quantify and explain that a tiny excess loss, which is not balanced by the amplified light but by normal luminescence inside the laser, provides the answer to the spectral linewidth of the laser.

Researchers at MIT have developed a process to manufacture and integrate “artificial atoms” with photonic circuitry, and in doing so, are able to produce the largest quantum chip of its kind.

The atoms, which are created by atomic-scale defects in microscopically thin slices of diamond, allow for the scaling up of quantum chip production.

RELATED: 7 REASONS WHY WE SHOULD BE EXCITED BY QUANTUM COMPUTERS

Last week the President Council of Advisors on Science and Technology (PCAST) met (webinar) to review policy recommendations around three sub-committee reports: 1) Industries of the Future (IotF), chaired be Dario Gil (director of research, IBM); 2) Meeting STEM Education and Workforce Needs, chaired by Catherine Bessant (CTO, Bank of America), and 3) New Models of Engagement for Federal/National Laboratories in the Multi-Sector R&D Enterprise, chaired by Dr. A.N. Sreeram (SVP, CTO, Dow Corp.)

Yesterday, the full report (Recommendations For Strengthening American Leadership In Industries Of The Future) was issued and it is fascinating and wide-ranging. To give you a sense of the scope, here are three highlights taken from the executive summary of the full report:

Army researchers developed a new way to protect and safeguard quantum information, moving quantum networks a step closer to reality.

Quantum information science is a rapidly growing interdisciplinary field exploring new ways of storing, manipulating and communicating information. Researchers want to create powerful computational capabilities using new hardware that operates on quantum physics principles.

For the Army, the new quantum paradigms could potentially lead to transformational capabilities in fast, efficient and secure collecting, exchanging and processing vast amounts of information on dynamic battlefields of the future.

Researchers at the Army Research Laboratory have developed a new method to protect and safeguard quantum information, moving quantum networks a step closer to reality.

Quantum information science is a rapidly growing interdisciplinary field exploring new ways of storing, manipulating and communicating information. Researchers aim to create powerful computational capabilities using new hardware that operates on quantum physics principles.

For the army, these new quantum paradigms could potentially lead to transformational capabilities in fast, efficient and secure collecting, exchanging and processing vast amounts of information on dynamic battlefields in the future.

Quantum information scientists have introduced a new method for machine learning classifications in quantum computing. The non-linear quantum kernels in a quantum binary classifier provide new insights for improving the accuracy of quantum machine learning, deemed able to outperform the current AI technology.

The research team led by Professor June-Koo Kevin Rhee from the School of Electrical Engineering, proposed a quantum classifier based on quantum state fidelity by using a different initial state and replacing the Hadamard classification with a swap test. Unlike the conventional approach, this method is expected to significantly enhance the classification tasks when the training dataset is small, by exploiting the quantum advantage in finding non-linear features in a large feature space.

Quantum machine learning holds promise as one of the imperative applications for quantum computing. In machine learning, one fundamental problem for a wide range of applications is classification, a task needed for recognizing patterns in labeled training data in order to assign a label to new, previously unseen data; and the kernel method has been an invaluable classification tool for identifying non-linear relationships in complex data.