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Scientific knowledge can progress rapidly, yet its social, economic, and political impacts often unfold at a painstakingly slow pace. The medicine of the 21st century draws upon genetic and embryological breakthroughs of the 19th century. Our current technology is firmly grounded in quantum physics, which was formulated a century ago. And the topic of the day, artificial intelligence (AI), traces its origins to the secret weapons research during World War II.

‌In 1935, the brilliant British mathematician, Alan Turing, envisioned a conceptual computer. His genius would later lead him to crack the Enigma code used by German submarines for secret communications during the war. Turing’s contributions extended beyond cryptography, as he introduced fundamental concepts of AI, including the training of artificial neural networks. Benedict Cumberbatch portrayed Turing in the 2014 film The Imitation Game, which earned a screenplay Oscar that year. All this historical context brings us to the heart of the current AI revolution.

‌AI uses neural networks, also known as artificial neural networks, which are comprised of multiple layers of artificial neurons. Each neuron receives numerous inputs from the lower layer and produces a single output to the upper layer, similar to the dendrites and axon of natural neurons. As information progresses through each layer, it gradually becomes more abstract, resembling the process that occurs in the visual cortex of our brains.

Ever think you’d see a single atom without staring down the barrel of a powerful microscope? Oxford University physicist David Nadlinger has won the top prize in the fifth annual Engineering and Physical Sciences Research Council’s (EPSRC) national science photography competition for his image ‘Single Atom in an Ion Trap’, which does something incredible: makes a single atom visible to the human eye.

Click image to zoom. Photo: David Nadlinger/EPSRC

Captured on an ordinary digital camera, the image shows an atom of strontium suspended by electric fields emanating from the metal electrodes of an ion trap—those electrodes are about 2mm apart. Nadlinger shot the photo through the window of the ultra-high vacuum chamber that houses the ion trap, which is used to explore the potential of laser-cooled atomic ions in new applications such as highly accurate atomic clocks and sensors, and quantum computing.

One of the greatest challenges of modern physics is to find a coherent method for describing phenomena, on the cosmic and microscale. For over a hundred years, to describe reality on a cosmic scale we have been using general relativity theory, which has successfully undergone repeated attempts at falsification.

Albert Einstein curved space-time to describe gravity, and despite still-open questions about dark matter or dark energy, it seems, today, to be the best method of analyzing the past and future of the universe.

To describe phenomena on the scale of atoms, we use the second great theory: quantum mechanics, which differs from general relativity in basically everything. It uses flat space-time and a completely different mathematical apparatus, and most importantly, perceives reality radically differently.

Scientists are exploring the potential of quantum machine learning. But whether there are useful applications for the fusion of artificial intelligence and quantum computing is unclear.

Governor Kristi Noem has proposed a $6 million investment in a new Center for Quantum Information Science & Technology (C-QIST) in her recommended budget for the upcoming fiscal year.

According to details from the proposed budget, the center, a collaborative effort between Dakota State University, South Dakota School of Mines & Technology, South Dakota State University, and the University of South Dakota, aims to position the state as a leader in this emerging field with the potential to revolutionize everything from national security to healthcare.

The governor mentioned the potential of quantum computers to solve intractable problems as a reason for pursuing a quantum computing center, according to South Dakota Searchlight.

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.

One year after initial deliveries of solid-state battery prototypes to its automotive partners, QuantumScape is receiving additional praise from PowerCo – the battery-centric subsidiary of Volkswagen Group – for the potential of its technology. PowerCo recently completed an endurance test with QuantumScape’s solid-state cells and determined they can someday power EVs that can drive 500,000 kilometers with virtually no loss of range.

QuantumScape ($QS) is an advanced battery technology company that has been working for over a decade to develop scalable, energy-dense solid-state battery cells that can one-day power EVs that are safer, charge faster, and drive farther.

During QuantumScape’s tenure in solid-state battery development, Volkswagen Group has been a partner from early on and remains one of the startup’s largest investors. OEMs like Volkswagen have helped empower QuantumScape to continue its development and deliver some of the most promising solid-state battery technology in the industry.

Ultra-intense ultrashort lasers have a wide-ranging scope of applications, encompassing basic physics, national security, industrial service, and health care. In basic physics, such lasers have become a powerful tool for researching strong-field laser physics, especially for laser-driven radiation sources, laser particle acceleration, vacuum quantum electrodynamics, and more.

A dramatic increase in peak laser power, from the 1996 1-petawatt “Nova” to the 2017 10-petawatt “Shanghai Super-intense Ultrafast Laser Facility” (SULF) and the 2019 10-petawatt “Extreme Light Infrastructure—Nuclear Physics” (ELI-NP), is due to a shift in gain medium for large-aperture lasers (from neodymium-doped glass to titanium:sapphire crystal). That shift reduced the pulse duration of high-energy lasers from around 500 femtoseconds (fs) to around 25 fs.

However, the upper limit for titanium: sapphire ultra-intense ultrashort lasers appears to be 10-petawatt. Presently, for 10-petawatt to 100-petawatt development planning, researchers generally abandon the titanium: sapphire chirped pulse amplification technology, and turn to optical parametric chirped pulse amplification technology, based on deuterated potassium dihydrogen phosphate nonlinear crystals. That technology, due to its low pump-to-signal conversion efficiency and poor spatiotemporal-spectral-energy stability, will pose a great challenge for the realization and application of the future 10–100 petawatt lasers.