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The quantum properties underlying crystal formation can be replicated and investigated with the help of ultracold atoms. A team led by Dr. Axel U. J. Lode from the University of Freiburg’s Institute of Physics has now described in the journal Physical Review Letters how the use of dipolar atoms enables even the realization and precise measurement of structures that have not yet been observed in any material. The theoretical study was a collaboration involving scientists from the University of Freiburg, the University of Vienna and the Technical University of Vienna in Austria, and the Indian Institute of Technology in Kanpur, India.

Crystals are ubiquitous in nature. They are formed by many different materials—from mineral salts to heavy metals like bismuth. Their structures emerge because a particular regular ordering of atoms or molecules is favorable, because it requires the smallest amount of energy. A cube with one constituent on each of its eight corners, for instance, is a that is very common in nature. A crystal’s determines many of its , such as how well it conducts a current or heat or how it cracks and behaves when it is illuminated by light. But what determines these crystal structures? They emerge as a consequence of the of and the interactions between their constituents, which, however, are often scientifically hard to understand and also hard measure.

To nevertheless get to the bottom of the quantum properties of the formation of crystal structures, scientists can simulate the process using Bose-Einstein condensates—trapped ultracold atoms cooled down to temperatures close to absolute zero or minus 273.15 degrees Celsius. The atoms in these highly artificial and highly fragile systems are extremely well under control.

DARPA announces a new type of cryptography to protect the Big Tech firm profits from the dawn of quantum computers and allow backdoor access into 3 trillion internet-connected devices.

by Raul Diego

The U.S. Military-Industrial complex is sprinting on a chariot to shore up the encryption space before the next era of computation upends the entire digital edifice built on semiconductors and transistors. But, the core of the effort is protecting markets for Big Tech and all of its tentacles, which stand to lose years or even decades of profits should the new tech be rolled out too quickly.

Recent advancements in quantum computing have driven the scientific community’s quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve the efficiency with which quantum computers can solve these problems, scientists are investigating the use of artificial intelligence approaches.

In a new study, scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have developed a based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications.

“Combinatorial optimization problems are those for which the solution space gets exponentially larger as you expand the number of decision variables,” said Argonne scientist Prasanna Balaprakash. “In one traditional example, you can find the shortest route for a salesman who needs to visit a few cities once by enumerating all possible routes, but given a couple thousand cities, the number of possible routes far exceeds the number of stars in the universe; even the fastest supercomputers cannot find the shortest route in a reasonable time.”

A team of researchers with Google’s AI Quantum team (working with unspecified collaborators) has conducted the largest chemical simulation on a quantum computer to date. In their paper published in the journal Science, the group describes their work and why they believe it was a step forward in quantum computing. Xiao Yuan of Stanford University has written a Perspective piece outlining the potential benefits of quantum computer use to conduct chemical simulations and the work by the team at AI Quantum, published in the same journal issue.

Developing an ability to predict by simulating them on computers would be of great benefit to chemists—currently, they do most of it through trial and error. Prediction would open up the door to the development of a wide range of new materials with still unknown properties. Sadly, current computers lack the exponential scaling that would be required for such work. Because of that, chemists have been hoping quantum computers will one day step in to take on the role.

Current quantum computer technology is not yet ready to take on such a challenge, of course, but computer scientists are hoping to get them there sometime in the near future. In the meantime, big companies like Google are investing in research geared toward using quantum computers once they mature. In this new effort, the team at AI Quantum focused their efforts on simulating a simple chemical process—the Hartree-Fock approximation of a real system—in this particular case, a diazene molecule undergoing a reaction with hydrogen atoms, resulting in an altered configuration.

Accurate computational prediction of chemical processes from the quantum mechanical laws that govern them is a tool that can unlock new frontiers in chemistry, improving a wide variety of industries. Unfortunately, the exact solution of quantum chemical equations for all but the smallest systems remains out of reach for modern classical computers, due to the exponential scaling in the number and statistics of quantum variables. However, by using a quantum computer, which by its very nature takes advantage of unique quantum mechanical properties to handle calculations intractable to its classical counterpart, simulations of complex chemical processes can be achieved. While today’s quantum computers are powerful enough for a clear computational advantage at some tasks, it is an open question whether such devices can be used to accelerate our current quantum chemistry simulation techniques.

In “Hartree-Fock on a Superconducting Qubit Quantum Computer”, appearing today in Science, the Google AI Quantum team explores this complex question by performing the largest chemical simulation performed on a quantum computer to date. In our experiment, we used a noise-robust variational quantum eigensolver (VQE) to directly simulate a chemical mechanism via a quantum algorithm. Though the calculation focused on the Hartree-Fock approximation of a real chemical system, it was twice as large as previous chemistry calculations on a quantum computer, and contained ten times as many quantum gate operations. Importantly, we validate that algorithms being developed for currently available quantum computers can achieve the precision required for experimental predictions, revealing pathways towards realistic simulations of quantum chemical systems.

Berkeley Lab-led center to catalyze U.S. leadership in quantum information science, and strengthen the nation’s research community to accelerate commercialization.

The Department of Energy (DOE) has awarded $115 million over five years to the Quantum Systems Accelerator (QSA), a new research center led by Lawrence Berkeley National Laboratory (Berkeley Lab) that will forge the technological solutions needed to harness quantum information science for discoveries that benefit the world. It will also energize the nation’s research community to ensure U.S. leadership in quantum R&D and accelerate the transfer of quantum technologies from the lab to the marketplace. Sandia National Laboratories is the lead partner of the center.

Total planned funding for the center is $115 million over five years, with $15 million in Fiscal Year 2020 dollars and outyear funding contingent on congressional appropriations. The center is one of five new Department of Energy Quantum Information Science (QIS) Research Centers announced today (August 26, 2020).

🤔 “The White House today detailed the establishment of 12 new research institutes focused on AI and quantum information science. Agencies including the National Science Foundation (NSF), U.S. Department of Homeland Security, and U.S. Department of Energy (DOE) have committed to investing tens of millions of dollars in centers intended to serve as nodes for AI and quantum computing study.

Laments over the AI talent shortage in the U.S. have become a familiar refrain. While higher education enrollment in AI-relevant fields like computer science has risen rapidly in recent years, few colleges have been able to meet student demand due to a lack of staffing. In June, the Trump administration imposed a ban on U.S. entry for workers on certain visas — including for high-skilled H-1B visa holders, an estimated 35% of whom have an AI-related degree — through the end of the year. And Trump has toyed with the idea of suspending the Optional Practical Training program, which allows international students to work for up to three years in the U.S.”


The White House announced the creation of AI and quantum research institutes funded by billions in venture and taxpayer dollars.

It’s extremely difficult to make a fair comparison of US and Chinese spend on technology like AI as funding and research in this area is diffuse. Although China announced ambitious plans to become the world leader in AI by 2030, America still outspends the country in military funding (which increasingly includes AI research), while US tech companies like Google and Microsoft remain world leaders in artificial intelligence.

The Trump administration will likely present today’s news as a counterbalance to its dismal reputation for supporting scientific research. For four years in a row, government budgets have proposed broad cuts for federal research, including work in pressing subjects like climate change. Only the fields of artificial intelligence and quantum computing, with their overt links to military prowess and global geopolitics, have seen increased investment.

“It is absolutely imperative the United States continues to lead the world in AI and quantum,” said US Chief Technology Officer Michael Kratsios ahead of today’s announcement, according to The Wall Street Journal. “The future of American economic prosperity and national security will be shaped by how we invest, research, develop and deploy these cutting edge technologies today.”

Clarke urges other companies to also get ready now by investing in developing a quantum-ready workforce. “Quantum computing requires a specialized workforce, expertise that is pretty rare today,” he says. Clarke also advises companies to work with government agencies that are sponsoring quantum computing experiments and to fund quantum research in universities. He also supports nation-wide initiatives to spread the word all the way down the education system, even to high-school students, “so people aren’t scared or intimidated by the word quantum.”


Intel aims to achieve quantum practicality—commercially-viable quantum computing—by the end of this decade.

For the first time, researchers have designed a fully connected 32-qubit trapped-ion quantum computer register operating at cryogenic temperatures. The new system represents an important step toward developing practical quantum computers.

Junki Kim from Duke University will present the new hardware design at the inaugural OSA Quantum 2.0 conference to be co-located as an all-virtual event with OSA Frontiers in Optics and Laser Science APS/DLS (FiO + LS) conference 14—17 September.

Instead of using traditional bits that can only be a zero or a one, quantum computers use qubits that can be in a superposition of computational states. This allows quantum computers to solve problems that are too complex for traditional computers.