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Researchers at QuTech—a collaboration between the Delft University of Technology and TNO—have engineered a record number of six, silicon-based, spin qubits in a fully interoperable array. Importantly, the qubits can be operated with a low error-rate that is achieved with a new chip design, an automated calibration procedure, and new methods for qubit initialization and readout. These advances will contribute to a scalable quantum computer based on silicon. The results are published in Nature today.

Different materials can be used to produce qubits, the quantum analog to the bit of the classical computer, but no one knows which material will turn out to be best to build a large-scale quantum computer. To date there have only been smaller demonstrations of silicon quantum chips with high quality qubit operations. Now, researchers from QuTech, led by Prof. Lieven Vandersypen, have produced a six qubit chip in silicon that operates with low error-rates. This is a major step towards a fault-tolerant quantum computer using silicon.

To make the qubits, individual electrons are placed in a linear array of six “quantum dots” spaced 90 nanometers apart. The array of quantum dots is made in a silicon chip with structures that closely resemble the transistor—a common component in every computer chip. A quantum mechanical property called spin is used to define a qubit with its orientation defining the 0 or 1 logical state. The team used finely-tuned microwave radiation, magnetic fields, and electric potentials to control and measure the spin of individual electrons and make them interact with each other.

Most mass in everyday matter around us resides in protons and neutrons inside the atomic nucleus. However, the lifetime of a free neutron—one not bounded to a nucleus—is unstable, decaying by a process called beta decay. For neutrons, beta decay involves the emission of a proton, an electron, and an anti-neutrino. Beta decay is a common process.

However, scientists have some significant uncertainties about the neutron lifetime and about the neutron decaying inside a nucleus that leads to a proton emission. This is called beta-delayed proton emission. There are only a few neutron-rich nuclei for which beta-delayed proton emission is energetically allowed. The radioactive nucleus beryllium-11 (¹¹ Be), an isotope that consists of 4 protons and 7 neutrons, with its last neutron very weakly bound, is among those rare cases. Scientists recently observed a surprising large beta-delayed proton decay rate for ¹¹ Be. Their work is published in Physical Review Letters.

The discovery of an exotic near-threshold resonance that favors proton decay is a key for explaining the beta-delayed proton decay of ¹¹ Be. The discovery is also a remarkable and not fully understood manifestation of quantum many-body physics. Many-body physics involves interacting subatomic particles. While scientists may know the physics that apply to each particle, the complete system can be too complex to understand.

Have you ever been faced with a problem where you had to find an optimal solution out of many possible options, such as finding the quickest route to a certain place, considering both distance and traffic?

If so, the problem you were dealing with is what is formally known as a “combinatorial optimization problem.” While mathematically formulated, these problems are common in the real world and spring up across several fields, including logistics, network routing, machine learning, and materials science.

Continue reading “Scalable and fully coupled quantum-inspired processor solves optimization problems” »

Jülich researchers have been able to demonstrate an exotic electronic state, so-called Fermi Arcs, for the first time in a 2D material. The surprising appearance of Fermi arcs in such a material provides a link between novel quantum materials and their respective potential applications in a new generation of spintronics and quantum computing. The results have recently been published in Nature Communications.

The newly detected Fermi arcs represent special—arc-like—deviations from the so-called Fermi surface. The Fermi surface is used in condensed matter physics to describe the momentum distribution of electrons in a metal. Normally, these Fermi surfaces represent closed surfaces. Exceptions such as the Fermi arcs are very rare and often are associated with exotic properties like superconductivity, negative magnetoresistance and anomalous quantum transport effects.

Today’s technology challenge is to develop the “on-demand” control of physical properties in materials. However, such experimental tests have been largely limited to bulk materials and are key grand challenges in condensed matter science. With its groundbreaking paradigm, the findings present a promising new frontier for quantum control of topological states in low-dimensional systems by external means—the external magnetic field that offers unprecedented capabilities on 2D materials for artificial intelligence as well as future information processing.

PRESS RELEASE — There has been a lot of buzz about quantum computers and for good reason. The futuristic computers are designed to mimic what happens in nature at microscopic scales, which means they have the power to better understand the quantum realm and speed up the discovery of new materials, including pharmaceuticals, environmentally friendly chemicals, and more. However, experts say viable quantum computers are still a decade away or more. What are researchers to do in the meantime?

A new Caltech-led study in the journal Science describes how machine learning tools, run on classical computers, can be used to make predictions about quantum systems and thus help researchers solve some of the trickiest physics and chemistry problems. While this notion has been shown experimentally before, the new report is the first to mathematically prove that the method works.

“Quantum computers are ideal for many types of physics and materials science problems,” says lead author Hsin-Yuan (Robert) Huang, a graduate student working with John Preskill, the Richard P. Feynman Professor of Theoretical Physics and the Allen V. C. Davis and Lenabelle Davis Leadership Chair of the Institute for Quantum Science and Technology (IQIM). “But we aren’t quite there yet and have been surprised to learn that classical machine learning methods can be used in the meantime. Ultimately, this paper is about showing what humans can learn about the physical world.”

Physicists at UC Santa Barbara, the University of Maryland, and the University of Washington have found an answer to the longstanding physics question: How do interparticle interactions affect dynamical localization?

“It’s a really old question inherited from condensed matter physics,” said David Weld, an experimental physicist at UCSB with specialties in ultracold atomic physics and quantum simulation. The question falls into the category of “many-body” physics, which interrogates the physical properties of a quantum system with multiple interacting parts. While many-body problems have been a matter of research and debate for decades, the complexity of these systems, with quantum behaviors such as superposition and entanglement, lead to multitudes of possibilities, making it impossible to solve through calculation alone. “Many aspects of the problem are beyond the reach of modern computers,” Weld added.

Fortunately, this problem was not beyond the reach of an experiment that involves ultracold lithium atoms and lasers. So, what emerges when you introduce interaction in a disordered, chaotic quantum system? A “weird quantum state,” according to Weld. “It’s a state which is anomalous, with properties which in some sense lie between the classical prediction and the non-interacting quantum prediction.”

In the future, many computers will most likely be based on electronic circuits made of superconductors. These are materials through which an electrical current can flow without energy losses, could be very promising for the development of high-performance supercomputers and quantum computers.

Researchers at University of California Santa Barbara, Raytheon BBN Technologies, University of Cagliari, Microsoft Research, and the Tokyo Institute of Technology have recently developed a magneto-optic modulator—a device that control the properties of a light beam through a magnetic field. This device, introduced in a paper published in Nature Electronics, could contribute to the implementation of large-scale electronics and computers based on superconductors.

“We are working on a new technology that can speed up high-performance supercomputers and quantum computers based on superconductor technology,” Paolo Pintus, the researcher who led the study, told TechXplore. “Superconductors work properly only at low temperatures, generally just above absolute zero (−273.15° Celsius). Because of this, circuits made of these materials must be kept inside a dedicated refrigerator.”

Using artificial intelligence, physicists have compressed a daunting quantum problem that until now required 100,000 equations into a bite-size task of as few as four equations—all without sacrificing accuracy. The work, published in the September 23 issue of Physical Review Letters, could revolutionize how scientists investigate systems containing many interacting electrons. Moreover, if scalable to other problems, the approach could potentially aid in the design of materials with sought-after properties such as superconductivity or utility for clean energy generation.

“We start with this huge object of all these coupled-together differential equations; then we’re using machine learning to turn it into something so small you can count it on your fingers,” says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute’s Center for Computational Quantum Physics (CCQ) in New York City and an assistant professor at the University of Bologna in Italy.

The formidable problem concerns how electrons behave as they move on a gridlike lattice. When two electrons occupy the same lattice site, they interact. This setup, known as the Hubbard model, is an idealization of several important classes of materials and enables scientists to learn how electron behavior gives rise to sought-after phases of matter, such as superconductivity, in which electrons flow through a material without resistance. The model also serves as a testing ground for new methods before they’re unleashed on more complex quantum systems.

Quantum computers have the potential to vastly exceed the capabilities of conventional computers for certain tasks. But there is still a long way to go before they can help to solve real-world problems. Many applications require quantum processors with millions of quantum bits. Today’s prototypes merely come up with a few of these compute units.

“Currently, each individual qubit is connected via several signal lines to control units about the size of a cupboard. That still works for a few qubits. But it no longer makes sense if you want to put millions of qubits on the chip. Because that’ s necessary for quantum error correction,” says Dr. Lars Schreiber from the JARA Institute for Quantum Information at Forschungszentrum Jülich and RWTH Aachen University.

At some point, the number of signal lines becomes a bottleneck. The lines take up too much space compared to the size of the tiny qubits. And a quantum chip cannot have millions of inputs and outputs—a modern classical chip only contains about 2,000 of these. Together with colleagues at Forschungszentrum Jülich and RWTH Aachen University, Schreiber has been conducting research for several years to find a solution to this problem.