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Archive for the ‘quantum physics’ category

Sep 30, 2022

A computational shortcut for neural networks

Posted by in categories: information science, mathematics, quantum physics, robotics/AI

Neural networks are learning algorithms that approximate the solution to a task by training with available data. However, it is usually unclear how exactly they accomplish this. Two young Basel physicists have now derived mathematical expressions that allow one to calculate the optimal solution without training a network. Their results not only give insight into how those learning algorithms work, but could also help to detect unknown phase transitions in physical systems in the future.

Neural networks are based on the principle of operation of the brain. Such computer algorithms learn to solve problems through repeated training and can, for example, distinguish objects or process spoken language.

For several years now, physicists have been trying to use to detect as well. Phase transitions are familiar to us from everyday experience, for instance when water freezes to ice, but they also occur in more complex form between different phases of magnetic materials or , where they are often difficult to detect.

Sep 30, 2022

For the longest time: Quantum computing engineers set new standard in silicon chip performance

Posted by in categories: computing, quantum physics

Two milliseconds—or two thousandths of a second—is an extraordinarily long time in the world of quantum computing. On these timescales the blink of an eye—at one 10th of a second—is like an eternity.

Now a team of researchers at UNSW Sydney has broken new ground in proving that ‘spin qubits’—properties of electrons representing the basic units of information in quantum computers—can hold information for up to two milliseconds. Known as ‘coherence time’, the duration of time that qubits can be manipulated in increasingly complicated calculations, the achievement is 100 times longer than previous benchmarks in the same .

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Sep 29, 2022

Breakthrough Prize for the Physics of Quantum Information…and of Cells

Posted by in categories: bioengineering, biotech/medical, genetics, information science, nanotechnology, quantum physics, robotics/AI

This year’s Breakthrough Prize in Life Sciences has a strong physical sciences element. The prize was divided between six individuals. Demis Hassabis and John Jumper of the London-based AI company DeepMind were awarded a third of the prize for developing AlphaFold, a machine-learning algorithm that can accurately predict the 3D structure of proteins from just the amino-acid sequence of their polypeptide chain. Emmanuel Mignot of Stanford University School of Medicine and Masashi Yanagisawa of the University of Tsukuba, Japan, were awarded for their work on the sleeping disorder narcolepsy.

The remainder of the prize went to Clifford Brangwynne of Princeton University and Anthony Hyman of the Max Planck Institute of Molecular Cell Biology and Genetics in Germany for discovering that the molecular machinery within a cell—proteins and RNA—organizes by phase separating into liquid droplets. This phase separation process has since been shown to be involved in several basic cellular functions, including gene expression, protein synthesis and storage, and stress responses.

The award for Brangwynne and Hyman shows “the transformative role that the physics of soft matter and the physics of polymers can play in cell biology,” says Rohit Pappu, a biophysicist and bioengineer at Washington University in St. Louis. “[The discovery] could only have happened the way it did: a creative young physicist working with an imaginative cell biologist in an ecosystem where boundaries were always being pushed at the intersection of multiple disciplines.”

Sep 29, 2022

Going Beyond Fermi’s Golden Rule

Posted by in categories: evolution, particle physics, quantum physics

Researchers have calculated the likelihood that a quantum state will decay when its evolution is inhibited by a dearth of final states.

Quantum systems are fragile, meaning a specific quantum state generally decays into other states over time. This decay process is formalized by Fermi’s golden rule (FGR), which in its traditional formalization applies when there exists an infinite continuum of states for the quantum system state to decay to—for example, when an excited atom emits a photon into a vacuum. Now Tobias Micklitz at the Brazilian Center for Research in Physics and colleagues have developed and solved a model showing how a quantum system evolves when its initial state is instead coupled to a finite set of states spread across discrete energy levels [1]. Micklitz says that their model could be the foundation for models of more complex, many-body quantum systems.

FGR-obeying systems occupy one end of a scale, where the coupling strength between the systems’ initial and final states is large relative to the energy gap between the various final states (zero for a continuum of states). At the other end of the scale, the coupling strength is much lower relative to this gap. A system that sits in this second regime remains in its initial state, as there are too few available final states for it to decay into.

Sep 28, 2022

Engineering robust and scalable molecular qubits

Posted by in categories: biological, computing, engineering, particle physics, quantum physics

The concept of “symmetry” is essential to fundamental physics: a crucial element in everything from subatomic particles to macroscopic crystals. Accordingly, a lack of symmetry—or asymmetry—can drastically affect the properties of a given system.

Qubits, the quantum analog of computer bits for quantum computers, are extremely sensitive—the barest disturbance in a qubit system is enough for it to lose any it might have carried. Given this fragility, it seems intuitive that would be most stable in a symmetric environment. However, for a certain type of qubit—a molecular qubit—the opposite is true.

Researchers from the University of Chicago’s Pritzker School of Molecular Engineering (PME), the University of Glasgow, and the Massachusetts Institute of Technology have found that molecular qubits are much more stable in an asymmetric environment, expanding the possible applications of such qubits, especially as biological quantum sensors.

Sep 28, 2022

Full control of a six-qubit quantum processor in silicon

Posted by in categories: computing, quantum physics, robotics/AI

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 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 “” 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.

Sep 28, 2022

Near-threshold resonance helps explain a controversial measurement of exotic decay in beryllium-11

Posted by in categories: particle physics, quantum physics

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 and 7 , 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 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 . While scientists may know the physics that apply to each particle, the complete system can be too complex to understand.

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Sep 28, 2022

Scalable and fully coupled quantum-inspired processor solves optimization problems

Posted by in categories: particle physics, quantum physics, robotics/AI

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 .

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Sep 27, 2022

Exotic electronic effect found in 2D topological material

Posted by in categories: particle physics, quantum physics, robotics/AI

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 that offers unprecedented capabilities on 2D materials for as well as future information processing.

Sep 27, 2022

Caltech-led Research Team Finds Traditional Computers Can Solve Some Quantum Problems

Posted by in categories: chemistry, quantum physics, robotics/AI

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.”

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