“Because nothing can protect hardware, software, applications or data from a quantum-enabled adversary, encryption keys and data will require re-encrypting with a quantum-resistant algorithm and deleting or physically securing copies and backups.” v/@preskil… See More.
To ease the disruption caused by moving away from quantum-vulnerable cryptographic code, NIST has released a draft document describing the first steps of that journey.
New research shows that there are problems even quantum computers might never be able to solve.
Physics-informed machine learning might help verify microchips.
Physicists love recreating the world in software. A simulation lets you explore many versions of reality to find patterns or to test possibilities. But if you want one that’s realistic down to individual atoms and electrons, you run out of computing juice pretty quickly.
Machine-learning models can approximate detailed simulations, but often require lots of expensive training data. A new method shows that physicists can lend their expertise to machine-learning algorithms, helping them train on a few small simulations consisting of a few atoms, then predict the behavior of system with hundreds of atoms. In the future, similar techniques might even characterize microchips with billions of atoms, predicting failures before they occur.
The researchers started with simulated units of 16 silicon and germanium atoms, two elements often used to make microchips. They employed high-performance computers to calculate the quantum-mechanical interactions between the atoms’ electrons. Given a certain arrangement of atoms, the simulation generated unit-level characteristics such as its energy bands, the energy levels available to its electrons. But “you realize that there is a big gap between the toy models that we can study using a first-principles approach and realistic structures,” says Sanghamitra Neogi, a physicist at the University of Colorado, Boulder, and the paper’s senior author. Could she and her co-author, Artem Pimachev, bridge the gap using machine learning?
Researchers at ETH Zurich have succeeded in observing a crystal that consists only of electrons. Such Wigner crystals were already predicted almost ninety years ago but could only now be observed directly in a semiconductor material.
Crystals have fascinated people through the ages. Who hasn’t admired the complex patterns of a snowflake at some point, or the perfectly symmetrical surfaces of a rock crystal? The magic doesn’t stop even if one knows that all this results from a simple interplay of attraction and repulsion between atoms and electrons. A team of researchers led by Ataç Imamoğlu, professor at the Institute for Quantum Electronics at ETH Zurich, have now produced a very special crystal. Unlike normal crystals, it consists exclusively of electrons. In doing so, they have confirmed a theoretical prediction that was made almost ninety years ago and which has since been regarded as a kind of holy grail of condensed matter physics. Their results were recently published in the scientific journal Nature.
Researchers at ETH Zurich have succeeded in observing a crystal that consists only of electrons. Such Wigner crystals were already predicted almost ninety years ago but could only now be observed directly in a semiconductor material.
Crystals have fascinated people through the ages. Who hasn’t admired the complex patterns of a snowflake at some point, or the perfectly symmetrical surfaces of a rock crystal? The magic doesn’t stop even if one knows that all this results from a simple interplay of attraction and repulsion between atoms and electrons. A team of researchers led by Ataç Imamoğlu, professor at the Institute for Quantum Electronics at ETH Zurich, have now produced a very special crystal. Unlike normal crystals, it consists exclusively of electrons. In doing so, they have confirmed a theoretical prediction that was made almost ninety years ago and which has since been regarded as a kind of holy grail of condensed matter physics. Their results were recently published in the scientific journal Nature.
A decades-old prediction
“What got us excited about this problem is its simplicity,” says Imamoğlu. Already in 1934 Eugene Wigner, one of the founders of the theory of symmetries in quantum mechanics, showed that electrons in a material could theoretically arrange themselves in regular, crystal-like patterns because of their mutual electrical repulsion. The reasoning behind this is quite simple: if the energy of the electrical repulsion between the electrons is larger than their motional energy, they will arrange themselves in such a way that their total energy is as small as possible.
Protocol for performing two-qubit entangling gates trades laser power for speed with little loss of fidelity.
Rigetti Computing, a California-based developer of quantum integrated circuits, has announced it is launching the world’s first multi-chip quantum processor.
The processor incorporates a proprietary modular architecture that accelerates the path to commercialization and solves key scaling challenges toward fault-tolerant quantum computers.
“We’ve developed a fundamentally new approach to scaling quantum computers,” says Chad Rigetti, founder and CEO of Rigetti Computing. “Our proprietary innovations in chip design and manufacturing have unlocked what we believe is the fastest path to building the systems needed to run practical applications and error correction.”
Quantum computing is coming on leaps and bounds. Now there’s an operating system available on a chip thanks to a Cambridge University-led consortia with a vision is make quantum computers as transparent and well known as RaspberryPi.
This “sensational breakthrough” is likened by the Cambridge Independent Press to the moment during the 1960s when computers shrunk from being room-sized to being sat on top of a desk.
Around 50 quantum computers have been built to date, and they all use different software – there is no quantum equivalent of Windows, IOS or Linux. The new project will deliver an OS that allows the same quantum software to run on different types of quantum computing hardware.
