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Quantum computers could make modern day Macs look like the very first Commodore computer.

Initial tests on Google and NASA’s quantum computing system D-Wave showed that it was a staggering one hundred million times faster than a traditional desktop.

Hartmut Nevan, director of engineering at Google, claimed: “What a D-Wave does in a second would take a conventional computer 10000 years to do.”

A Harvard-led team has created a 256-qubit programmable quantum simulator that represents the cutting edge in the world-wide quantum race.

A team of physicists from the Harvard-MIT Center for Ultracold Atoms and other universities has developed a special type of quantum computer known as a programmable quantum simulator capable of operating with 256 quantum bits, or “qubits.”

The system marks a major step toward building large-scale quantum machines that could be used to shed light on a host of complex quantum processes and eventually help bring about real-world breakthroughs in material science, communication technologies, finance, and many other fields, overcoming research hurdles that are beyond the capabilities of even the fastest supercomputers today. Qubits are the fundamental building blocks on which quantum computers run and the source of their massive processing power.

“This moves the field into a new domain where no one has ever been to thus far,” said Mikhail Lukin, the George Vasmer Leverett Professor of Physics, co-director of the Harvard Quantum Initiative, and one of the senior authors of the study published today in the journal Nature. “We are entering a completely new part of the quantum world.”

Very recently, researchers led by Markus Aspelmeyer at the University of Vienna and Lukas Novotny at ETH Zurich cooled a glass nanoparticle into the quantum regime for the first time. To do this, the particle is deprived of its kinetic energy with the help of lasers. What remains are movements, so-called quantum fluctuations, which no longer follow the laws of classical physics but those of quantum physics. The glass sphere with which this has been achieved is significantly smaller than a grain of sand, but still consists of several hundred million atoms. In contrast to the microscopic world of photons and atoms, nanoparticles provide an insight into the quantum nature of macroscopic objects. In collaboration with experimental physicist Markus Aspelmeyer, a team of theoretical physicists led by Oriol Romero-Isart of the University of Innsbruck and the Institute of Quantum Optics and Quantum Information of the Austrian Academy of Sciences is now proposing a way to harness the quantum properties of nanoparticles for various applications.

Briefly delocalized

“While atoms in the motional ground state bounce around over distances larger than the size of the atom, the motion of macroscopic objects in the ground state is very, very small,” explain Talitha Weiss and Marc Roda-Llordes from the Innsbruck team. “The quantum fluctuations of nanoparticles are smaller than the diameter of an atom.” To take advantage of the quantum nature of nanoparticles, the wave function of the particles must be greatly expanded. In the Innsbruck quantum physicists’ scheme, nanoparticles are trapped in optical fields and cooled to the ground state. By rhythmically changing these fields, the particles now succeed in briefly delocalizing over exponentially larger distances. “Even the smallest perturbations may destroy the coherence of the particles, which is why by changing the optical potentials, we only briefly pull apart the wave function of the particles and then immediately compress it again,” explains Oriol Romero-Isart.

Imagine you sit down and pick up your favorite book. You look at the image on the front cover, run your fingers across the smooth book sleeve, and smell that familiar book smell as you flick through the pages. To you, the book is made up of a range of sensory appearances.

But you also expect the book has its own independent existence behind those appearances. So when you put the book down on the coffee table and walk into the kitchen, or leave your house to go to work, you expect the book still looks, feels, and smells just as it did when you were holding it.

Two important factors limiting Moore’s Law are power consumption and Coulomb interactions are interactions between electric charges that follow Coloumb’s law, an electrodynamics theory.

These interactions can be a major challenge for the development of nanoelectronic circuits. Quantum spin Hall (QSH) insulators are particularly promising materials for the development of low-power electronics, yet so far the impact of Coulomb interactions on nanocircuits made by these materials have only been examined theoretically, rather than experimentally.

Researchers at Nanjing University and Peking University have recently observed one-dimensional (1D) Coulomb drag between adjacent QSH edges separated by an air gap. Their paper, published in Nature Electronics, highlights the potential of QSH effects for suppressing the adverse effects of Coulomb interactions on the performance of nanocircuits.

“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.

Continue reading “Physicists Teach AI to Simulate Atomic Clusters” »