New Intel Horse Ridge Cryogenic Chip Offers High-Fidelity Two-Qubit Control: A Major Quantum Computing Breakthrough

By Griffin Davis 

Intel collaborates with QuTech, an advanced research center for quantum internet and quantum computing, to develop a new cryogenic control SoC called the Horse Ridge. The tech giant manufacturer claimed that this new chipset is a breakthrough in quantum computing. 

A “Mistral” supercomputer, installed in 2016, at the German Climate Computing Center (DKRZ, or Deutsches Klimarechenzentrum) on June 7, 2017 in Hamburg, Germany. The DKRZ provides HPC (high performance computing) and associated services for climate research institutes in Germany. Its high performance computer and storage systems have been specifically selected with respect to climate and Earth system modeling.

Right now, researchers and experts are finding it hard to understand quantum computations since they are complex mathematical equations. On the other hand, they also deal with quantum states, specifically superposition and entanglement. 

Because of these, traditional computers are currently unable to perform quantum computations because they don’t have the ability to harness the phenomenon of quantum mechanics. Since this is the case, experts are forced to create quantum supercomputers that are specifically developed to perform quantum computing. 
And now, Intel also developed a new SoC that could be used in these special desktops.

To help you have more idea about it, here are other details of Intel’s new Horse Ridge. 

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Will quantum computing deliver a big leap forward for battery cells?

By Michelle Lewis 

The Cologne-headquartered German Aerospace Center (DLR) and Cambridge Quantum Computing(CQC) in the UK is the latest pair to explore how quantum computing could help create better simulation models for battery development. The DLR is Germany’s research center for aeronautics and space.

As IBM defines it, “Quantum computing harnesses the phenomena of quantum mechanics to deliver a huge leap forward in computation to solve certain problems.”

DLR will use CQC’s quantum algorithms for solving partial differential equation systems to render a one-dimensional simulation of a lithium-ion battery cell.

This will lay the groundwork for exploring multi-scale simulations of complete battery cells with quantum computers, which are considered a viable alternative for rendering full 3D models. A multi-scale approach incorporates information from different system levels (e.g., atomistic, molecular, and macroscopic) to make a simulation more manageable and realistic. That, in turn, will potentially accelerate battery research and development for a variety of sustainable energy solutions.

DLR has previously used classical computer modeling to research a range of different battery types, including lithium-ion and other technologies.

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The World’s Highest Performing Quantum Computer is Here

Our team of scientists, engineers and technicians, have built what is currently the highest performing quantum computer available.

With a quantum volume of 64, the Honeywell quantum computer is twice as powerful as the next alternative in the industry. That means we are closer to industries leveraging our solutions to solve computational problems that are impractical to solve with traditional computers.

“What makes our quantum computers so powerful is having the highest quality qubits, with the lowest error rates.  This is a combination of using identical, fully connected qubits and precision control,” said Tony Uttley, president of Honeywell Quantum Solutions.

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‘We’re hacking the process of creating qubits.’ How standard silicon chips could be used for quantum computing

Quantum Motion’s researchers have shown that it is possible to create a qubit on a standard silicon chip.  Image: Quantum Motion

By Daphne Leprince-Ringuet 

Quantum Motion says that its latest experiment paves the way for large-scale, practical quantum computers.

Forget about superconducting circuits, trapped ions, and other exotic-sounding manufacturing techniques typically associated with quantum computing: scientists have now shown that it is possible to create a qubit on a standard silicon chip, just like those found in any smartphone. 

UK-based start-up Quantum Motion has published the results of its latest experiments, which saw researchers cooling down CMOS silicon chips to a fraction of a degree above absolute zero (-273 degrees Celsius), enabling them to successfully isolate and measure the quantum state of a single electron for a whole nine seconds. 

The apparent simplicity of the method, which taps similar hardware to that found in handsets and laptops, is striking in comparison to the approaches adopted by larger players like IBM, Google or Honeywell, in their efforts to build a large-scale quantum computer. 

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Honeywell says quantum computers will outpace standard verification in ’18 to 24 months’

By Michael Vizard

Honeywell expects that as advances in quantum computing continue to accelerate over the next 18 to 24 months, the ability to replicate the results of a quantum computing application workload using a conventional computing platform simulation will come to an end.

The company’s System Model H1 has now quadrupled its performance capabilities to become the first commercial quantum computer to attain a 512 quantum volume. Ascertaining quantum volume requires running a complex set of statistical tests that are influenced by the number of qubits, error rates, connectivity of qubits, and cross-talk between qubits. That approach provides a more accurate assessment of a quantum computer’s processing capability that goes beyond simply counting the number of qubits that can be employed.

Honeywell today provides access to a set of simulation tools that make it possible to validate the results delivered on its quantum computers on a conventional machine. Those simulations give organizations more confidence in quantum computing platforms by allowing them to compare results. However, quantum computers are now approaching a level where at some point between 2022 and 2023 that will no longer be possible, Honeywell Quantum Solutions president Tony Uttley said.

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Quantum computing: How basic broadband fiber could pave the way to the next breakthrough

By Daphne Leprince-Ringuet |

New research from NIST could hold the key to accelerating the development of large-scale quantum computers packing one million qubits – using simple telecommunications cables.

Google’s Sycamore quantum processor.  Image: Google

The usefulness of most quantum computers is still significantly limited by the low number of qubits that hardware can support. But simple fiber optic cables – just like the ones used for broadband connections – could be the answer. 

A team of researchers from the National Institute of Standards and Technology (NIST) found that, with just a few tweaks, optical fiber can be used to communicate with the qubits sitting inside superconducting quantum computers, with the same level of accuracy as existing methods.

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A Desktop Quantum Computer for Just $5,000

A cheap, portable quantum computer, aimed at schools and colleges will be launched later this year.

A Chinese start-up has unveiled plans to sell a desktop quantum computer costing less than $5,000. The new portable device is one of a range called SpinQ, aimed at schools and colleges. It is made by the Shenzhen SpinQ Technology, based in Shenzhen, China.

This is not the company’s first quantum computer. Last year, it started selling a desktop quantum computer for around $50,000. The desk in question would need to be sturdy given that the device weighs a hefty 55kg (121 lbs)—about the weight of a small adult. 

But the new machine will be simpler, more portable and cheaper. “This simplified version is expected to be released in the fourth quarter of 2021, such that it can be more affordable for most K-12 schools around the world,” say the team behind the device.

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Artificial ‘Magnetic Texture’ in Graphene May Add New Spin to Quantum Computers

The image shows eight electrodes around a 20-nanometer-thick magnet (white rectangle). The graphene, not show, is less than 1 nanometer thick and next to the magnet. (Image: University at Buffalo.)

Graphene is incredibly strong, lightweight, conductive … the list of its superlative properties goes on.

It is not, however, magnetic — a shortcoming that has stunted its usefulness in spintronics, an emerging field that scientists say could eventually rewrite the rules of electronics, leading to more powerful semiconductors, computers and other devices.

Now, an international research team led by the University at Buffalo is reporting an advancement that could help overcome this obstacle. The researchers added that the advance may lead to powerful spintronic devices, such as semiconductors and quantum computers.

In a study published today in the journal Physical Review Letters, researchers describe how they paired a magnet with graphene, and induced what they describe as “artificial magnetic texture” in the nonmagnetic wonder material.

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Lack of symmetry in qubits can’t fix errors in quantum computing, might explain matter/antimatter

A new paper seeking to cure a time restriction in quantum annealing computers instead opened up a class of new physics problems that can now be studied with quantum annealers without requiring they be too slow. Credit: Los Alamos National Laboratory

by Charles Poling , Los Alamos National Laboratory

A team of quantum theorists seeking to cure a basic problem with quantum annealing computers—they have to run at a relatively slow pace to operate properly—found something intriguing instead. While probing how quantum annealers perform when operated faster than desired, the team unexpectedly discovered a new effect that may account for the imbalanced distribution of matter and antimatter in the universe and a novel approach to separating isotopes.

“Although our discovery did not the cure the annealing time restriction, it brought a class of new physics problems that can now be studied with quantum annealers without requiring they be too slow,” said Nikolai Sinitsyn, a theoretical physicist at Los Alamos National Laboratory. Sinitsyn is author of the paper published Feb. 19 in Physical Review Letters, with coauthors Bin Yan and Wojciech Zurek, both also of Los Alamos, and Vladimir Chernyak of Wayne State University.

Significantly, this finding hints at how at least two famous scientific problems may be resolved in the future. The first one is the apparent asymmetry between matter and antimatter in the universe.

“We believe that small modifications to recent experiments with quantum annealing of interacting qubits made of ultracold atoms across phase transitions will be sufficient to demonstrate our effect,” Sinitsyn said.

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A quantum computer just solved a decades-old problem three million times faster than a classical computer

To simulate exotic magnetism, King and his team programmed the D-Wave 2,000-qubit system to model a quantum magnetic system.

By Daphne Leprince-Ringuet 

Using a method called quantum annealing, D-Wave’s researchers demonstrated that a quantum computational advantage could be achieved over classical means.

Scientists from quantum computing company D-Wave have demonstrated that, using a method called quantum annealing, they could simulate some materials up to three million times faster than it would take with corresponding classical methods.  

Together with researchers from Google, the scientists set out to measure the speed of simulation in one of D-Wave’s quantum annealing processors, and found that performance increased with both simulation size and problem difficulty, to reach a million-fold speedup over what could be achieved with a classical CPU.  

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Quantum Computer Chips Manufactured Using Mass-Market Industrial Fabrication Techniques


The quantum computing revolution is upon us. Well, almost. It’s hard to have missed the headlines proclaiming the great power of the latest generation of quantum, their ability to outperform conventional computers , a property called quantum supremacy, and the huge promise of the years ahead.

But an important question remains — how are we going to build these devices? Quantum computers variously rely on photons and/or exotic states of matter trapped in magnetic fields at mind-numbingly cold temperatures. So it’s easy to imagine that quantum computing will require an entirely new industrial base founded on novel technologies.

But there is another possibility: that quantum computers can work with electrons passing through transistor-like devices called quantum dots carved out of silicon. If that’s the case, the entire revolution can piggyback on the industrial base that supports current chip-manufacture.

Now this option looks a step closer thanks to the work of Anne-Marije Zwerver at Delft University of Technology in Denmark and colleagues, many at the research labs at U.S. chipmaker Intel, based in Hillsboro, Oregon. This group has fabricated nanoscale silicon transistors that can reliably process quantum information in ways that match specialist devices. 

But the key breakthrough is that they have done this using industrial chip fabrication processes with a yield that is high enough to allow significant scalability. That paves the way for industrial-scale fabrication of quantum computing chips. “The feasibility of high-quality qubits made with fully-industrial techniques strongly enhances the prospects of a large-scale quantum computer,” says the team.

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Q-CTRL’s new AI toolset allows quantum computers to self-optimize

The toolset runs on Q-CTRL’s flagship BOULDER OPAL software

by: Praharsha Anand

Q-CTRL has announced a new AI-based toolset to facilitate the unassisted performance optimization of quantum computers.

By and large, quantum algorithms are susceptible to errors, creating a substantial barrier to progress and advancement in quantum computing. Q-CTRL’s new automated closed-loop hardware optimization tool uses custom AI agents to run quantum algorithms, resulting in fewer errors and better overall performance for end-users.

Integrated with Q-CTRL’s flagship BOULDER OPAL software for developers and R&D teams, automated closed-loop hardware optimization is also trained to obtain new experimental data/results from quantum computers while simultaneously running optimizations for algorithms. It can be used as a standalone tool or in tandem with a machine-learner online optimization package (M-LOOP) that manages quantum experiments autonomously.

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