Machine Learning as a Service Market COVID19 Impact Analysis Drivers, Analysis, Share, Growth, and Trends & Forecast to 2026: Amazon, Oracle…

Machine Learning as a Service Market research report enhanced worldwide COVID19 Impact analysis on Market Size (Value, Production, Sales, Consumption, Revenue, and Growth Rate), Gross Margin, Industry Chain, Trends, Top Manufacturers, Development Trends, History and 6 Year Forecast. This Machine Learning as a Service Market competitive landscapes provides details by topmost manufactures like (Amazon, Oracle Corporation, IBM, Microsoft Corporation, Google Inc., Salesforce.Com, Tencent, Alibaba, UCloud, Baidu, Rackspace, SAP AG, Century Link Inc., CSC (Computer Science Corporation), Heroku, Clustrix, Xeround) with data such as Company Profiles, Trade Sales Volume, Gross, Cost, Industry Share By Type, Product Revenue , Specifications and Contact Information. Besides, Machine Learning as a Service industry report helps to analyse competitive developments such as Joint Ventures, Strategic Alliances, Mergers and Acquisitions, New Product Developments, Research and Developments.

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Target Audience of the Machine Learning as a Service Market in This Study: Key Consulting Companies & Advisors, Large, medium-sized, and small enterprises, Venture capitalists, Value-Added Resellers (VARs), Manufacturers, Third-party knowledge providers, Equipment Suppliers/ Buyers, Machine Learning as a Service market Investors/Investment Bankers, Research Professionals, Emerging Companies, Service Providers.

Scope of Machine Learning as a Service Market:Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to learn (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.

On the basis of product type, this report displays the shipments, revenue (Million USD), price, and market share and growth rate of each type.

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On the basis on the end users/applications,this report focuses on the status and outlook for major applications/end users, shipments, revenue (Million USD), price, and market share and growth rate foreach application.

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Geographically, the report includes the research on production, consumption, revenue, Machine Learning as a Service market share and growth rate, and forecast (2020-2026) of the following regions:

Key Developments in the Machine Learning as a Service Market:

To describe Machine Learning as a Service Introduction, product type and application, market overview, market analysis by countries, Machine Learning as a Service market Opportunities, Market Risk, Market Driving Force;

To analyze the manufacturers of Machine Learning as a Service market , with Profile, Main Business, News, Sales, Price, Revenue and Market Share in 2016 and 2020;

To display the competitive situation among the top manufacturers in Global, with sales, revenue and Machine Learning as a Service market share in 2016 and 2020;

To analyze the key countries by manufacturers, Type and Application, covering North America, Europe, Asia Pacific, Middle-East and South America, with sales, revenue and Machine Learning as a Service market share by manufacturers, types and applications;

To analyze the Machine Learning as a Service market Manufacturing Cost, Key Raw Materials and Manufacturing Process etc.

To analyze the Industrial Chain, Sourcing Strategy and Downstream End Users (buyers);

To describe Machine Learning as a Service market sales Channel, Distributors, Traders, Dealers etc.

To describe Machine Learning as a Service market Research Findings and Conclusion, Appendix, Methodology and Data Source.

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Machine Learning as a Service Market COVID19 Impact Analysis Drivers, Analysis, Share, Growth, and Trends & Forecast to 2026: Amazon, Oracle...

Announcing availability of Inf1 instances in Amazon SageMaker for high performance and cost-effective machine learning inference – idk.dev

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thompson Reuters, use Amazon SageMaker to remove the heavy lifting from each step of the ML process.

When it comes to deploying ML models for real-time prediction, Amazon SageMaker provides you with a large selection of AWS instance types, from small CPU instances to multi-GPU instances. This lets you find the right cost/performance ratio for your prediction infrastructure. Today we announce the availability of Inf1 instances in Amazon SageMaker to deliver high performance, low latency, and cost-effective inference.

The Amazon EC2 Inf1 instances were launched at AWS re:Invent 2019. Inf1 instances are powered by AWS Inferentia, a custom chip built from the ground up by AWS to accelerate machine learning inference workloads. When compared to G4 instances, Inf1 instances offer up to three times the inferencing throughput and up to 45% lower cost per inference.

Inf1 instances are available in multiple sizes, with 1, 4, or 16 AWS Inferentia chips. An AWS Inferentia chip contains four NeuronCores. Each implements a high-performance systolic array matrix multiply engine, which massively speeds up typical deep learning operations such as convolution and transformers. NeuronCores are also equipped with a large on-chip cache, which helps cut down on external memory accesses and saves I/O time in the process.

When several AWS Inferentia chips are available on an Inf1 instance, you can partition a model across them and store it entirely in cache memory. Alternatively, to serve multi-model predictions from a single Inf1 instance, you can partition the NeuronCores of an AWS Inferentia chip across several models.

To run machine learning models on Inf1 instances, you need to compile models to a hardware-optimized representation using the AWS Neuron SDK. Since the launch of Inf1 instances, AWS has released five versions of the AWS Neuron SDK that focused on performance improvements and new features, with plans to add more on a regular cadence. For example, image classification (ResNet-50) performance has improved by more than 2X, from 1100 to 2300 images/sec on a single AWS Inferentia chip. This performance improvement translates to 45% lower cost per inference as compared to G4 instances. Support for object detection models starting with Single Shot Detection (SSD) was also added, with Mask R-CNN coming soon.

Now let us show you how you can easily compile, load and run models on ml.Inf1 instances in Amazon SageMaker.

Compiling and deploying models for Inf1 instances in Amazon SageMaker is straightforward thanks to Amazon SageMaker Neo. The AWS Neuron SDK is integrated with Amazon SageMaker Neo to run your model optimally on Inf1 instances in Amazon SageMaker. You only need to complete the following steps:

In the following example use case, you train a simple TensorFlow image classifier on the MNIST dataset, like in this sample notebook on GitHub. The training code would look something like the following:

To compile the model for an Inf1 instance, you make a single API call and select ml_inf1 as the deployment target. See the following code:

Once the machine learning model has been compiled, you deploy the model on an Inf1 instance in Amazon SageMaker using the optimized estimator from Amazon SageMaker Neo. Under the hood, when creating the inference endpoint, Amazon SageMaker automatically selects a container with the Neo Deep Learning Runtime, a lightweight runtime that will load and invoke the optimized model for inference.

Thats it! After you deploy the model, you can invoke the endpoint and receive predictions in real time with low latency. You can find a full example on Github.

Inf1 instances in Amazon SageMaker are available in four sizes: ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, and ml.inf1.24xlarge. Machine learning models developed using TensorFlow and MxNet frameworks can be compiled with Amazon SageMaker Neo to run optimally on Inf1 instances and deployed on Inf1 instances in Amazon SageMaker for real-time inference. You can start using Inf1 instances in Amazon SageMaker today in the US East (N. Virginia) and US West (Oregon) Regions.

Julien Simon is an Artificial Intelligence & Machine Learning Evangelist for EMEA, Julien focuses on helping developers and enterprises bring their ideas to life.

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Announcing availability of Inf1 instances in Amazon SageMaker for high performance and cost-effective machine learning inference - idk.dev

Blockstream CEO: Bitcoin (BTC) Creator Satoshi Nakamoto May Have Written This Newly Discovered Post – The Daily Hodl

Blockstream chief executive and cryptographer Adam Back says a 200-word post from back in 1999, a decade before Bitcoin was launched, appears to carry the hallmarks of the anonymous creator of Bitcoin known as Satoshi Nakamoto.

The text is part of a back and forth among the cypherpunks, a group of activists who emerged in the late 80s advocating cryptography, anonymity and personal privacy.

Back, who is referenced in the Bitcoin whitepaper, is a longtime member of the movement and the inventor of Hashcash, a proof-of-work system that ultimately became a cornerstone for BTC.

In a series of tweets, Back says he has unearthed a post from the early cypherpunk days featuring an anonymous author who spouted a number of Bitcoins ideals, including how to successfully secure a virtual currency in a decentralized manner.

One possibility is to make the double-spending database public. Whenever someone receives a coin they broadcast its value. The [database] operates in parallel across a large number of servers so it is intractableto shut it down.

However, at one point, the author writes over night instead of overnight a mistake that would be out of character for the notably meticulous Nakamoto.

Back says the error is noteworthy, but calls it more of a typo than a misspelling.

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Blockstream CEO: Bitcoin (BTC) Creator Satoshi Nakamoto May Have Written This Newly Discovered Post - The Daily Hodl

Quantum Cryptography Solutions Market Analysis by Size, Share, Top Key Manufacturers, Demand Overview, Regional Outlook And Growth Forecast to 2026 …

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Global Quantum Cryptography Solutions Market Segmentation

This market was divided into types, applications and regions. The growth of each segment provides an accurate calculation and forecast of sales by type and application in terms of volume and value for the period between 2020 and 2026. This analysis can help you develop your business by targeting niche markets. Market share data are available at global and regional levels. The regions covered by the report are North America, Europe, the Asia-Pacific region, the Middle East, and Africa and Latin America. Research analysts understand the competitive forces and provide competitive analysis for each competitor separately.

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Global Quantum Cryptography Solutions Market Regions and Countries Level Analysis

The regional analysis is a very complete part of this report. This segmentation highlights Quantum Cryptography Solutions sales at regional and national levels. This data provides a detailed and accurate analysis of volume by country and an analysis of market size by region of the world market.

The report provides an in-depth assessment of growth and other aspects of the market in key countries such as the United States, Canada, Mexico, Germany, France, the United Kingdom, Russia and the United States Italy, China, Japan, South Korea, India, Australia, Brazil and Saudi Arabia. The chapter on the competitive landscape of the global market report contains important information on market participants such as business overview, total sales (financial data), market potential, global presence, Quantum Cryptography Solutions sales and earnings, market share, prices, production locations and facilities, products offered and applied strategies. This study provides Quantum Cryptography Solutions sales, revenue, and market share for each player covered in this report for a period between 2016 and 2020.

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We offer state of the art critical reports with accurate information about the future of the market.

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Our report helps readers decipher the current and future constraints of the Quantum Cryptography Solutions market and formulate optimal business strategies to maximize market growth.

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Table of Contents:

Study Coverage: It includes study objectives, years considered for the research study, growth rate and Quantum Cryptography Solutions market size of type and application segments, key manufacturers covered, product scope, and highlights of segmental analysis.

Executive Summary: In this section, the report focuses on analysis of macroscopic indicators, market issues, drivers, and trends, competitive landscape, CAGR of the global Quantum Cryptography Solutions market, and global production. Under the global production chapter, the authors of the report have included market pricing and trends, global capacity, global production, and global revenue forecasts.

Quantum Cryptography Solutions Market Size by Manufacturer: Here, the report concentrates on revenue and production shares of manufacturers for all the years of the forecast period. It also focuses on price by manufacturer and expansion plans and mergers and acquisitions of companies.

Production by Region: It shows how the revenue and production in the global market are distributed among different regions. Each regional market is extensively studied here on the basis of import and export, key players, revenue, and production.

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Quantum Cryptography Solutions Market Analysis by Size, Share, Top Key Manufacturers, Demand Overview, Regional Outlook And Growth Forecast to 2026 ...

Nodle Launches Coalition, a Free, Privacy-First Contact Tracing App to Help Stop The Spread of COVID-19 – Business Wire

SAN FRANCISCO--(BUSINESS WIRE)--The team behind Nodle.io, an Internet of Things (IoT) connectivity and security startup, today announces the launch of Coalition, a free, privacy-first contact tracing app to help stop the spread of COVID-19. Coalition aims to be an essential preventive tool to protect communities and global citizens during the current COVID-19 crisis. Coalition is now available for Android in the Google Play Store and pending release for iOS in the App Store.

Coalition enables community-driven contact tracing through a privacy-by-design approach. The app utilizes secure Bluetooth Low Energy (BLE) and cryptography to protect a users identity by generating random anonymous IDs. Coalitions Whisper Tracing, an open and privacy-first protocol, randomizes a users device identity and does not share identifiable information with the cloud. No personal data is ever asked for, collected or shared. View Coalitions Whisper Tracing research paper, reviewed by Nodle peers and cryptographers from MIT, Stanford, USC and Oxford University.

While we have yet to see any plausible global contact tracing solutionmainly due to issues of privacywe believe the Coalition App could change that with the teams experience and primary focus on protecting users data with a privacy by design approach as described in [the] scientific paper, said Dr. Newton Howard, D.Phil., HDR, PhD, C.O., M.A., University of Oxford & Georgetown University Medical Center.

How does it work?

After a user downloads the app and turns on Bluetooth, their phone becomes aware of other app users within a range of approximately 10 meters. The app records anonymous encounters with other Coalition app users nearby. These encounters are stored locally on the phone, using randomly generated IDs to preserve users privacy.

The Coalition App benefits from decades-long R&D by the Nodle team in the space of decentralized wireless networks, mobile mesh, and secure identity management. Nodles CEO & Co-Founder Micha Benoliel previously created FireChat, a peer-to-peer Bluetooth-based off-the-grid messaging technology that allowed for communication on planes, cruises, at festivals and during internet shutdowns (see Hong Kongs 2014 Umbrella Revolution) and disaster-recovery situations where internet isnt available.

Our team has the technology to create this type of application with the utmost security and respect for privacy. Every feature is built with privacy and anonymity at its core, says Benoliel. We believe the only way to stop COVID-19 is through massive cooperation and solidarity between citizens around the world who can do their part to protect themselves and their communities. We must work fast to put the right safeguards in place before we reopen society and put economies back on track.

The Whisper Tracing protocol is open and the code from the apps will be open sourced in the coming days so the various tracing apps can communicate with each other and allow the community to make improvements for future use. It is comparable to what Apple and Google announced, with the exception that Coalition has already released a working implementation of its protocol within the Coalition app. The Coalition team mentioned that they would add support for Apple and Googles protocol if it can increase interoperability, the same way they are also adding support for Singapores TraceTogether app.

Coalition is kicking off in the coming week with local leaders in Berkeley, California, through a series of virtual town halls with Mayor Jesse Arreguin and City Councilmember Ben Bartlett. You have my full support and commitment to help bring this life-saving application to affected communities everywhere, Bartlett said in his letter of support for the Coalition App.

Operating as a non-profit, Coalition already received interest from several foundations who wish to provide additional financial support to stop the spread of COVID-19. The growing number of partners include Berkeley Blockchain Xcelerator, NY Tech Alliance, TCN Coalition, COVID Alliance, and more.

About Coalition:

Coalitions app and technology were developed by the team behind Nodle, an Internet of Things (IoT) connectivity and security startup, and a few independents. The Nodle founders previously created FireChat, a peer-to-peer Bluetooth-based messaging technology that is used to stay connected and communicate in areas without internet access.

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Nodle Launches Coalition, a Free, Privacy-First Contact Tracing App to Help Stop The Spread of COVID-19 - Business Wire

Explainer: What is a quantum computer? | MIT Technology Review

This is the first in a series of explainers on quantum technology. The other two are on quantum communication and post-quantum cryptography.

A quantum computer harnesses some of the almost-mystical phenomena of quantum mechanics to deliver huge leaps forward in processing power. Quantum machines promise to outstrip even the most capable of todaysand tomorrowssupercomputers.

They wont wipe out conventional computers, though. Using a classical machine will still be the easiest and most economical solution for tackling most problems. But quantum computers promise to power exciting advances in various fields, from materials science to pharmaceuticals research. Companies are already experimenting with them to develop things like lighter and more powerful batteries for electric cars, and to help create novel drugs.

The secret to a quantum computers power lies in its ability to generate and manipulate quantum bits, or qubits.

Today's computers use bitsa stream of electrical or optical pulses representing1s or0s. Everything from your tweets and e-mails to your iTunes songs and YouTube videos are essentially long strings of these binary digits.

Quantum computers, on the other hand, usequbits, whichare typically subatomic particles such as electrons or photons. Generating and managing qubits is a scientific and engineering challenge. Some companies, such as IBM, Google, and Rigetti Computing, use superconducting circuits cooled to temperatures colder than deep space. Others, like IonQ, trap individual atoms in electromagnetic fields on a silicon chip in ultra-high-vacuum chambers. In both cases, the goal is to isolate the qubits in a controlled quantum state.

Qubits have some quirky quantum properties that mean a connected group of them can provide way more processing power than the same number of binary bits. One of those properties is known as superposition and another is called entanglement.

Qubits can represent numerous possible combinations of 1and 0 at the same time. This ability to simultaneously be in multiple states is called superposition. To put qubits into superposition, researchers manipulate them using precision lasers or microwave beams.

Thanks to this counterintuitive phenomenon, a quantum computer with several qubits in superposition can crunch through a vast number of potential outcomes simultaneously. The final result of a calculation emerges only once the qubits are measured, which immediately causes their quantum state to collapse to either 1or 0.

Researchers can generate pairs of qubits that are entangled, which means the two members of a pair exist in a single quantum state. Changing the state of one of the qubits will instantaneously change the state of the other one in a predictable way. This happens even if they are separated by very long distances.

Nobody really knows quite how or why entanglement works. It even baffled Einstein, who famously described it as spooky action at a distance. But its key to the power of quantum computers. In a conventional computer, doubling the number of bits doubles its processing power. But thanks to entanglement, adding extra qubits to a quantum machine produces an exponential increase in its number-crunching ability.

Quantum computers harness entangled qubits in a kind of quantum daisy chain to work their magic. The machines ability to speed up calculations using specially designed quantum algorithms is why theres so much buzz about their potential.

Thats the good news. The bad news is that quantum machines are way more error-prone than classical computers because of decoherence.

The interaction of qubits with their environment in ways that cause their quantum behavior to decay and ultimately disappear is called decoherence. Their quantum state is extremely fragile. The slightest vibration or change in temperaturedisturbances known as noise in quantum-speakcan cause them to tumble out of superposition before their job has been properly done. Thats why researchers do their best to protect qubits from the outside world in those supercooled fridges and vacuum chambers.

But despite their efforts, noise still causes lots of errors to creep into calculations. Smart quantum algorithmscan compensate for some of these, and adding more qubits also helps. However, it will likely take thousands of standard qubits to create a single, highly reliable one, known as a logical qubit. This will sap a lot of a quantum computers computational capacity.

And theres the rub: so far, researchers havent been able to generate more than 128 standard qubits (see our qubit counter here). So were still many years away from getting quantum computers that will be broadly useful.

That hasnt dented pioneers hopes of being the first to demonstrate quantum supremacy.

Its the point at which a quantum computer can complete a mathematical calculation that is demonstrably beyond the reach of even the most powerful supercomputer.

Its still unclear exactly how many qubits will be needed to achieve this because researchers keep finding new algorithms to boost the performance of classical machines, and supercomputing hardware keeps getting better. But researchers and companies are working hard to claim the title, running testsagainst some of the worlds most powerful supercomputers.

Theres plenty of debate in the research world about just how significant achieving this milestone will be. Rather than wait for supremacy to be declared, companies are already starting to experiment with quantum computers made by companies like IBM, Rigetti, and D-Wave, a Canadian firm. Chinese firms like Alibaba are also offering access to quantum machines. Some businesses are buying quantum computers, while others are using ones made available through cloud computing services.

One of the most promising applications of quantum computers is for simulating the behavior of matterdown to the molecular level. Auto manufacturers like Volkswagen and Daimler are using quantum computers to simulate the chemical composition of electrical-vehicle batteries to help find new ways to improve their performance. And pharmaceutical companies are leveraging them to analyze and compare compounds that could lead to the creation of new drugs.

The machines are also great for optimization problems because they can crunch through vast numbers of potential solutions extremely fast. Airbus, for instance, is using them to help calculate the most fuel-efficient ascent and descent paths for aircraft. And Volkswagen has unveiled a service that calculates the optimal routes for buses and taxis in cities in order to minimize congestion. Some researchers also think the machines could be used to accelerate artificial intelligence.

It could take quite a few years for quantum computers to achieve their full potential. Universities and businesses working on them are facing a shortage of skilled researchersin the fieldand a lack of suppliersof some key components. But if these exotic new computing machines live up to their promise, they could transform entire industries and turbocharge global innovation.

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Explainer: What is a quantum computer? | MIT Technology Review

Google’s Head of Quantum Computing Hardware Resigns – WIRED

In late October 2019, Google CEO Sundar Pichai likened the latest result from the companys quantum computing hardware lab in Santa Barbara, California, to the Wright brothers first flight.

One of the labs prototype processors had achieved quantum supremacyevocative jargon for the moment a quantum computer harnesses quantum mechanics to do something seemingly impossible for a conventional computer. In a blog post, Pichai said the milestone affirmed his belief that quantum computers might one day tackle problems like climate change, and the CEO also name-checked John Martinis, who had established Googles quantum hardware group in 2014.

Heres what Pichai didnt mention: Soon after the team had first got its quantum supremacy experiment working a few months earlier, Martinis says, he had been reassigned from a leadership position to an advisory one. Martinis tells WIRED that the change led to disagreements with Hartmut Neven, the longtime leader of Googles quantum project.

Martinis resigned from Google early this month. Since my professional goal is for someone to build a quantum computer, I think my resignation is the best course of action for everyone, he adds.

A Google spokesman did not dispute this account, and says that the company is grateful for Martinis contributions and that Neven continues to head the companys quantum project. Parent company Alphabet has a second, smaller, quantum computing group at its X Labs research unit. Martinis retains his position as a professor at the UC Santa Barbara, which he held throughout his tenure at Google, and says he will continue to work on quantum computing.

Googles quantum computing project was founded by Neven, who pioneered Googles image search technology, in 2006, and initially focused on software. To start, the small group accessed quantum hardware from Canadian startup D-Wave Systems, including in collaboration with NASA.

Everything you ever wanted to know about qubits, superpositioning, and spooky action at a distance.

The project took on greater scale and ambition when Martinis joined in 2014 to establish Googles quantum hardware lab in Santa Barbara, bringing along several members of his university research group. His nearby lab at UC Santa Barbara had produced some of the most prominent work in the field over the past 20 years, helping to demonstrate the potential of using superconducting circuits to build qubits, the building blocks of quantum computers.

Qubits are analogous to the bits of a conventional computer, but in addition to representing 1s and 0s, they can use quantum mechanical effects to attain a third state, dubbed a superposition, something like a combination of both. Qubits in superposition can work through some very complex problems, such as modeling the interactions of atoms and molecules, much more efficiently than conventional computer hardware.

How useful that is depends on the number and reliability of qubits in your quantum computing processor. So far the best demonstrations have used only tens of qubits, a far cry from the hundreds or thousands of high quality qubits experts believe will be needed to do useful work in chemistry or other fields. Googles supremacy experiment used 53 qubits working together. They took minutes to crunch through a carefully chosen math problem the company calculated would take a supercomputer on the order of 10,000 years, but does not have a practical application.

Martinis leaves Google as the company and rivals that are working on quantum computing face crucial questions about the technologys path. Amazon, IBM, and Microsoft, as well as Google offer their prototype technology to companies such as Daimler and JP Morgan so they can run experiments. But those processors are not large enough to work on practical problems, and it is not clear how quickly they can be scaled up.

When WIRED visited Googles quantum hardware lab in Santa Barbara last fall, Martinis responded optimistically when asked if his hardware team could see a path to making the technology practical. I feel we know how to scale up to hundreds and maybe thousands of qubits, he said at the time. Google will now have to do it without him.

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Google's Head of Quantum Computing Hardware Resigns - WIRED

Quantum computing heats up down under as researchers reckon they know how to cut costs and improve stability – The Register

Boffins claim to have found path to 'real-world applications' by running hot

Dr Henry Yang and Professor Andrew Dzurak: hot qubits are a game-changer for quantum computing development. Pic credit: Paul Henderson-Kelly

Scientists in Australia are claiming to have made a breakthrough in the field of quantum computing which could ease the technology's progress to affordability and mass production.

A paper by researchers led by Professor Andrew Dzurak at Sydney's University of New South Wales published in Nature today says they have demonstrated quantum computing at temperatures 15 times warmer than previously thought possible.

Temperature is important to quantum computing because quantum bits (qubits) the equivalent classical computing bits running the computer displaying this story can exist in superconducting circuits or form within semiconductors only at very low temperatures.

Most quantum computers being developed by the likes of IBM and Google form qubits at temperatures within 0.1 degrees above absolute zero or -273.15C (-459.67F). These solid-state platforms require cooling to extremely low temperatures because vibrations generated by heat disrupt the qubits, which can impede performance. Getting this cold requires expensive dilution refrigerators.

Artistic representation of quantum entanglement. Pic credit: Luca Petit for QuTech

But Dzurak's team has shown that they can maintain stable "hotbits" at temperatures up to 15 times higher than existing technologies. That is a sweltering 1.5 Kelvin (-271.65C). It might not seem like much, but it could make a big difference when it comes to scaling quantum computers and getting them one step closer to practical applications.

"For most solid-state qubit technologies for example, those using superconducting circuits or semiconductor spins scaling poses a considerable challenge because every additional qubit increases the heat generated, whereas the cooling power of dilution refrigerators is severely limited at their operating temperature. As temperatures rise above 1 Kelvin, the cost drops substantially and the efficiency improves. In addition, using silicon-based platforms is attractive, as this can assist integration into classical systems that use existing silicon-based hardware," the paper says.

Keeping temperature at around 1.5 Kelvin can be achieved using a few thousand dollars' worth of refrigeration, rather than the millions of dollars needed to cool chips to 0.1 Kelvin, Dzurak said.

"Our new results open a path from experimental devices to affordable quantum computers for real-world business and government applications," he added.

The researchers used "isotopically enriched silicon" but the proof of concept published today promises cheaper and more robust quantum computing which can be built on hardware using conventional silicon chip foundries, they said.

Nature published another independent study by Dr Menno Veldhorst and colleagues at Delft University of Technology in the Netherlands which details a quantum circuit that operates at 1.1 Kelvin, confirming the breakthrough.

If made more practical and cheaper, quantum computers could represent a leap forward in information science. Whereas the bit in classical computing either represents a one or a zero, qubits superimpose one and zero, representing both states at the same time. This creates an exponential improvement in performances such that so eight qubits theoretically have two to eight times the performance of eight bits. For example, Google and NASA have demonstrated that a quantum computer with 1,097 qubits outperformed existing supercomputers by more than 3,600 times and personal computers by 100 million.

While the experimental nature and cost of quantum computing means it is unlikely to make it into any business setup soon, anything to make the approach more practical could make a big difference to scientific computational challenges such as protein folding. The problem of how to predict the structure of a protein from its amino acid sequence is important for understanding how proteins function in a wide range of biological processes and could potentially help design better medicines.

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Quantum computing heats up down under as researchers reckon they know how to cut costs and improve stability - The Register

What To Expect In The Emerging Age Of Quantum Computing – Law360

Law360 (April 21, 2020, 5:23 PM EDT) -- Once considered a scientific impossibility, quantum computing is now expected to have a far-reaching commercial impact thanks to an increase in investment and a myriad of new discoveries by physicists and computer scientists. Quantum computers have the potential to transform industries from auto manufacturing to pharmaceuticals to finance, but the technology has only recently moved from the laboratory to the commercial market.

At the Consumer Electronics Show in Las Vegas in January, IBM Corp. announced it had struck partnerships with Daimler AG (the parent company of Mercedes-Benz) and Delta Air Lines Inc. to harness quantum computing to solve real-world issues for...

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What To Expect In The Emerging Age Of Quantum Computing - Law360

Muquans and Pasqal partner to advance quantum computing – Quantaneo, the Quantum Computing Source

This partnership is an opportunity to leverage a unique industrial and technological expertise for the design, integration and validation of advanced quantum solutions that has been applied for more than a decade to quantum gravimeters and atomic clocks. It will speed up the development of Pasqals processors and will bring them to an unprecedented maturity level.

Muquans will supply several key technological building blocks and a technical assistance to Pasqal, that will offer an advanced computing and simulation capability towards quantum advantage for real life applications.

We have the strong belief that the neutral atoms technology developed by Pasqal has a unique potential and this agreement is a wonderful opportunity for Muquans to participate on the great adventure of quantum computing. It will also help us find new opportunities for our technologies. We expect this activity to significantly grow in the coming years and this partnership will allow us to become a key stakeholder in the supply chain of quantum computers., Bruno Desruelle, CEO Muquans

Muquans laser solutions combine extreme performance, advanced functionalities and industrial reliability. When you develop the next generation of quantum computers, you need to rely on strong bases and build trust with your partners. Being able to embed this technology in our processors will be a key factor for our company to consolidate our competitive advantage and bring quantum processors to the market., Georges-Olivier Reymond, CEO Pasqal

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Muquans and Pasqal partner to advance quantum computing - Quantaneo, the Quantum Computing Source