On Oct. 23, 2019, Google published a paper in the journal Nature entitled Quantum supremacy using a programmable superconducting processor. The tech giant announced its achievement of a much vaunted goal: quantum supremacy.

This perhaps ill-chosen term (coined by physicist John Preskill) is meant to convey the huge speedup that processors based on quantum-mechanical systems are predicted to exhibit, relative to even the fastest classical computers.

Googles benchmark was achieved on a new type of quantum processor, code-named Sycamore, consisting of 54 independently addressable superconducting junction devices (of which only 53 were working for the demonstration).

Each of these devices allows the storage of one bit of quantum information. In contrast to the bits in a classical computer, which can only store one of two states (0 or 1 in the digital language of binary code), a quantum bit qbit can store information in a coherent superposition state which can be considered to contain fractional amounts of both 0 and 1.

Sycamore uses technology developed by the superconductivity research group of physicist John Martinis at the University of California, Santa Barbara. The entire Sycamore system must be kept cold at cryogenic temperatures using special helium dilution refrigeration technology. Because of the immense challenge involved in keeping such a large system near the absolute zero of temperature, it is a technological tour de force.

The Google researchers demonstrated that the performance of their quantum processor in sampling the output of a pseudo-random quantum circuit was vastly better than a classical computer chip like the kind in our laptops could achieve. Just how vastly became a point of contention, and the story was not without intrigue.

An inadvertent leak of the Google groups paper on the NASA Technical Reports Server (NTRS) occurred a month prior to publication, during the blackout period when Nature prohibits discussion by the authors regarding as-yet-unpublished papers. The lapse was momentary, but long enough that The Financial Times, The Verge and other outlets picked up the story.

A well-known quantum computing blog by computer scientist Scott Aaronson contained some oblique references to the leak. The reason for this obliqueness became clear when the paper was finally published online and Aaronson could at last reveal himself to be one of the reviewers.

The story had a further controversial twist when the Google groups claims were immediately countered by IBMs quantum computing group. IBM shared a preprint posted on the ArXiv (an online repository for academic papers that have yet to go through peer review) and a blog post dated Oct. 21, 2019 (note the date!).

While the Google group had claimed that a classical (super)computer would require 10,000 years to simulate the same 53-qbit random quantum circuit sampling task that their Sycamore processor could do in 200 seconds, the IBM researchers showed a method that could reduce the classical computation time to a mere matter of days.

However, the IBM classical computation would have to be carried out on the worlds fastest supercomputer the IBM-developed Summit OLCF-4 at Oak Ridge National Labs in Tennessee with clever use of secondary storage to achieve this benchmark.

While of great interest to researchers like myself working on hardware technologies related to quantum information, and important in terms of establishing academic bragging rights, the IBM-versus-Google aspect of the story is probably less relevant to the general public interested in all things quantum.

For the average citizen, the mere fact that a 53-qbit device could beat the worlds fastest supercomputer (containing more than 10,000 multi-core processors) is undoubtedly impressive. Now we must try to imagine what may come next.

The reality of quantum computing today is that very impressive strides have been made on the hardware front. A wide array of credible quantum computing hardware platforms now exist, including ion traps, superconducting device arrays similar to those in Googles Sycamore system and isolated electrons trapped in NV-centres in diamond.

These and other systems are all now in play, each with benefits and drawbacks. So far researchers and engineers have been making steady technological progress in developing these different hardware platforms for quantum computing.

What has lagged quite a bit behind are custom-designed algorithms (computer programs) designed to run on quantum computers and able to take full advantage of possible quantum speed-ups. While several notable quantum algorithms exist Shors algorithm for factorization, for example, which has applications in cryptography, and Grovers algorithm, which might prove useful in database search applications the total set of quantum algorithms remains rather small.

Much of the early interest (and funding) in quantum computing was spurred by the possibility of quantum-enabled advances in cryptography and code-breaking. A huge number of online interactions ranging from confidential communications to financial transactions require secure and encrypted messages, and modern cryptography relies on the difficulty of factoring large numbers to achieve this encryption.

Quantum computing could be very disruptive in this space, as Shors algorithm could make code-breaking much faster, while quantum-based encryption methods would allow detection of any eavesdroppers.

The interest various agencies have in unbreakable codes for secure military and financial communications has been a major driver of research in quantum computing. It is worth noting that all these code-making and code-breaking applications of quantum computing ignore to some extent the fact that no system is perfectly secure; there will always be a backdoor, because there will always be a non-quantum human element that can be compromised.

More appealing for the non-espionage and non-hacker communities in other words, the rest of us are the possible applications of quantum computation to solve very difficult problems that are effectively unsolvable using classical computers.

Ironically, many of these problems emerge when we try to use classical computers to solve quantum-mechanical problems, such as quantum chemistry problems that could be relevant for drug design and various challenges in condensed matter physics including a number related to high-temperature superconductivity.

So where are we in the wonderful and wild world of quantum computation?

In recent years, we have had many convincing demonstrations that qbits can be created, stored, manipulated and read using a number of futuristic-sounding quantum hardware platforms. But the algorithms lag. So while the prospect of quantum computing is fascinating, it will likely be a long time before we have quantum equivalents of the silicon chips that power our versatile modern computing devices.

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Google claims to have invented a quantum computer, but IBM begs to differ - The Conversation CA

In September of 2019, Peter Wittek, an assistant professor at the University of Toronto, went missing during a mountaineering expedition in the Himalayas after reportedly being caught in an avalanche. A search and rescue mission was launched but the conditions were very difficult and Wittek was not found.

Peters loss is keenly felt, said Professor Ken Corts, acting dean of the Rotman School of Management. He was the Founding Academic Director of the CDL Quantum Stream, a valued instructor in the MMA program, data scientist in residence with the TD Management Data and Analytics Lab, an exceptional contributor to Rotman and U of T and a wonderful colleague.

A ceremony to remember Wittek will take place on Feb. 3 from 3 to 4:30 pm in Desautels Hall at the Rotman School of Management.

Quantum computing and quantum machine learning an emerging field that counted Wittek as one of its few experts was the topic of his final interview inRotman Management Magazine. It is reprinted below:

You oversee the Creative Destruction Labs Quantum stream, which seeks entrepreneurs pursuing commercial opportunities at the intersection of quantum computing and machine learning. What do those opportunities look like?

Weve been running this stream for three years now, and we were definitely the first to do this in an organized way. However, the focus has shifted slightly. We are now interested in looking at any application of quantum computing.

These are still very early days for quantum computing. To give you a sense of where we are at, some people say its like the state of digital computing in the 1950s, but Id say its more like the 1930s. We dont even agree yet on what the architecture should look likeand, as a result, we are very limited with respect to the kind of applications we can build.

As a result, focusing on quantum is still quite risky. Nevertheless, so far we have had 45 companies complete our program. Not all of them survived, but a good dozen of them have raised funding. If you look at the general survival rate for AI start-ups, our record is roughly the same and given how new this technology is, that is pretty amazing.

What are the successful start-ups doing? Can you give an example of the type of problems theyre looking to solve?

At the moment I would say the main application areas are logistics and supply chain. Another promising area is life sciences, where all sorts of things can be optimized with this technology. For instance, one of our companies,Protein-Qure, is folding proteins with quantum computers.

Finance is another attractive area for these applications. In the last cohort we had a company that figured out a small niche problem where they had both the data and the expertise to provide something new and innovative; they are in the process of raising money right now. The other area where quantum makes a lot of sense is in material discovery. The reason we ever even thought of building these computers was to understand quantum materials, back in the 1980s. Today, one of our companies is figuring out how to discover new materials using quantum processing units instead of traditional supercomputers.

We have a company calledAgnostic, which is doing encryption and obfuscation for quantum computers. Right nowIBM,Rigetti ComputingandD-Wave Systemsare building quantum computers for individual users. They have access to everything that you do on the computer and can see all the data that youre sending. But if youre building a commercial application, obviously you will want tohide that. Agnostic addresses this problem by obfuscating the code you are running. One application weve seen in the life sciences is a company calledEigenMed, which addresses primary care. They provide novel machine learning algorithms for primary care by using quantum-enhanced sampling algorithms.

We also seed companies that dont end up using quantum computing. They might try out a bunch of things and discover that it doesnt work for the application they have in mind, and they end up being 100 per cent classical.StratumAIis an example of this. It uses machine learning to map out the distribution of ore bodies under the ground. The mining industry is completely underserved by technology, and this company figured out thatto beat the state-of-the-art by a significant margin, it didnt even need quantum. They just used classical machine learning and they already have million dollar contracts.

Which industries will be most affected by this technology?

Life sciences will be huge because, as indicated, it often has complex networks and probability distributions, and these are very difficult to analyze with classical computers. The way quantum computers work, this seems to be a very good fit, so that is where I expect the first killer app to come from. One company,Entropica Labs, is looking at various interactions of several genomes to identify how the combined effects cause certain types of disease. This is exactly the sort of problem that is a great fit for a quantum computer.

You touched on quantum applications in primary care. If I walked into a doctors office, how would that affect me?

Its trickybecause, like mining, primary care is vastly underserved by technology. So, if you were to use any machine learning, you would only do better. But EigenMed was actually founded by an MD. He realized that there are certain machine learning methods that we dont use simply because their computational requirements are too high but that they happen to be a very good fit for primary care, because the questions you can ask the computer are similar to what a GP would ask.

For instance, if a patient walks in with a bunch of symptoms, you can ask, What is the most likely disease? and What are the most likely other symptoms that I should verify to make sure it is what I suspect? These are the kinds of probabilistic questions that are hard to ask on current neural network architectures, but they are exactly the kind of questions that probabilistic graphical models handle well.

Are physicians and other health-care providers open to embracing this technology, or do they feel threatened by it?

First of all, health care is a heavily regulated market, so you need approval for everything. Thats not always easy to getand, as a result, it can be very difficult to obtain data. This is the same problem that any machine learning company faces. Fine, they have this excellent piece of technology and theyve mastered it,but if you dont have any good data, you dont have a company. I see that as the biggest obstacle to machine learning-based progress in health care and life sciences.

You have said that QML has the potential to bring about the next wave of technology shock. Any predictions as to what that might look like?

I think its going to be similar to what happened with deep learning. The academic breakthrough happened about nine years ago, but it took a long time to get into the public discussion. This is currently happening with AI which, at its core, is actually just very simple pattern recognition. Its almost embarrassing how simplistic AI is and yet it is already changing entire industries.

Quantum is next not just quantum machine learning but quantum computing in general. Breakthroughs are happening every day, both on the hardware side and in the kind of algorithms you can build with quantum computers. But its going to take another 10 years until it gets into public discussions and starts to disrupt industries. The companies we are seeding today are going to be the ones that eventually disrupt industries.

Alibaba is one of the companies at the forefront of embracing quantum, having already committed $15 billion to it. What is Alibaba after?

First of all, I want to say a huge thank you toAlibaba becausethe moment it made that commitment, everyone woke up and said, Hey, look: the Chinese are getting into quantum computing! Almost immediately, the U.S. government allocated $1.3 billion to invest in and develop quantum computers, and a new initiative is also coming together in Canada.

The worlds oldest commercial quantum computing company is actually from Canada:D-Wave Systemsstarted in 1999 in British Columbia. Over its 20-year history, it managed to raise over $200 million. Then Alibaba came along and announced it was committing $15 billion to quantumand this completely changed the mindset. People suddenly recognized that theres a lot of potential in this area.

What does Alibaba want from quantum? You could ask the same question ofGoogle, which is also building a quantum computer. For them, its because they want to make their search and advertisement placement even better than it already is. Eventually, this will be integrated into their core business. I think Alibaba is looking to do something similar. As indicated, one of the main application areas for quantum is logistics and supply chain. Alibaba has a lot more traffic thanAmazon. Its orders are smaller, but the volume of goods going through its warehouses is actually much larger. Any kind of improved optimization it can achieve will translate into millions of dollars in savings. My bet is that Alibabas use of quantum will be applied to something that is critical to its core operation.

The mission of CDLs Quantum stream is that, by 2022, it will have produced more revenue-generating quantum software companies than the rest of the world combined. What is the biggest challenge you face in making that a reality?

People are really waking up to all of this. There is already a venture capital firm that focuses exclusively on quantum technologies. So, the competition is steep, but we are definitely leading in terms of the number of companies created. In Canada, the investment community is a bit slow to put money into these ventures. But every year we are recruiting better and better people and the cohorts are more and more focused and, as a result, I think we are going to see more and more success stories.

It seems like everyone is interested in quantum andthey are thinking about investing in it, but they are all waiting for somebody else to make the first move. Im waiting for that barrier to break and, in the meantime, we are making progress.Xanadujust raised $32 million in Series A financing, which indicates that it has shown progress in building its business model and demonstrated the potential to grow and generate revenue. ProteinQure raised a seed of around $4 million dollars. And another company,BlackBrane, raised $2 million. So, already, there are some very decent financing rounds happening around quantum. It will take lots of hard work, but I believe we will reach our goal.

Peter Wittekwas an Assistant Professor at the Rotman School of Management and Founding Academic Director of the Creative Destruction Labs Quantum stream. The author ofQuantum Machine Learning: What Quantum Computing Means to Data Mining(Academic Press, 2016),he was also a Faculty Affiliate at the Vector Institute for Artificial Intelligence and the Perimeter Institute for Theoretical Physics.

This article appeared in theWinter 2020 issueof Rotman ManagementMagazine.Published by the University of Torontos Rotman School of Management,Rotman Managementexplores themes of interest to leaders, innovators and entrepreneurs.

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U of T's Peter Wittek, who will be remembered at Feb. 3 event, on why the future is quantum - News@UofT

Nicely timed to drop on the final day of Windows 7 support, Windows 10 received a fix to an extremely serious flaw in crypt32.dll. This flaw was reported by the good guys at the NSA. (We know it was the good guys, because they reported it rather than used it to spy on us.) Its really bad. If youre running Windows 10, go grab the update now. OK, youre updated? Good, lets talk about it now.

The flaw applies to X.509 keys that use elliptic curve cryptography. Weve discussed ECC in the past, but lets review. Public key encryption is based on the idea that some calculations are very easy to perform and verify, but extremely difficult to calculate the reverse operation.

The historic calculation is multiplying large primes, as its unreasonably difficult to factorize that result by a conventional computer. A true quantum computer with enough qubits will theoretically be able to factorize those numbers much quicker than a classical computer, so the crypto community has been searching for a replacement for years. The elliptic curve is the solution that has become the most popular. An agreed-upon curve and initial vector are all that is needed to perform the ECC calculation.

There are potential weaknesses in ECC. One such weakness is that not all curves are created equal. A well constructed curve results in good cryptography, but there are weak curves that result in breakable encryption.

With that foundation laid, the flaw itself is relatively easy to understand. An X.509 certificate can define its own curve. The Windows 10 implementation doesnt properly check the curve that is specified. A malicious curve is specified that is similar to the expected curve similar enough that the checks in crypt32 dont catch it.

Imagine this scenario. You get an email, click a link, and immediately realize that this isnt the page you thought it was. Close that tab, and all is well, right? You didnt actually fall for the fishing scam. Well, [Alex Birsan] has bad news, in the form of a clever attack based off a Cross-Site Script Inclusion (XSSI) vulnerability in the Paypal login flow.

XSSI is similar to its other cross-site scripting brethren, but rather than running malicious code on a target web page, it runs a script from another web service on a malicious web page. Its pretty common to include a JavaScript script from a different domain. The difference here is that not all JS scripts are intended to be included in other pages. Paypal had such a script. Visiting a malicious web page could load that script in your browser, and if youve ever logged in to Paypal, it would contain a set of valid session keys. The attacker could make a few password attempts using those credentials, which triggered a CAPTCHA request.

That CAPTCHA request is important. When the CAPTCHA form is filled, it launched a self-submitting form that contained the plain text username and password. Yikes! Once a user logged back in to Paypal, that CAPTCHA page could be run again, with the stolen session key, and the username and password easily recovered by the attacker. Thankfully, [Alex] disclosed the vulnerability to Paypal, who fixed it and paid him a nice tidy sum for his work.

Brought to us by Phoronix, Intel is in the process of mitigating a problem in their integrated GPU cores. Even in Intels disclosure, there isnt a whole lot of detail, but it seems to be another information leak in the same vein as Meltdown and Spectre.

The solution, at least in the Linux kernel, is to reset the iGPU between context switches. On 7th generation processors in particular, the performance hit to GPU is pretty severe. Considering the less than stellar video performance of those chips, losing 50% performance to this mitigation is quite the blow.

Use a cable modem? Theres a decent chance it has a Broadcom chip in it, and is vulnerable to Cablehaunt. A group of researchers found a way to download the current modem settings, which started a hunt for vulnerabilities. They found a spectrum analyzer page that responds to JSON requests. Naturally, the JSON parser isnt written defensively. A long enough value in a request overflows the buffer, and the processor and microkernel that system runs doesnt have any modern mitigation. Getting from access to the open port to malicious modification is a nearly trivial task. Check out the page for more details, as well as instructions for how to test your modem.

At this point, if you have any Cisco equipment you can put your hands on, unplug it now before the long weekend of patching that you have ahead of you. [Steven Seeley] did an audit of the Cisco Data Center Network Manager. While he found multiple security problems, the glaring issue is a hardcoded authentication key. Yes, another Cisco product had a backdoor left in a production unit. There are deserialization bugs, SQL injection vulnerabilities, and plenty more to wade through, so go check it out if you want the gritty details.

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This Week In Security: Windows 10 Apocalypse, Paypal Problems, And Cablehaunt - Hackaday

Xanadu's unique photonic approach to quantum computing will be much more energy efficient than traditional computing methods, thereby saving energy and emissions from power generation.

TORONTO, Jan. 16, 2020 /PRNewswire/ - Xanadu, a Canadian quantum hardware and technology company has received a $4.4M investment from Sustainable Development Technology Canada (SDTC). The investment will expedite the development of Xanadu's photonic quantum computers and make them available over the cloud. This project will also further the company's overall progress towards the construction of energy-efficient universal quantum computers.

Xanadu (CNW Group/Xanadu)

"Canadian cleantech entrepreneurs are tackling problems across Canada and in every sector. I have never been more positive about the future. The quantum hardware technology that Xanadu is building will develop quantum computers with the ability to solve extremely challenging computational problems, completing chemical calculations in minutes which would otherwise require a million CPUs in a data center," said Leah Lawrence, President and CEO, Sustainable Development Technology Canada.

Despite efforts to improve the power efficiency of traditional computing methods, the rapid growth of data centres and cloud computing presents a major source of new electricity consumption. In comparison to classical computing, quantum computing systems have the benefit of performing certain tasks and algorithms at an unprecedented rate. This will ultimately reduce the requirements for electrical power and the accompanying air and water emissions associated with electricity production.

Xanadu is developing a unique type of quantum computer, based on photonic technology, which is inherently more power-efficient than electronics. Xanadu's photonic approach uses laser light to carry information through optical chips, rather than the electrons or ions used by their competitors. By using photonic technology, Xanadu's quantum computers will one day have the ability to perform calculations at room temperature, and eliminate the bulky and power-hungry cooling systems required by most other types of quantum computers.

The project will be undertaken by Xanadu's team of in-house scientists, with collaboration from the University of Toronto and Swiftride. The project will be carried out over three years and will encompass the development of Xanadu's architecture, hardware, software and client interfaces with the overall goal of expediting the development of the company's technology, and demonstrating the practical benefits of quantum computing for users and customers by the end of 2022.

"We are thrilled by the recognition and support that we are receiving from SDTC for the development of our technology. We firmly believe that our unique, photonic-based approach to quantum computing will deliver both valuable insights and tangible environmental benefits for our customers and partners," said Christian Weedbrook, CEO of Xanadu.

About XanaduXanadu is a photonic quantum hardware company. We build integrated photonic chips that can be used in quantum computing, communication and sensing systems. The company's mission is to build quantum computers that are useful and available to people everywhere, visitwww.xanadu.ai or follow us on Twitter @XanaduAI.

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Xanadu Receives $4.4M Investment from SDTC to Advance its Photonic Quantum Computing Technology - Yahoo Finance