Category Archives: Quantum Computing

A close-up view of the IBM Q quantum computer. The processor is in the silver-colored cylinder.

Quantum computers are getting a lot more real. No, you won't be playing Call of Duty on one anytime soon. But Google, Amazon, Microsoft, Rigetti Computing and IBM all made important advances in 2019 that could help bring computers governed by the weird laws of atomic-scale physics into your life in other ways.

Google's declaration of quantum supremacywas the most headline-grabbing moment in the field. The achievement -- more limited than the grand term might suggest -- demonstrated that quantum computers could someday tackle computing problems beyond the reach of conventional "classical" computers.

Proving quantum computing progress is crucial. We're still several breakthroughs away from realizing the full vision of quantum computing. Qubits, the tiny stores of data that quantum computers use, need to be improved. So do the finicky control systems used to program and read quantum computer results. Still, today's results help justify tomorrow's research funding to sustain the technology when the flashes of hype inevitably fizzle.

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Quantum computers will live in data centers, not on your desk, when they're commercialized. They'll still be able to improve many aspects of your life, though. Money in your retirement account might grow a little faster and your packages might be delivered a little sooner as quantum computers find new ways to optimize businesses. Your electric-car battery might be a little lighter and new drugs might help you live a little longer after quantum computers unlock new molecular-level designs. Traffic may be a little lighter from better simulations.

But Google's quantum supremacy step was just one of many needed to fulfill quantum computing's promise.

"We're going to get there in cycles. We're going to have a lot of dark ages in which nothing happens for a long time," said Forrester analyst Brian Hopkins. "One day that new thing will really change the world."

Among the developments in 2019:

Classical computers, which include everything from today's smartwatches to supercomputers that occupy entire buildings, store data as bits that represent either a 1 or a 0. Quantum computers use a different approach called qubits that can represent a combination of 1 and 0 through an idea called superposition.

Ford and Microsoft adapted a quantum computing traffic simulation to run on a classical computer. The result: a traffic routing algorithm that could cut Seattle traffic congestion by 73%.

The states of multiple qubits can be linked, letting quantum computers explore lots of possible solutions to a problem at once. With each new qubit added, a quantum computer can explore double the number of possible solutions, an exponential increase not possible with classical machines.

Quantum computers, however, are finicky. It's hard to get qubits to remain stable long enough to return useful results. The act of communicating with qubits can perturb them. Engineers hope to add error correction techniques so quantum computers can tackle a much broader range of problems.

Plenty of people are quantum computing skeptics. Even some fans of the technology acknowledge we're years away from high-powered quantum computers. But already, quantum computing is a real business. Samsung, Daimler, Honda, JP Morgan Chase and Barclays are all quantum computing customers. Spending on quantum computers should reach hundreds of millions of dollars in the 2020s, and tens of billions in the 2030s, according to forecasts by Deloitte, a consultancy. China, Europe, the United States and Japan have sunk billions of dollars into investment plans. Ford and Microsoft say traffic simulation technology for quantum computers, adapted to run on classical machines, already is showing utility.

Right now quantum computers are used mostly in research. But applications with mainstream results are likely coming. The power of quantum computers is expected to allow for the creation of new materials, chemical processes and medicines by giving insight into the physics of molecules. Quantum computers will also help for greater optimization of financial investments, delivery routes and flights by crunching the numbers in situations with a large number of possible courses of action.

They'll also be used for cracking today's encryption, an idea spy agencies love, even if you might be concerned about losing your privacy or some snoop getting your password. New cryptography adapted for a quantum computing future is already underway.

Another promising application is artificial intelligence, though that may be years in the future.

"Eventually we'll be able to reinvent machine learning," Forrester's Hopkinssaid. But it'll take years of steady work in quantum computing beyond the progress of 2019. "The transformative benefits are real and big, but they are still more sci-fi and theory than they are reality."

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Quantum computing leaps ahead in 2019 with new power and speed - CNET

When asked what invention will be as revolutionary in the 2020s as smartphones were in the 2010s, Bank of America strategist Haim Isreal said, without hesitation, quantum computing.

At the banks annual year ahead event last week in New York, Israel qualified his prediction, arguing in an interview with MarketWatch that the timing of the smartphones arrival on the scene in the mid-2000s, and its massive impact on the American business landscape in the 2010s, doesnt line up neatly with quantum-computing breakthroughs, which are only now being seen, just a few weeks before the start of the 2020s.

The iPhone already debuted in 2007, enabling its real impact to be felt in the 2010s, he said, while the first business applications for quantum computing won't be seen till toward the end of the coming decade.

But, Israel argued, when all is said and done, quantum computing could be an even more radical technology in terms of its impact on businesses than the smartphone has been. This is going to be a revolution, he said.

Quantum computing is a nascent technology based on quantum theory in physics which explains the behavior of particles at the subatomic level, and states that until observed these particles can exist in different places at the same time. While normal computers store information in ones and zeros, quantum computers are not limited by the binary nature of current data processing and so can provide exponentially more computing power.

Quantum things can be in multiple places at the same time, said Chris Monroe, a University of Maryland physicist and founder of IonQ told the Associated Press . The rules are very simple, theyre just confounding.

In October, Alphabet Inc. GOOG, -0.01% subsidiary Google claimed to have achieved a breakthrough by using a quantum computer to complete a calculation in 200 seconds on a 53-qubit quantum computing chip, a task it calculated would take the fastest current super-computer 10,000 years. Earlier this month, Amazon.com Inc. AMZN, +0.41% announced its intention to collaborate with experts to develop quantum computing technologies that can be used in conjunction with its cloud computing services. International Business Machines Corp. IBM, -0.91% and Microsoft Corp. MSFT, +0.83% are also developing quantum computing technology.

Israel argued these tools will revolutionize several industries, including health care, the internet of things and cyber security. He said that pharmaceutical companies are most likely to be the first commercial users of these devices, given the explosion of data created by health care research.

Pharma companies are right now subject to Moores law in reverse, he said. They are seeing the cost of drug development doubling every nine years, as the amount of data on the human body becomes ever more onerous to process. Data on genomics doubles every 50 days, he added, arguing that only quantum computers will be able to solve the pharmaceutical industrys big-data problem.

Quantum computing will also have a major impact on cybersecurity, an issue that effects nearly every major corporation today. Currently cyber security relies on cryptographic algorithms, but quantum computings ability to solve these equations in the fraction of the time a normal computer does will render current cyber security methods obsolete.

In the future, even robust cryptographic algorithms will be substantially weakened by quantum computing, while others will no longer be secure at all, according to Swaroop Sham, senior product marketing manager at Okta.

For investors, Israel said, it is key to realize that the first one or two companies to develop commercially applicable quantum-computing will be richly rewarded with access to untold amounts of data and that will only make their software services more valuable to potential customers in a virtuous circle.

What weve learned this decade is that whoever controls the data will win big time, he said.

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Quantum computing will be the smartphone of the 2020s, says Bank of America strategist - MarketWatch

More than half (54%) of cybersecurity professionals have expressed concerns that quantum computing will outpace the development of other security tech, according to a research from Neustar.

Keeping a watchful eye on developments, 74% of organizations admitted to paying close attention to the technologys evolution, with 21% already experimenting with their own quantum computing strategies.

A further 35% of experts claimed to be in the process of developing a quantum strategy, while just 16% said they were not yet thinking about it. This shift in focus comes as the vast majority (73%) of cyber security professionals expect advances in quantum computing to overcome legacy technologies, such as encryption, within the next five years.

Almost all respondents (93%) believe the next-generation computers will overwhelm existing security technology, with just 7% under the impression that true quantum supremacy will never happen.

Despite expressing concerns that other technologies will be overshadowed, 87% of CISOs, CSOs, CTOs and security directors are excited about the potential positive impact of quantum computing. The remaining 13% were more cautious and under the impression that the technology would create more harm than good.

At the moment, we rely on encryption, which is possible to crack in theory, but impossible to crack in practice, precisely because it would take so long to do so, over timescales of trillions or even quadrillions of years, said Rodney Joffe, Chairman of NISC and Security CTO at Neustar.

Without the protective shield of encryption, a quantum computer in the hands of a malicious actor could launch a cyberattack unlike anything weve ever seen.

For both todays major attacks, and also the small-scale, targeted threats that we are seeing more frequently, it is vital that IT professionals begin responding to quantum immediately.

The security community has already launched a research effort into quantum-proof cryptography, but information professionals at every organization holding sensitive data should have quantum on their radar.

Quantum computings ability to solve our great scientific and technological challenges will also be its ability to disrupt everything we know about computer security. Ultimately, IT experts of every stripe will need to work to rebuild the algorithms, strategies, and systems that form our approach to cybersecurity, added Joffe.

The report also highlighted a steep two-year increase on the International Cyber Benchmarks Index. Calculated based on changes in the cybersecurity landscape including the impact of cyberattacks and changing level of threat November 2019 saw the highest score yet at 28.2. In November 2017, the benchmark sat at just 10.1, demonstrating an 18-point increase over the last couple of years.

During September October 2019, security professionals ranked system compromise as the greatest threat to their organizations (22%), with DDoS attacks and ransomware following very closely behind (21%).

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Will quantum computing overwhelm existing security tech in the near future? - Help Net Security

Quantum is having a moment. In October, Google claimed to have achieved a quantum supremacy milestone. In November, Microsoft announced Azure Quantum, a cloud service that lets you tap into quantum hardware providers Honeywell, IonQ, or QCI. Last week, AWS announced Amazon Braket, a cloud service that lets you tap into quantum hardware providers D-Wave, IonQ, and Rigetti. At the Q2B 2019 quantum computing conference this week, I got a pulse for how the nascent industry is feeling.

Binary digits (bits) are the basic units of information in classical computing, while quantum bits (qubits) make up quantum computing. Bits are always in a state of 0 or 1, while qubits can be in a state of 0, 1, or a superposition of the two. Quantum computing leverages qubits to perform computations that would be much more difficult for a classical computer. Potential applications are so vast and wide (from basic optimization problems to machine learning to all sorts of modeling) that interested industries span finance, chemistry, aerospace, cryptography, and more. But its still so early that the industry is nowhere close to reaching consensus on what the transistor for qubits should look like.

Currently, your cloud quantum computing options are limited to single hardware providers, such as those from D-Wave and IBM. Amazon and Microsoft want to change that.

Enterprises and researchers interested in testing and experimenting with quantum are excited because they will be able to use different quantum processors via the same service, at least in theory. Theyre uneasy, however, because the quantum processors are so fundamentally different that its not clear how easy it will be to switch between them. D-Wave uses quantum annealing, Honeywell and IonQ use ion trap devices, and Rigetti and QCI use superconducting chips. Even the technologies that are the same have completely different architectures.

Entrepreneurs and enthusiasts are hopeful that Amazon and Microsoft will make it easier to interface with the various quantum hardware technologies. Theyre uneasy, however, because Amazon and Microsoft have not shared pricing and technical details. Plus, some of the quantum providers offer their own cloud services, so it will be difficult to suss out when it makes more sense to work with them directly.

The hardware providers themselves are excited because they get exposure to massive customer bases. Amazon and Microsoft are the worlds biggest and second biggest cloud providers, respectively. Theyre uneasy, however, because the tech giants are really just middlemen, which of course poses its own problems of costs and reliance.

At least right now, it looks like this will be the new normal. Even hardware providers that havent announced they are partnering with Amazon and/or Microsoft, like Xanadu, are in talks to do just that.

Overall at the event, excitement trumped uneasiness. If youre participating in a domain as nascent as quantum, you must be optimistic. The news this quarter all happened very quickly, but there is still a long road ahead. After all, these cloud services have only been announced. They still have to become available, gain exposure, pick up traction, become practical, prove useful, and so on.

The devil is in the details. How much are these cloud services for quantum going to cost? Amazon and Microsoft havent said. When exactly will they be available in preview or in beta? Amazon and Microsoft havent said. How will switching between different quantum processors work in practice? Amazon and Microsoft havent said.

One thing is clear. Everyone at the event was talking about the impact of the two biggest cloud providers offering quantum hardware from different companies. The clear winners? Amazon and Microsoft.

ProBeat is a column in which Emil rants about whatever crosses him that week.

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ProBeat: AWS and Azure are generating uneasy excitement in quantum computing - VentureBeat

What if we could do chemistry inside a computer instead of in a test tube or beaker in the laboratory? What if running a new experiment was as simple as running an app and having it completed in a few seconds?

For this to really work, we would want it to happen with complete fidelity. The atoms and molecules as modeled in the computer should behave exactly like they do in the test tube. The chemical reactions that happen in the physical world would have precise computational analogs. We would need a completely accurate simulation.

If we could do this at scale, we might be able to compute the molecules we want and need.

These might be for new materials for shampoos or even alloys for cars and airplanes. Perhaps we could more efficiently discover medicines that are customized to your exact physiology. Maybe we could get a better insight into how proteins fold, thereby understanding their function, and possibly creating custom enzymes to positively change our body chemistry.

Is this plausible? We have massive supercomputers that can run all kinds of simulations. Can we model molecules in the above ways today?

This article is an excerpt from the book Dancing with Qubits written by Robert Sutor. Robert helps you understand how quantum computing works and delves into the math behind it with this quantum computing textbook.

Lets start with C8H10N4O2 1,3,7-Trimethylxanthine.

This is a very fancy name for a molecule that millions of people around the world enjoy every day: caffeine. An 8-ounce cup of coffee contains approximately 95 mg of caffeine, and this translates to roughly 2.95 10^20 molecules. Written out, this is

295, 000, 000, 000, 000, 000, 000 molecules.

A 12 ounce can of a popular cola drink has 32 mg of caffeine, the diet version has 42 mg, and energy drinks often have about 77 mg.

These numbers are large because we are counting physical objects in our universe, which we know is very big. Scientists estimate, for example, that there are between 10^49 and 10^50 atoms in our planet alone.

To put these values in context, one thousand = 10^3, one million = 10^6, one billion = 10^9, and so on. A gigabyte of storage is one billion bytes, and a terabyte is 10^12 bytes.

Getting back to the question I posed at the beginning of this section, can we model caffeine exactly on a computer? We dont have to model the huge number of caffeine molecules in a cup of coffee, but can we fully represent a single molecule at a single instant?

Caffeine is a small molecule and contains protons, neutrons, and electrons. In particular, if we just look at the energy configuration that determines the structure of the molecule and the bonds that hold it all together, the amount of information to describe this is staggering. In particular, the number of bits, the 0s and 1s, needed is approximately 10^48:

10, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000.

And this is just one molecule! Yet somehow nature manages to deal quite effectively with all this information. It handles the single caffeine molecule, to all those in your coffee, tea, or soft drink, to every other molecule that makes up you and the world around you.

How does it do this? We dont know! Of course, there are theories and these live at the intersection of physics and philosophy. However, we do not need to understand it fully to try to harness its capabilities.

We have no hope of providing enough traditional storage to hold this much information. Our dream of exact representation appears to be dashed. This is what Richard Feynman meant in his quote: Nature isnt classical.

However, 160 qubits (quantum bits) could hold 2^160 1.46 10^48 bits while the qubits were involved in a computation. To be clear, Im not saying how we would get all the data into those qubits and Im also not saying how many more we would need to do something interesting with the information. It does give us hope, however.

In the classical case, we will never fully represent the caffeine molecule. In the future, with enough very high-quality qubits in a powerful quantum computing system, we may be able to perform chemistry on a computer.

I can write a little app on a classical computer that can simulate a coin flip. This might be for my phone or laptop.

Instead of heads or tails, lets use 1 and 0. The routine, which I call R, starts with one of those values and randomly returns one or the other. That is, 50% of the time it returns 1 and 50% of the time it returns 0. We have no knowledge whatsoever of how R does what it does.

When you see R, think random. This is called a fair flip. It is not weighted to slightly prefer one result over the other. Whether we can produce a truly random result on a classical computer is another question. Lets assume our app is fair.

If I apply R to 1, half the time I expect 1 and another half 0. The same is true if I apply R to 0. Ill call these applications R(1) and R(0), respectively.

If I look at the result of R(1) or R(0), there is no way to tell if I started with 1 or 0. This is just like a secret coin flip where I cant tell whether I began with heads or tails just by looking at how the coin has landed. By secret coin flip, I mean that someone else has flipped it and I can see the result, but I have no knowledge of the mechanics of the flip itself or the starting state of the coin.

If R(1) and R(0) are randomly 1 and 0, what happens when I apply R twice?

I write this as R(R(1)) and R(R(0)). Its the same answer: random result with an equal split. The same thing happens no matter how many times we apply R. The result is random, and we cant reverse things to learn the initial value.

There is a catch, though. You are not allowed to look at the result of what H does if you want to reverse its effect. If you apply H to 0 or 1, peek at the result, and apply H again to that, it is the same as if you had used R. If you observe what is going on in the quantum case at the wrong time, you are right back at strictly classical behavior.

To summarize using the coin language: if you flip a quantum coin and then dont look at it, flipping it again will yield heads or tails with which you started. If you do look, you get classical randomness.

A second area where quantum is different is in how we can work with simultaneous values. Your phone or laptop uses bytes as individual units of memory or storage. Thats where we get phrases like megabyte, which means one million bytes of information.

A byte is further broken down into eight bits, which weve seen before. Each bit can be a 0 or 1. Doing the math, each byte can represent 2^8 = 256 different numbers composed of eight 0s or 1s, but it can only hold one value at a time. Eight qubits can represent all 256 values at the same time

This is through superposition, but also through entanglement, the way we can tightly tie together the behavior of two or more qubits. This is what gives us the (literally) exponential growth in the amount of working memory.

Artificial intelligence and one of its subsets, machine learning, are extremely broad collections of data-driven techniques and models. They are used to help find patterns in information, learn from the information, and automatically perform more intelligently. They also give humans help and insight that might have been difficult to get otherwise.

Here is a way to start thinking about how quantum computing might be applicable to large, complicated, computation-intensive systems of processes such as those found in AI and elsewhere. These three cases are in some sense the small, medium, and large ways quantum computing might complement classical techniques:

As I write this, quantum computers are not big data machines. This means you cannot take millions of records of information and provide them as input to a quantum calculation. Instead, quantum may be able to help where the number of inputs is modest but the computations blow up as you start examining relationships or dependencies in the data.

In the future, however, quantum computers may be able to input, output, and process much more data. Even if it is just theoretical now, it makes sense to ask if there are quantum algorithms that can be useful in AI someday.

To summarize, we explored how quantum computing works and different applications of artificial intelligence in quantum computing.

Get this quantum computing book Dancing with Qubits by Robert Sutor today where he has explored the inner workings of quantum computing. The book entails some sophisticated mathematical exposition and is therefore best suited for those with a healthy interest in mathematics, physics, engineering, and computer science.

Intel introduces cryogenic control chip, Horse Ridge for commercially viable quantum computing

Microsoft announces Azure Quantum, an open cloud ecosystem to learn and build scalable quantum solutions

Amazon re:Invent 2019 Day One: AWS launches Braket, its new quantum service and releases

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Quantum expert Robert Sutor explains the basics of Quantum Computing - Packt Hub

Google announced this fall to much fanfare that it had demonstrated quantum supremacy that is, it performed a specific quantum computation far faster than the best classical computers could achieve. IBM promptly critiqued the claim, saying that its own classical supercomputer could perform the computation at nearly the same speed with far greater fidelity and, therefore, the Google announcement should be taken with a large dose of skepticism.

This wasnt the first time someone cast doubt on quantum computing. Last year, Michel Dyakonov, a theoretical physicist at the University of Montpellier in France, offered a slew of technical reasons why practical quantum supercomputers will never be built in an article in IEEE Spectrum, the flagship journal of electrical and computer engineering.

So how can you make sense of what is going on?

As someone who has worked on quantum computing for many years, I believe that due to the inevitability of random errors in the hardware, useful quantum computers are unlikely to ever be built.

Whats a quantum computer?

To understand why, you need to understand how quantum computers work since theyre fundamentally different from classical computers.

A classical computer uses 0s and 1s to store data. These numbers could be voltages on different points in a circuit. But a quantum computer works on quantum bits, also known as qubits. You can picture them as waves that are associated with amplitude and phase.

Qubits have special properties: They can exist in superposition, where they are both 0 and 1 at the same time, and they may be entangled so they share physical properties even though they may be separated by large distances. Its a behavior that does not exist in the world of classical physics. The superposition vanishes when the experimenter interacts with the quantum state.

Due to superposition, a quantum computer with 100 qubits can represent 2100 solutions simultaneously. For certain problems, this exponential parallelism can be harnessed to create a tremendous speed advantage. Some code-breaking problems could be solved exponentially faster on a quantum machine, for example.

There is another, narrower approach to quantum computing called quantum annealing, where qubits are used to speed up optimization problems. D-Wave Systems, based in Canada, has built optimization systems that use qubits for this purpose, but critics also claim that these systems are no better than classical computers.

Regardless, companies and countries are investing massive amounts of money in quantum computing. China has developed a new quantum research facility worth US$10 billion, while the European Union has developed a 1 billion ($1.1 billion) quantum master plan. The United States National Quantum Initiative Act provides $1.2 billion to promote quantum information science over a five-year period.

Breaking encryption algorithms is a powerful motivating factor for many countries if they could do it successfully, it would give them an enormous intelligence advantage. But these investments are also promoting fundamental research in physics.

Many companies are pushing to build quantum computers, including Intel and Microsoft in addition to Google and IBM. These companies are trying to build hardware that replicates the circuit model of classical computers. However, current experimental systems have less than 100 qubits. To achieve useful computational performance, you probably need machines with hundreds of thousands of qubits.

Noise and error correction

The mathematics that underpin quantum algorithms is well established, but there are daunting engineering challenges that remain.

For computers to function properly, they must correct all small random errors. In a quantum computer, such errors arise from the non-ideal circuit elements and the interaction of the qubits with the environment around them. For these reasons the qubits can lose coherency in a fraction of a second and, therefore, the computation must be completed in even less time. If random errors which are inevitable in any physical system are not corrected, the computers results will be worthless.

In classical computers, small noise is corrected by taking advantage of a concept known as thresholding. It works like the rounding of numbers. Thus, in the transmission of integers where it is known that the error is less than 0.5, if what is received is 3.45, the received value can be corrected to 3.

Further errors can be corrected by introducing redundancy. Thus if 0 and 1 are transmitted as 000 and 111, then at most one bit-error during transmission can be corrected easily: A received 001 would be a interpreted as 0, and a received 101 would be interpreted as 1.

Quantum error correction codes are a generalization of the classical ones, but there are crucial differences. For one, the unknown qubits cannot be copied to incorporate redundancy as an error correction technique. Furthermore, errors present within the incoming data before the error-correction coding is introduced cannot be corrected.

Quantum cryptography

While the problem of noise is a serious challenge in the implementation of quantum computers, it isnt so in quantum cryptography, where people are dealing with single qubits, for single qubits can remain isolated from the environment for significant amount of time. Using quantum cryptography, two users can exchange the very large numbers known as keys, which secure data, without anyone able to break the key exchange system. Such key exchange could help secure communications between satellites and naval ships. But the actual encryption algorithm used after the key is exchanged remains classical, and therefore the encryption is theoretically no stronger than classical methods.

Quantum cryptography is being commercially used in a limited sense for high-value banking transactions. But because the two parties must be authenticated using classical protocols, and since a chain is only as strong as its weakest link, its not that different from existing systems. Banks are still using a classical-based authentication process, which itself could be used to exchange keys without loss of overall security.

Quantum cryptography technology must shift its focus to quantum transmission of information if its going to become significantly more secure than existing cryptography techniques.

Commercial-scale quantum computing challenges

While quantum cryptography holds some promise if the problems of quantum transmission can be solved, I doubt the same holds true for generalized quantum computing. Error-correction, which is fundamental to a multi-purpose computer, is such a significant challenge in quantum computers that I dont believe theyll ever be built at a commercial scale.

[ Youre smart and curious about the world. So are The Conversations authors and editors. You can get our highlights each weekend. ]

Subhash Kak, Regents Professor of Electrical and Computer Engineering, Oklahoma State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image: Reuters

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Quantum Computers Are the Ultimate Paper Tiger - The National Interest Online

A team of scientists from the University of Chicago discovered a method by which quantum states can be integrated and controlled in everyday electronics. The teams breakthrough research resulted in the experimental creation of what theyre dubbing a quantum FM radio to transmit data over long distances. This feels like an eureka moment for quantum computing.

The teams work involves silicon carbide, a naturally occurring semiconductor used to make all sorts of electronics including light emitting diodes (LEDs) and circuit boards. Its also used in rocketry due to its ability to withstand high temperatures and in the production of sand paper presumably because its coarse. What were excited about is its potential as a conduit for controlling quantum states.

Todays quantum computers under the IBM/Google/MIT paradigm are giant, unwieldy things that absolutely wont fit on your desktop. They require lasers and sub-zero temperatures to function. You need a team of physicists standing by in an expensive laboratory just to get started. But the University of Chicago teams work may change all that.

They used good old fashioned electricity, something were pretty good at controlling, to initiate and direct quantum states in silicon carbide. That means they didnt need fancy lasers, a super cold environment, or any of that mainframe-sized stuff to produce quantum results. This wasnt the result of a single experiment, but in fact involved two significant breakthroughs.

The first, the ability to control quantum states in silicon carbide, has the potential to solve quantum computings exotic materials problem. Silicon carbide is plentiful and relatively easy to work with compared to the standard-fair physicists use which includes levitated atoms, laser-ready metals, and perfectly-flawed diamonds. This is cool, and could fundamentally change the direction most quantum computing research goes in 2020 and beyond. But its the second breakthrough that might be the most exciting.

According to a press release from the University of Chicago, the teams method solves quantum computings noise problem. Per Chris Anderson, a co-author on the teams paper:

Impurities are common in all semiconductor devices, and at the quantum level, these impurities can scramble the quantum information by creating a noisy electrical environment. This is a near-universal problem for quantum technologies.

Co-author Alexandre Bourassa added:

In our experiments we need to use lasers, which unfortunately jostle the electrons around. Its like a game of musical chairs with electrons; when the light goes out everything stops, but in a different configuration. The problem is that this random configuration of electrons affects our quantum state. But we found that applying electric fields removes the electrons from the system and makes it much more stable.

The work is still early, but it has incredible implications for the field of quantum computing. With a little tweaking, it appears that this silicon carbide-based method of wrangling quantum states could lead us to the unhackable quantum communications network sooner than many experts believed. According to the team, it would work with the existing fiber optic network that already transmits 90 percent of the worlds data.

On the outside, a quantum FM radio, that essentially sends data along frequency-modulated waves, could augment or replace existing wireless communication methods and bring about an entirely new class of technology. Were thinking something like Star Treks TriCorders, a gadget that records environmental data, processes it instantly, and uses quantum AI to analyze and interpret the results.

For more information read the Chicago teams research papers here and here.

H/t: Phys.Org

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Double eureka: Breakthroughs could lead to quantum 'FM radio' and the end of noise - The Next Web

Technology and society are intertwined. Self-driving cars and facial recognition technologies are no longer science fiction, and data and efficiency are harbingers of this new world.

But these new technologies are only the beginning. In the coming decades, further advances in artificial intelligence and the dawn of quantum computing are poised to change lives in both discernible and inconspicuous ways.

Even everyday technology, like a smartphone app, affects people in significant ways that they might not realize, said Fermilab scientist Daniel Bowring. If there are concerns about something as familiar as an app, then we need to take more opaque and complicated technology, like AI, very seriously.

A two-day workshop took place from Oct. 31-Nov.1 at the University of Chicago to raise awareness and generate strategies for the ethical development and implementation of AI and quantum computing. The workshop was organized by the Chicago Quantum Exchange, a Chicago-based intellectual hub and community of researchers whose aim is to promote the exploration of quantum information technologies, and funded by the Kavli Foundation and the Center for Data and Computing, a University of Chicago center for research driven by data science and AI approaches.

Members of the Chicago Quantum Exchange engage in conversation at a workshop at the University of Chicago. Photo: Anne Ryan, University of Chicago

At the workshop, industry experts, physicists, sociologists, journalists and more gathered to learn, share insights and identify next steps as AI and quantum computing advance.

AI and quantum computing are developing tools that will affect everyone, said Bowring, a member of the workshop organizing team. It was important to us to get as many stakeholders in the room as possible.

Workshop participants listened to presentations that framed concerns such as power asymmetries, algorithmic bias and privacy before breaking out into small groups to deliberate these topics and develop actionable strategies. Groups reported to all attendees after each breakout session. On the last day of the workshop, participants considered how they would nurture the dialogue.

At one of the breakout sessions, participants discussed the balance between collaborative quantum computing research and national security. Today, the results of quantum computing research are dispersed in a wide variety of academic journals, and a lot of code is accessible and open source. However, because of its potential implications for cybersecurity and encryption, quantum computing is also of interest to national security, so it may be subject to intelligence and export controls. What endeavors, if any, should be open source or private? Are these outcomes realizable? What level of control should be maintained? How should these technologies be regulated?

Were already behind on setting ground rules for these technologies, which, if left to progress on their own, could increase power asymmetries in society, said Brian Nord, Fermilab and University of Chicago scientist and member of the workshop organizing team. Our research programs, for example, need to be crafted in a way that does not reinforce or exacerbate these asymmetries.

Workshop participants will continue the dialogue through online and in-person meetings to address key ethical and societal issues in the quantum and AI space. Potential future activities include writing proposals for joint research projects that consider ethical and societal implications, white papers addressed to academic audiences, and media editorials and developing community action plans.

Organizers are planning to hold a panel next spring to engage the public, as well.

The spring event will help us continue to spread awareness and engage a variety of groups on issues of ethics in AI and quantum computing, Nord said.

The workshop was sponsored by the Kavli Foundation in partnership with the Center for Data and Computing at the University of Chicago. Artificial intelligence and quantum information science are two of six initiatives identified as special priority by the Department of Energy Office of Science.

The Kavli Foundation is dedicated to advancing science for the benefit of humanity, promoting public understanding of scientific research, and supporting scientists and their work. The foundations mission is implemented through an international program of research institutes, initiatives and symposia in the fields of astrophysics, nanoscience, neuroscience, and theoretical physics, as well as the Kavli Prize and a program in public engagement with science. Visitkavlifoundation.org.

The Chicago Quantum Exchange catalyzes research activity across disciplines and member institutions. It is anchored by the University of Chicago, Argonne National Laboratory, Fermi National Accelerator Laboratory, and the University of Illinois at Urbana-Champaign and includes the University of Wisconsin-Madison, Northwestern University and industry partners. Visit chicagoquantum.org.

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Shaping the technology transforming our society - Fermi National Accelerator Laboratory

AWS re:Invent 2019 which concluded last week marked another milestone for Amazon and the cloud computing ecosystem. Some of the new AWS services announced this year will become the foundation for upcoming products and services.

Dart Board

Though there have been many surprises, AWS didnt mention or announce some of the services that were expected by the community. My own predictions for AWS re:Invent 2019 were partially accurate.

Based on the wishlist and what was expected, here is a list of hits and misses from this years mega cloud event:

Hits of AWS re:Invent 2019

1) Quantum Computing Delivered through Amazon Braket

After IBM, Microsoft, and Google, it was Amazons turn to jump the quantum computing bandwagon.

Amazon Braket is a managed service for quantum computing that provides a development environment to explore and design quantum algorithms, test them on simulated quantum computers, and run them on different quantum hardware technologies.

This new service from Amazon lets customers use both quantum and classical tasks on a hybrid infrastructure. It is tightly integrated with existing AWS services such as S3 and CloudWatch.

Amazon Braket has the potential to become one of the key pillars of AWS compute services.

2) Leveraging Project Nitro

Project Nitro is a collection of hardware accelerators that offload hypervisor, storage, and network to custom chips freeing up resources on EC2 to deliver the best performance.

Amazon has started to launch additional EC2 instance types based on custom chips powered by the Nitro System. The Inf1 family of EC2 delivers the best of the breed hardware and software combination to accelerate machine learning model inferencing.

Along with Nitro, Amazon is also investing in ARM-based compute resources. Amazon EC2 now offers general purpose (M6g), compute optimized (C6g), and memory optimized (R6g) Amazon instances powered by AWS Graviton2 processor that use 64-bit Arm Neoverse cores and custom silicon designed by AWS.

Going forward, Amazon will launch additional instance types based on Graviton2 processors that will become cheaper alternatives to Intel x64-based instance types.

3) Augmented AI with Human in the Loop

Remember Amazon Mechanical Turk (MTurk)? A crowdsourced service that delegates jobs to real humans. Based on the learnings from applying automation to retail, Amazon encourages keeping the human in the loop.

More recently, Amazon launched SageMaker Ground Truth - the data labeling service powered by humans. Customers can upload raw datasets and have humans draw bounding boxes around specific objects identified in the images. This increases accuracy while training machine learning models.

With Amazon Augmented AI (Amazon A2I), AWS now introduces human-driven validation of machine learning models. The low-confidence predictions from an augmented AI model are sent to real humans for validation. This increases the precision and accuracy of models while performing predictions from an ML model.

Amazon continues to bring humans into the technology-driven automation loop.

4) AI-driven Code Review and Profiling through Amazon CodeGuru

Amazon CodeGuru is a managed service that helps developers proactively improve code quality and application performance through AI-driven recommendations. The service comes with a reviewer and profiler that can detect and identify issues in code. Amazon CodeGuru can review and profile Java code targeting the Java Virtual Machine.

This service was expected to come from a platform and tools vendor. Given the heritage of developer tools, I was expecting this from Microsoft. But Amazon has taken a lead in infusing AI into code review and analysis.

CodeGuru is one of my favorite announcements from AWS re:Invent 2019.

5) Decentralized Cloud Infrastructure - Local Zones and AWS Wavelength

When the competition is caught up in expanding the footprint of data centers through traditional regions and zones, Amazon has taken an unconventional approach of setting up mini data centers in each metro.

The partnership with Verizon and other telecom providers is a great move from AWS.

Both, Local Zones and AWS Wavelength are game-changers from Amazon. They redefine edge computing by providing a continuum of compute services.

Bonus: AWS DeepComposer

Having launched DeepLens in 2017 and DeepRacer in 2018, I was curious to see how AWS mixes and matches its deep learning research with a hardware-based, educational device.

AWS DeepComposer brings the power of Generative Adversarial Networks (GAN) to music composition.

Misses of AWS re:Invent 2019

1) Open Source Strategy

Open source was conspicuously missing from the keynotes at re:Invent. With a veteran like Adrian Cockroft leading the open source efforts, I was expecting Amazon to make a significant announcement related to OSS.

Amazon has many internal projects which are good candidates for open source. From machine learning to compute infrastructure, AWS has many on-going research efforts. Open sourcing a tiny subset of these projects could immensely benefit the community.

The only open source project that was talked about was Firecracker which was announced last year. Even for that, Amazon didnt mention handing it over to a governing body to drive broader contribution and participation of the community.

The industry expects Amazon to actively participate in open source initiatives.

2) Container Strategy

Containers are the building blocks of modern infrastructure. They are becoming the de facto standard to build modern, cloud native applications.

With Amazon claiming that 80% of all containerized and Kubernetes applications running in the cloud run on AWS, I expect a streamlined developer experience of deploying containerized workloads on AWS.

The current developer experience of dealing with AWS container services such as ECS, Fargate and EKS leaves a lot to be desired.

The only significant announcement from re:Invent 2019 related to containers was the general availability of the serveless container platform based on EKS for Fargate. Based on my personal experience, I found the service to be complex.

Both Microsoft and Google score high on the innovation of containerized platforms and enhancing the developer experience.

AWS has work to do in simplifying the developer workflow when dealing with containerized workloads.

3) VMware Partnership

Surprisingly, there was no discussion on the roadmap, growth and adoption of VMware Cloud on AWS. While the focus shifted to AWS Outposts, there has been no mention of the upcoming AWS managed services on VMware.

Though AWS Outposts are available on vSphere, the GA announcement had little to no mention of Outposts on VMware.

4) Simplified Developer Experience

AWS now has multiple compute services in the form of EC2 (IaaS), Beanstalk (PaaS), Lambda (FaaS) and Container Services offered through ECS, Fargate and EKS (CaaS).

Amazon recommends using a variety of tools to manage the lifecycle of the infrastructure and applications. Customers use CloudFormation, Kubernetes YAML, Cloud Developer Kit (CDK) and Serverless Application Model (SAM) to deal with each of the workloads running in different compute environments.

The current deployment model and programmability aspects of AWS are becoming increasingly complex. There is a need to simplify the developer and admin experience of AWS.

I was expecting a new programmability model from Amazon that would make it easier for developers to target AWS for running their workloads.

5) Custom AutoML Models for Offline Usage

Though AWS launched SageMaker Autopilot and Rekognition Custom Labels in the AutoML domain, it didnt mention about enhancing AutoML-based language services for newer verticals and domains.

Custom models trained through Amazons AutoML services cannot be exported for offline usage in disconnected scenarios such as industrial automation. None of the services are integrated with AWS Greengrass deployments for offline inferencing.

Both Google and Microsoft offer exporting AutoML models optimized for the edge.

Amazon Comprehend service could be easily expanded to support newer verticals and domains such as legal and finance through AutoML.

Though the above announcements and services didnt make it to this years re:Invent, I am sure they are in the roadmap.

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The Hits And Misses Of AWS re:Invent 2019 - Forbes

Its been six years since hackers linked with China breached the US Office of Personnel Managements computer system and stole sensitive information about millions of federal employees and contractors. It was the sort of information thats collected during background checks for security clearancesvery personal stuff. But not all was lost. Even though there were obviously some massive holes in the OPMs security setup, some of its data was encrypted. It was useless to the attackers.

Perhaps not for much longer. Its only a matter of time before even encrypted data is at risk. Thats the view of John Prisco, CEO of Quantum Xchange, a cybersecurity firm based in Bethesda, Maryland. Speaking at the EmTech Future Compute event last week, he said that Chinas aggressive pursuit of quantum computing suggests it will eventually have a system capable of figuring out the key to access that data. Current encryption doesnt stand much of a chance against a quantum system tasked with breaking it.

China is moving forward with a harvest today, read tomorrow approach, said Prisco. The country wants to steal as much data as possible, even if it cant access it yet, because its banking on a future when it finally can, he said. Prisco says the China is outspending the US in quantum computing 10 times over. Its allegedly spending $10 billion alone to build the National Laboratory for Quantum Information Sciences, scheduled to open next year (although this number is disputed). Americas counterpunch is just $1.2 billion over five years toward quantum information science. Were not really that safe, he said.

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Part of Chinas massive investment has gone toward quantum security itself, including the development of quantum key distribution, or QKD. This involves sending encrypted data as classical bits (strictly binary information) over a fiber-optic network, while sending the keys used to decrypt the information in the form of qubits (which can represent more than just two states, thanks to quantum superposition). The mere act of trying to observe the key changes its state, alerting the sender and receiver of a security breach.

Bu it has its limits. QKD requires sending information-carrying photons over incredibly long distances (tens to hundreds of miles). The best way to do this right now is by installing a fiber-optic network, a costly and time-consuming process.

Its not foolproof, either. The signals eventually scatter and break down over long stretches of fiber optics, so you need to build nodes that will continue to boost them forward. These networks are also point-to-point only (as opposed to a broadcast connection), so you can communicate with only one other party at a time.

Nevertheless, China looks to be all in on QKD networks. Its already built a 1,263-mile link between Beijing and Shanghai to deliver quantum keys. And a successful QKD demonstration by the Chinese Micius satellite was reported across the 4,700 miles between Beijing and Vienna.

Even Europe is making aggressive strides: the European Unions OPENQKD initiative calls for using a combination of fiber optics and satellites to create a QKD-safe communications network covering 13 nations. The US, Prisco argues, is incredibly far behind, for which he blames a lack of urgency. The closest thing it has is a 500-mile fiber-optic cable running down the East Coast. Quantum Xchange has inked a deal to use the cable to create a QKD network that secures data transfers for customers (most notably the financial companies based around New York City).

With Europe and China already taking QKD seriously, Prisco wants to see the US catch upand fast. Its a lot like the space race, he said. We really cant afford to come in second place.

Update: This story has been amended to note that the funding figures for the National Laboratory for Quantum Information Sciences are disputed among some experts.

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China is beating the US when it comes to quantum security - MIT Technology Review