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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, -0.18% 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, +0.03% 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, -0.82% and Microsoft Corp. MSFT, +0.84% 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

The technology has helped to improve congestion by 73 per cent in scenario-testing

Ford and Microsoft are using quantum-inspired computing technology to reduce traffic congestion. Through a joint research pilot, scientists have used the technology to simulate thousands of vehicles and their impact on congestion in the US city of Seattle.

Ford said it is still early in the project but encouraging progress has been made and it is further expanding its partnership with the tech giant.

The companies teamed up in 2018 to develop new quantum approaches running on classical computers already available to help reduce Seattles traffic congestion.

Writing on a blog post on Medium.com, Dr Ken Washington, chief technology officer, Ford Motor Company, explained that during rush hour, numerous drivers request the shortest possible routes at the same time, but current navigation services handle these requests "in a vacuum": They do not take into consideration the number of similar incoming requests, including areas where other drivers are all planning to share the same route segments, when delivering results.

What is required is a more balanced routing system that could manage all the various route requests from drivers and provide optimised route suggestions, reducing the number of vehicles on a particular road.

These and more are all variables well need to test for to ensure balanced routing can truly deliver tangible improvements for cities.

Traditional computers dont have the computational power to do this but, as Washington explained, in a quantum computer, information is processed by a quantum bit (or a qubit) and can simultaneously exist "in two different states" before it gets measured.

This ultimately enables a quantum computer to process information with a faster speed, he wrote. Attempts to simulate some specific features of a quantum computer on non-quantum hardware have led to quantum-inspired technology powerful algorithms that mimic certain quantum behaviours and run on specialised conventional hardware. That enables organisations to start realising some benefits before fully scaled quantum hardware becomes available."

Working with Microsoft, Ford tested several different possibilities, including a scenario involving as many as 5,000 vehicles each with 10 different route choices available to them simultaneously requesting routes across Metro Seattle. It reports that in 20 seconds, balanced routing suggestions were delivered to the vehicles that resulted in a 73 per cent improvement in total congestion when compared to selfish routing.

The average commute time, meanwhile, was also cut by eight per cent representing an annual reduction of more than 55,000 hours across this simulated fleet.

Based on these results, Ford is expanding its partnership with Microsoft to further improve the algorithm and understand its effectiveness in more real-world scenarios.

For example, will this method still deliver similar results when some streets are known to be closed, if route options arent equal for all drivers, or if some drivers decide to not follow suggested routes? wrote Washington. These and more are all variables well need to test for to ensure balanced routing can truly deliver tangible improvements for cities.

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Could quantum computing be the key to cracking congestion? - SmartCitiesWorld

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

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.

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

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

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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