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In his 2013 book, Schrdingers Killer App, Louisiana State University theoretical physicist Jonathan Dowling predicted what he called super exponential growth. He was right. Back in May, during Googles Quantum Spring Symposium, computer engineer Hartmut Neven reported the companys quantum computing chip had been gaining power at breakneck speed.

The subtext: We are venturing into an age of quantum supremacy the point at which quantum computers outperform the best classical supercomputers in solving a well-defined problem.

Engineers test the accuracy of quantum computing chips by using them to solve a problem, and then verifying the work with a classical machine. But in early 2019, that process became problematic, reported Neven, who runs Googles Quantum Artificial Intelligence Lab. Googles quantum chip was improving so quickly that his group had to commandeer increasingly large computers and then clusters of computers to check its work. Its become clear that eventually, theyll run out of machines.

Case in point: Google announced in October that its 53-qubit quantum processor had needed only 200 seconds to complete a problem that would have required 10,000 years on a supercomputer.

Nevens group observed a double exponential growth rate in the chips computing power over a few months. Plain old exponential growth is already really fast: It means that from one step to the next, the value of something multiplies. Bacterial growth can be exponential if the number of organisms doubles during an observed time interval. So can computing power of classical computers under Moores Law, the idea that it doubles roughly every year or two. But under double exponential growth, the exponents have exponents. That makes a world of difference: Instead of a progression from 2 to 4 to 8 to 16 to 32 bacteria, for example, a double-exponentially growing colony in the same time would grow from 2 to 4 to 16 to 256 to 65,536.

Neven credits the growth rate to two factors: the predicted way that quantum computers improve on the computational power of classical ones, and quick improvement of quantum chips themselves. Some began referring to this growth rate as Nevens Law. Some theorists say such growth was unavoidable.

We talked to Dowling (who suggests a more fitting moniker: the Dowling-Neven Law) about double exponential growth, his prediction and his underappreciated Beer Theory of Quantum Mechanics.

Q: You saw double exponential growth on the horizon long before it showed up in a lab. How?

A: Anytime theres a new technology, if it is worthwhile, eventually it kicks into exponential growth in something. We see this with the internet, we saw this with classical computers. You eventually hit a point where all of the engineers figure out how to make this work, miniaturize it and then you suddenly run into exponential growth in terms of the hardware. If it doesnt happen, that hardware falls off the face of the Earth as a nonviable technology.

Q: So you werent surprised to see Googles chip improving so quickly?

A: Im only surprised that it happened earlier than I expected. In my book, I said within the next 50 to 80 years. I guessed a little too conservatively.

Q: Youre a theoretical physicist. Are you typically conservative in your predictions?

People say Im fracking nuts when I publish this stuff. I like to think that Im the crazy guy that always makes the least conservative prediction. I thought this was far-out wacky stuff, and I was making the most outrageous prediction. Thats why its taking everybody by surprise. Nobody expected double exponential growth in processing power to happen this soon.

Q: Given that quantum chips are getting so fast, can I buy my own quantum computer now?

A: Most of the people think the quantum computer is a solved problem. That we can just wait, and Google will sell you one that can do whatever you want. But no. Were in the [prototype] era. The number of qubits is doubling every six months, but the qubits are not perfect. They fail a lot and have imperfections and so forth. But Intel and Google and IBM arent going to wait for perfect qubits. The people who made the [first computers] didnt say, Were going to stop making bigger computers until we figure out how to make perfect vacuum tubes.

Q: Whats the big deal about doing problems with quantum mechanics instead of classical physics?

A: If you have 32 qubits, its like you have 232 parallel universes that are working on parts of your computation. Or like you have a parallel processor with 232 processors. But you only pay the electric bill in our universe.

Q: Quantum mechanics gets really difficult, really fast. How do you deal with that?

A: Everybody has their own interpretation of quantum mechanics. Mine is the Many Beers Interpretation of Quantum Mechanics. With no beer, quantum mechanics doesnt make any sense. After one, two or three beers, it makes perfect sense. But once you get to six or 10, it doesnt make any sense again. Im on my first bottle, so Im in the zone.

[This story originally appeared in print as "The Rules of the Road to Quantum Supremacy."]

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Quantum Computers Finally Beat Supercomputers in 2019 - Discover Magazine

Weather forecasting today is good. Can it get better? Sure, it can, if computers can be better. This is where quantum computers come into the picture. They possess computing capacity beyond anything that todays classical computers can ever achieve. This is because quantum computers can run calculations exponentially faster than todays conventional binary computers. That makes them powerful enough to bridge gaps which exist in todays weather forecasting, drug discovery, financial modelling and many other complex areas.

Classical computing has been the backbone of modern society. It gave us satellite TV, the internet and digital commerce. It put robots on Mars and smartphones in our pockets.

But many of the worlds biggest mysteries and potentially greatest opportunities remain beyond the grasp of classical computers, says Stefan Filipp, quantum scientist at IBM Research. To continue the pace of progress, we need to augment the classical approach with a new platform, one that follows its own set of rules. That is quantum computing.

Classical computing is based on the binary system, where the fundamental carriers of information bits can take on a value of either 0 or 1.

All information is stored and read as a sequence of 0s and 1s. A state of 0 is off (or false) and a state of 1 is on (or true). Unlike bits, quantum bits or qubits can have multiple values or states between 0 and 1, enabling them to store different types of information.

Superposition and entanglement are two fundamental properties of quantum objects. The ability to manipulate these properties is what makes quantum algorithms fundamentally different from classical algorithms.

Quantum computers working with classical systems have the potential to solve complex real-world problems such as simulating chemistry, modelling financial risk and optimising supply chains.

For example, Exxon Mobil plans to use quantum computing to better understand catalytic and molecular interactions that are too difficult to calculate with classical computers. Potential applications include more predictive environmental models and highly accurate quantum chemistry calculations to enable the discovery of new materials for more efficient carbon capture.

JP Morgan Chase is focusing on use cases for quantum computing in the financial industry, including trading strategies, portfolio optimisation, asset pricing and risk analysis.

In India, the government has launched two initiatives in the emerging field a networked programme on Quantum Information Science and Technology (QuST) and the National Mission on Quantum Technologies & Applications (NMQTA).

Despite all the progress, practical and working quantum systems might take most of the 2020s. And you wont see or need a quantum machine on your desk. These will be used by governments and large enterprises, unless you want to find aliens or figure out and execute ways to boil the ocean while sitting at home.

This story is part of the 'Tech that can change your life in the next decade' package

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Quantum computing : Solving problems beyond the power of classical computing - Economic Times

However, even with that progression, there are still jobs that classical computers are not powerful enough to do. The answer looks set to come from quantum computing. In this post, we will look at what quantum computing is and how it could revolutionize a long-standing industry such as banking.

What is Quantum Computing?

Quantum computers are expected to be a new kind of technology that can solve complex problems well beyond the capabilities of traditional systems. If you take an everyday problem like climate change, the intricacies of solving it are incredibly complex. A standard computer does not have the power or ability to even get close to genuinely understanding everything that is going on. The main reason is the endless amounts of data that computers need to process to generate an accurate decision.

A quantum computer is often referred to as a supercomputer. It has highly advanced processing power that can take masses of variables into account, helping predict weather patterns and natural disasters in the case of climate change.

Brief Technical Summary

A typical computer stores information in what is known as bits. In quantum computing, these are known as qubits. Qubits have certain properties that mean a connected group of them can provide way more processing power than binary bits from classical computing. In short, where binary bits store 1s and 0s to handle a task, qubits can represent numerous possible combinations of these simultaneously.

Practical Example

An example of this could be if running a travel agency. Lets say three people need to move from one place to another, Jenny, Anna and Steve. For that purpose, there are two taxis and the problem you want to solve is who gets into which taxi. However, we know that Jenny and Anna are friends, Jenny and Steve are enemies and Anna and Steve are enemies.

The aim would be to maximize the number of friend pairs and minimize the enemy pairs sharing the same taxi. A classical computer would store each possible solution with bits one at a time before being able to calculate a potential solution. However, a quantum computer will use qubits to represent all the solutions at the same time. It will find the best solution in a few milliseconds as it piles everything into just 1 operation.

The difference here is a traditional computer performs more and more calculations every time the data scales up, whereas a quantum computer will only ever have to process one operation.

In the real-world, one industry that could heavily benefit from this technology and processing power is banking.

Quantum Computing in Banking

In an article from Banco Bilbao Vizcaya Argentaria (BBVA) from October 2019, it was suggested that this kind of quantum computing power might fundamentally change the face of banking in time.

Encryption of personal data is critical to banking, with RSA-2048 being used at the highest levels. For a classical computer to find the key to decrypt the algorithm would take 1,034 steps. To put that into context, a processor capable of a trillion operations per second would still take 317 billion years to resolve the problem. Realistically, that makes decryption impossible.

However, a quantum computer could solve the decryption in just 107 steps. If the computer were running at a million operations per second, this calculation would only take 10 seconds to complete. The potential of quantum computing in this context is quite amazing. That said, we are still a long way off having enough processing power to reach those heights, but experts are working on it.

Barclays

Researchers at Barclays Bank in collaboration with IBM have created a proof-of-concept quantum optimized application. The solution revolves around the transaction settlement process. A settlement works on a transaction-by-transaction basis where they are pushed into a queue and settled in batches. During a processing window, as many trades as possible from the queue are settled.

Trades are complex by nature according to Lee Braine, director of research and engineering at Barclays. Traders can tap into funds before the transaction has been cleared. They are settled if funding is available or if there is some sort of credit collateral facility.

In a quantum computing context, a small number of trades could, in theory, be done in your head. However, as you get up to 10 or 20 transactions, you might need to use a pen and paper. Any more than that and we start going into classical computing. However, as we get to hundreds of trades, the machines begin to experience limitations.

A bit like the travel agency example we gave earlier, a quantum computer could run masses of complex aspects of trading. Using a seven-bit qubit system, the team could identify certain features that were of sufficient complexity. The same calculations would need about 200 traditional computers.

JP Morgan

Using an IBM machine, researchers at JP Morgan have demonstrated that they could simulate the future value of a financial product. They are testing the use of quantum computers to speed up intensive pricing calculations which would take traditional machine hours to compute. As portfolios become larger, the algorithms have greater complexity and could get to a point where they are impossible to calculate.

The research by the team has shown that a commercial-grade quantum computer can run the same calculations in a matter of seconds.

Summary

According to Deltec Bank, the Bahamas Banks are successfully testing quantum computers to solve problems that were previously very resource-intensive or impossible to complete. Although the technology is still some years away from changing the way banks calculate financial models due to complex hardware requirements, it is important to start testing now.

IBM themselves have stated they are a while away from a perfect solution with big breakthroughs still required but the time will certainly come.

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The Impact of Quantum Computing on Banking will be gigantic says Deltec Bank, Bahamas - Quantaneo, the Quantum Computing Source

This was the year of quantum physics, which redefined the kilogram and the computer. It was also the year of teamwork. Hundreds of scientists across the globe worked together to do the seemingly impossible capture an image of a black hole. A global collaboration of scientists journeyed into the heart of the Arctic to measure how the climate is changing in this crucial spot. This was also the year we lost a portion of the Amazon rainforest to a fire fuelled by greed.

First image of a black hole

After more than a decade at work, the Event Horizon Telescope, a large telescope array consisting of a global network of radio telescopes, stunned the world by capturing the first direct image of a black hole, which is situated at the centre of the Messier 87 galaxy, 54 million light years away. The image shows a circular silhouette outlined by emission from hot gas swirling around it, lending credibility to Einsteins theory of general relativity near all black holes.

Evidence of black holes from which nothing, not even light, can escape has existed for aeons. And astronomers have long observed the effects of these mysterious phenomena on their surroundings. Because of the lack of light, it was believed that you could not snap an image of these caverns in space.

Polarstern breaks ice

The German icebreaker ship, RV Polarstern, is right now stuck in the midst of the frozen Arctic sea at the North Pole. Its on a mission known as the Multidisciplinary drifting Observatory for the Study of Arctic Climate (Mosaic) the largest climate-change research expedition to the central Arctic. This region, one of the most inaccessible places on our planet, is critical to Earths climate and its essential to study it thoroughly.

During the year-long expedition (September 2019 to September 2020) that has taken 20 years to organise, over 600 researchers will rotate on and off the ship, supported by many more in research institutes across the world. The data harvested should give us an accurate picture of ice or its absence near the North Pole and is expected to silence climate change sceptics forever.

Googles quantum claim

Google claims to have reached a long-sought breakthrough called quantum supremacy that allows computers to calculate at inconceivable speeds. While some scientists are cautious about the implications, major tech companies in the US and China are investing heavily in quantum computing. IBM, a Google competitor, described the term quantum supremacy as misleading and proposed another metric, quantum volume .

Denisovan discoveries

A jawbone of a 1,60,000-year-old Denisovan hominids who existed alongside Neanderthals and disappeared 50,000 years ago was recently discovered in the Tibetan Plateau. This is the first time a fossil of this species has been found outside the Denisova Cave in Siberia, confirming the theory that these relatives of modern humans once lived across much of central and eastern Asia. The find also suggests Denisovans may have evolved genetic adaptations to high altitudes, which Tibetans inherited thanks to interbreeding between Denisovans and modern humans.

Crispr in clinical trials

Crispr/Cas9, a gene editing technique akin to molecular scissors that can snip, repair or insert genes into DNA, went into a spate of clinical trials. The technique holds the promise of curing nearly 6,000 known genetic diseases. There is already clinical evidence that it has cured two patients in the US, one suffering from beta thalassaemia and the other from sickle cell disease.

Crash course on the moon

The race to land on the moon is back in vogue. While Chinas Change-4 lander touched down smoothly on the moons far side in January, probes sent by the Israeli agency, SpaceIL, and the Indian Space Research Organisation crash-landed. China plans to launch another lunar lander next year. The European Space Agency, Russia and Nasa hope to follow in its footsteps.

Kilogram, redefined

In the biggest overhaul of the International System of Units, four units kilogram, kelvin, ampere and mole were redefined in terms of constants of nature. The new definition anchors the value of the kilogram to the Planck constant, an unvarying and infinitesimal number at the heart of quantum physics. Previously, the kilogram was defined as the mass of a specific object (stored in a Paris vault) that represented the mass of one litre of pure water at its freezing point.

Amazon ablaze

The Amazon rainforest, the worlds largest carbon sink, was irreversibly damaged after settlers allegedly set fire to it, with tacit support from the Brazilian government. Data released by Brazils National Institute for Space Research shows that from January to July, fires consumed 4.6 million acres of the Brazilian part of the Amazon rainforest. The nations right-wing President, Jair Bolsonaro, wants to facilitate the interests of industries in the forest, uncaring of the worldwide environmental concern.

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Science stories that shaped 2019 - Telegraph India

OYSTER BAY, N.Y.--(BUSINESS WIRE)--The year 2020 will be an exciting one for the Artificial Intelligence (AI) chipset market. In 2020 alone, more than 1.4 million cloud AI chipsets and 330 million edge AI chipsets are forecasted to be shipped, generating a total revenue of US$9 billion, states global tech market advisory firm, ABI Research.

In its new whitepaper, 54 Technology Trends to Watch in 2020, ABI Researchs analysts have identified 35 trends that will shape the technology landscape and 19 others that, although attracting huge amounts of speculation and commentary, look less likely to move the needle over the next twelve months. After a tumultuous 2019 that was beset by many challenges, both integral to technology markets and derived from global market dynamics, 2020 looks set to be equally challenging, says Stuart Carlaw, Chief Research Officer at ABI Research.

What will happen in 2020:

More custom AI chipsets will be launched:Weve already seen the launch of new custom AI chipsets by both major vendors and new startups alike. From Cerebras Systems worlds largest chipset to Alibabas custom cloud AI inference chipset, the AI chipset industry has been hugely impacted by the desire to reduce energy consumption, achieve higher performance, and, in the case of China, minimize the influence of Western suppliers in their supply chain, says Lian Jye Su, AI & Machine Learning Principal Analyst at ABI Research. 2020 will be an exciting year for AI chipsets. Several stealth startups are likely to launch programmable chipsets for data centers, while the emergence of new AI applications in edge devices will give rise to more Application Specific Integrated Circuits (ASICs) dedicated for edge AI inference workloads.

Heterogeneous computing will emerge as the key to supporting future AI Networks:Existing Artificial Intelligence (AI) applications and networks are currently serviced by different processing architectures, either that be Field Programmable Gate Array (FPGA), Graphical Processing Units (GPUs), CPUs, Digital Signal Processors (DSPs), or hardware accelerators, each used to its strength depending on the use case addressed. However, the next generation and AI and Machine Learning (ML) frameworks will be multimodal by their nature and may require heterogeneous computing resources for their operations. The leading players, including Intel, NVIDIA, Xilinx, and Qualcomm will introduce new chipset types topped by hardware accelerators to address the new use cases, says Su. Vendors of these chips will move away from offering proprietary software stacks and will start to adopt open Software Development Kits (SDKs) and Application Programming Interface (API) approaches to their tools in order to simplify the technology complexity for their developers and help them focus on building efficient algorithms for the new AI and ML applications.

What wont happen in 2020:

Quantum computing:Despite claims from Google in achieving quantum supremacy, the tech industry is still far away from the democratization of quantum computing technology, Su says. Existing vendors, such as IBM and D-Wave, will continue to enhance its existing quantum computing systems, but the developer community remains small and the benefits brought by these systems will still be limited to selected industries, such as military, national laboratories, and aerospace agencies. Like other nascent processing technologies, such as photonic and neuromorphic chipset, quantum computing systems in their current form still require very stringent operating environment, a lot of maintenance, and custom adjustment, and are definitely not even remotely ready for large-scale commercial deployments, Su concludes.

For more trends that wont happen in 2020, and the 35 trends that will, download the 54 Technology Trends to Watch in 2020 whitepaper.

About ABI Research

ABI Research provides strategic guidance to visionaries, delivering actionable intelligence on the transformative technologies that are dramatically reshaping industries, economies, and workforces across the world. ABI Researchs global team of analysts publish groundbreaking studies often years ahead of other technology advisory firms, empowering our clients to stay ahead of their markets and their competitors.

For more information about ABI Researchs services, contact us at +1.516.624.2500 in the Americas, +44.203.326.0140 in Europe, +65.6592.0290 in Asia-Pacific or visit http://www.abiresearch.com.

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2020 Will be a Banner Year for AI Custom Chipsets and Heterogenous Computing; Quantum Computing Remains on the Far Horizon - Business Wire