Archive for the ‘Quantum Computing’ Category

Research by University of Chicago PhD Student and EPiQC Wins IBM Q Best Paper – Quantaneo, the Quantum Computing Source

The interdisciplinary team of researchers from UChicago, University of California, Berkeley, Princeton University and Argonne National Laboratory won the $2,500 first-place award for Best Paper. Their research examined how the VQE quantum algorithm could improve the ability of current and near-term quantum computers to solve highly complex problems, such as finding the ground state energy of a molecule, an important and computationally difficult chemical calculation the authors refer to as a killer app for quantum computing.

Quantum computers are expected to perform complex calculations in chemistry, cryptography and other fields that are prohibitively slow or even impossible for classical computers. A significant gap remains, however, between the capabilities of todays quantum computers and the algorithms proposed by computational theorists.

VQE can perform some pretty complicated chemical simulations in just 1,000 or even 10,000 operations, which is good, Gokhale says. The downside is that VQE requires millions, even tens of millions, of measurements, which is what our research seeks to correct by exploring the possibility of doing multiple measurements simultaneously.

Gokhale explains the research in this video.

With their approach, the authors reduced the computational cost of running the VQE algorithm by 7-12 times. When they validated the approach on one of IBMs cloud-service 20-qubit quantum computers, they also found lower error as compared to traditional methods of solving the problem. The authors have shared their Python and Qiskit code for generating circuits for simultaneous measurement, and have already received numerous citations in the months since the paper was published.

For more on the research and the IBM Q Best Paper Award, see the IBM Research Blog. Additional authors on the paper include Professor Fred Chong and PhD student Yongshan Ding of UChicago CS, Kaiwen Gui and Martin Suchara of the Pritzker School of Molecular Engineering at UChicago, Olivia Angiuli of University of California, Berkeley, and Teague Tomesh and Margaret Martonosi of Princeton University.

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Research by University of Chicago PhD Student and EPiQC Wins IBM Q Best Paper - Quantaneo, the Quantum Computing Source

Global Quantum Computing for Enterprise Market 2020 Report With Segmentation, Analysis On Trends, Growth, Opportunities and Forecast Till 2024 – News…

The Global Quantum Computing for Enterprise Market study report presents an in-depth study about the market on the basis of key segments such as product type, application, key companies and key regions, end users and others. The research report presents assessment of the growth and other characteristics of the Global Quantum Computing for Enterprise Market on the basis of key geographical regions and countries. The major regions which have good market in this industry are North America, Latin America, Europe, Asia-Pacific and Middle East Africa.

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Global Market By Type:

HardwareSoftware

Global Market By Application:

BFSITelecommunications and ITRetail and E-CommerceGovernment and DefenseHealthcareManufacturingEnergy and UtilitiesConstruction and EngineeringOthers

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Global Quantum Computing for Enterprise Market 2020 Report With Segmentation, Analysis On Trends, Growth, Opportunities and Forecast Till 2024 - News...

The future’s bright for quantum computing but it will need big backing – The Union Journal

IT stakeholders throughout markets are delighted by the potential customers of quantum computing, but it will take a whole lot a lot more source to make sure both the technologys all set for a large swimming pool of customers, and also those very same customers prepare to release it.

Thats according to a brand-new study by the International Data Corporation (IDC) qualified Quantum Computing Adoption Trends: 2020 Survey Findings, which has actually assembled information and also end-user metrics from over 2,700 European entities associated with the quantum ball, and also the people managing quantum financial investments.

Despite the slower price of quantum fostering total( financial investments consist of in between 0 2 percent of yearly budget plans), end-users are confident that quantum computing will placed them at an affordable benefit, supplied that very early seed financial investment gets on hand.

The favorable overview adheres to the growth of brand-new models and also very early progression in markets such as FinTech, cybersecurity and also production.

Made up of those that would certainly look after financial investment in quantum in their organisations, participants pointed out far better company knowledge information event, enhanced expert system (AI) capacities, in addition to increased effectiveness and also efficiency of their cloud-based systems and also solutions, as one of the most amazing applications.

While the innovation itself still has a lengthy means to precede its practical for organisations, also when it is, IT directors stress over high prices refuting them accessibility, restricted expertise of the area, scarcity of essential sources in addition to the high degree of details entailed within the innovation itself.

However, with such large applications and also possibility of the technology, quantum area makers and also vendors are established on making the innovation readily available for as wide a swathe of customers as feasible that implies production it easy to use, and also readily available to business with even more restricted source, as cloud-based Quantum-Computing- as-a-Service (QCaaS).

According to Heather Wells, the IDCs elderly study expert of Infrastructure Systems, Platforms, and also Technology, Quantum computing is the future market and also facilities disruptor for companies wanting to make use of big quantities of information, expert system, and also artificial intelligence to speed up real-time company knowledge and also introduce item growth.

Many organizations from many industries are already experimenting with its potential.

These understandings more mention one of the most prominent applications and also methods of quantum innovation, that include cloud-centric quantum computing, quantum networks, facility quantum formulas, and also crossbreed quantum computing which takes in 2 or even more adaptions of quantum technological opportunities.

The future appears significantly encouraging for quantum computing mass fostering, nonetheless, those business creating should act rapidly to make its very early power easily accessible to organisations in order to protect the financial investment to drive the innovations real future possibility.

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The future's bright for quantum computing but it will need big backing - The Union Journal

The growth of an organism rides on a pattern of waves – MIT News

When an egg cell of almost any sexually reproducing species is fertilized, it sets off a series of waves that ripple across the eggs surface. These waves are produced by billions of activated proteins that surge through the eggs membrane like streams of tiny burrowing sentinels, signaling the egg to start dividing, folding, and dividing again, to form the first cellular seeds of an organism.

Now MIT scientists have taken a detailed look at the pattern of these waves, produced on the surface of starfish eggs. These eggs are large and therefore easy to observe, and scientists consider starfish eggs to be representative of the eggs of many other animal species.

In each egg, the team introduced a protein to mimic the onset of fertilization, and recorded the pattern of waves that rippled across their surfaces in response. They observed that each wave emerged in a spiral pattern, and that multiple spirals whirled across an eggs surface at a time. Some spirals spontaneously appeared and swirled away in opposite directions, while others collided head-on and immediately disappeared.

The behavior of these swirling waves, the researchers realized, is similar to the waves generated in other, seemingly unrelated systems, such as the vortices in quantum fluids, the circulations in the atmosphere and oceans, and the electrical signals that propagate through the heart and brain.

Not much was known about the dynamics of these surface waves in eggs, and after we started analyzing and modeling these waves, we found these same patterns show up in all these other systems, says physicist Nikta Fakhri, the Thomas D. and Virginia W. Cabot Assistant Professor at MIT. Its a manifestation of this very universal wave pattern.

It opens a completely new perspective, adds Jrn Dunkel, associate professor of mathematics at MIT. You can borrow a lot of techniques people have developed to study similar patterns in other systems, to learn something about biology.

Fakhri and Dunkel have published their results today in the journal Nature Physics. Their co-authors are Tzer Han Tan, Jinghui Liu, Pearson Miller, and Melis Tekant of MIT.

Finding ones center

Previous studies have shown that the fertilization of an egg immediately activates Rho-GTP, a protein within the egg which normally floats around in the cells cytoplasm in an inactive state. Once activated, billions of the protein rise up out of the cytoplasms morass to attach to the eggs membrane, snaking along the wall in waves.

Imagine if you have a very dirty aquarium, and once a fish swims close to the glass, you can see it, Dunkel explains. In a similar way, the proteins are somewhere inside the cell, and when they become activated, they attach to the membrane, and you start to see them move.

Fakhri says the waves of proteins moving across the eggs membrane serve, in part, to organize cell division around the cells core.

The egg is a huge cell, and these proteins have to work together to find its center, so that the cell knows where to divide and fold, many times over, to form an organism, Fakhri says. Without these proteins making waves, there would be no cell division.

MIT researchers observe ripples across a newly fertilized egg that are similar to other systems, from ocean and atmospheric circulations to quantum fluids. Courtesy of the researchers.

In their study, the team focused on the active form of Rho-GTP and the pattern of waves produced on an eggs surface when they altered the proteins concentration.

For their experiments, they obtained about 10 eggs from the ovaries of starfish through a minimally invasive surgical procedure. Theyintroduced a hormone to stimulate maturation, and alsoinjected fluorescent markers to attach to any active forms of Rho-GTP thatrose up in response. They then observed each egg through a confocal microscope and watched as billions of the proteins activated and rippled across the eggs surface in response to varying concentrations of the artificial hormonal protein.

In this way, we created a kaleidoscope of different patterns and looked at their resulting dynamics, Fakhri says.

Hurricane track

The researchers first assembled black-and-white videos of each egg, showing the bright waves that traveled over its surface. The brighter a region in a wave, the higher the concentration of Rho-GTP in that particular region. For each video, they compared the brightness, or concentration of protein from pixel to pixel, and used these comparisons to generate an animation of the same wave patterns.

From their videos, the team observed that waves seemed to oscillate outward as tiny, hurricane-like spirals. The researchers traced the origin of each wave to the core of each spiral, which they refer to as a topological defect. Out of curiosity, they tracked the movement of these defects themselves. They did some statistical analysis to determine how fast certain defects moved across an eggs surface, and how often, and in what configurations the spirals popped up, collided, and disappeared.

In a surprising twist, they found that their statistical results, and the behavior of waves in an eggs surface, were the same as the behavior of waves in other larger and seemingly unrelated systems.

When you look at the statistics of these defects, its essentially the same as vortices in a fluid, or waves in the brain, or systems on a larger scale, Dunkel says. Its the same universal phenomenon, just scaled down to the level of a cell.

The researchers are particularly interested in the waves similarity to ideas in quantum computing. Just as the pattern of waves in an egg convey specific signals, in this case of cell division, quantum computing is a field that aims to manipulate atoms in a fluid, in precise patterns, in order to translate information and perform calculations.

Perhaps now we can borrow ideas from quantum fluids, to build minicomputers from biological cells, Fakhri says. We expect some differences, but we will try to explore [biological signaling waves] further as a tool for computation.

This research was supported, in part, by the James S. McDonnell Foundation, the Alfred P. Sloan Foundation, and the National Science Foundation.

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The growth of an organism rides on a pattern of waves - MIT News

The Well-matched Combo of Quantum Computing and Machine Learning – Analytics Insight

The pace of improvement in quantum computing mirrors the fast advances made in AI and machine learning. It is normal to ask whether quantum technologies could boost learning algorithms: this field of inquiry is called quantum-improved machine learning.

Quantum computers are gadgets that work dependent on principles from quantum physics. The computers that we at present use are constructed utilizing transistors and the information is stored as double 0 and 1. Quantum computers are manufactured utilizing subatomic particles called quantum bits, qubits for short, which can be in numerous states simultaneously. The principal advantage of quantum computers is that they can perform exceptionally complex tasks at supersonic velocities. In this way, they take care of issues that are not presently feasible.

The most significant advantage of quantum computers is the speed at which it can take care of complex issues. While theyre lightning speedy at what they do, they dont give abilities to take care of issues from undecidable or NP-Hard problem classes. There is a problem set that quantum computing will have the option to explain, anyway, its not applicable for all computing problems.

Ordinarily, the issue set that quantum computers are acceptable at solving includes number or data crunching with an immense amount of inputs, for example, complex optimisation problems and communication systems analysis problemscalculations that would normally take supercomputers days, years, even billions of years to brute force.

The application that is routinely mentioned as an instance that quantum computers will have the option to immediately solve is solid RSA encryption. A recent report by the Microsoft Quantum Team recommends this could well be the situation, figuring that itd be feasible with around a 2330 qubit quantum computer.

Streamlining applications leading the pack makes sense well since theyre at present to a great extent illuminated utilizing brute force and raw computing power. If quantum computers can rapidly observe all the potential solutions, an ideal solution can become obvious all the more rapidly. Streamlining stands apart on the grounds that its significantly more natural and simpler to get a hold on.

The community of people who can fuse optimization and robust optimization is a whole lot bigger. The machine learning community, the coinciding between the innovation and the requirements are technical; theyre just pertinent to analysts. Whats more, theres a much smaller network of statisticians on the planet than there are of developers.

Specifically, the unpredictability of fusing quantum computing into the machine learning workflow presents an impediment. For machine learning professionals and analysts, its very easy to make sense of how to program the system. Fitting that into a machine learning workflow is all the more challenging since machine learning programs are getting very complex. However, teams in the past have published a lot of research on the most proficient method to consolidate it in a training workflow that makes sense.

Undoubtedly, ML experts at present need another person to deal with the quantum computing part: Machine learning experts are searching for another person to do the legwork of building the systems up to the expansions and demonstrating that it can fit.

In any case, the intersection of these two fields goes much further than that, and its not simply AI applications that can benefit. There is a meeting area where quantum computers perform machine learning algorithms and customary machine learning strategies are utilized to survey the quantum computers. This region of research is creating at such bursting speeds that it has produced a whole new field called Quantum Machine Learning.

This interdisciplinary field is incredibly new, however. Recent work has created quantum algorithms that could go about as the building blocks of machine learning programs, yet the hardware and programming difficulties are as yet significant and the development of fully functional quantum computers is still far off.

The future of AI sped along by quantum computing looks splendid, with real-time human-imitable practices right around an inescapable result. Quantum computing will be capable of taking care of complex AI issues and acquiring multiple solutions for complex issues all the while. This will bring about artificial intelligence all the more effectively performing complex tasks in human-like ways. Likewise, robots that can settle on optimised decisions in real-time in practical circumstances will be conceivable once we can utilize quantum computers dependent on Artificial Intelligence.

How away will this future be? Indeed, considering just a bunch of the worlds top organizations and colleges as of now are growing (genuinely immense) quantum computers that right now do not have the processing power required, having a multitude of robots mirroring humans running about is presumably a reasonable way off, which may comfort a few people, and disappoint others. Building only one, however? Perhaps not so far away.

Quantum computing and machine learning are incredibly well matched. The features the innovation has and the requirements of the field are extremely close. For machine learning, its important for what you have to do. Its difficult to reproduce that with a traditional computer and you get it locally from the quantum computer. So those features cant be unintentional. Its simply that it will require some time for the people to locate the correct techniques for integrating it and afterwards for the innovation to embed into that space productively.

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The Well-matched Combo of Quantum Computing and Machine Learning - Analytics Insight