Category Archives: Quantum Computer
Google’s Head of Quantum Computing Hardware Resigns – WIRED
In late October 2019, Google CEO Sundar Pichai likened the latest result from the companys quantum computing hardware lab in Santa Barbara, California, to the Wright brothers first flight.
One of the labs prototype processors had achieved quantum supremacyevocative jargon for the moment a conventional computer does something seemingly impossible by harnessing quantum mechanics. In a blog post, Pichai said the milestone affirmed his belief that quantum computers might one day tackle problems like climate change, and the CEO also name-checked John Martinis, who had established Googles quantum hardware group in 2014.
Heres what Pichai didnt mention: Soon after the team had first got its quantum supremacy experiment working a few months earlier, Martinis says, he had been reassigned from a leadership position to an advisory one. Martinis tells WIRED that the change led to disagreements with Hartmut Neven, the longtime leader of Googles quantum project.
Martinis resigned from Google early this month. Since my professional goal is for someone to build a quantum computer, I think my resignation is the best course of action for everyone, he adds.
A Google spokesman did not dispute this account, and says that the company is grateful for Martinis contributions and that Neven continues to head the companys quantum project. Parent company Alphabet has a second, smaller, quantum computing group at its X Labs research unit. Martinis retains his position as a professor at the UC Santa Barbara, which he held throughout his tenure at Google, and says he will continue to work on quantum computing.
Googles quantum computing project was founded by Neven, who pioneered Googles image search technology, in 2006, and initially focused on software. To start, the small group accessed quantum hardware from Canadian startup D-Wave Systems, including in collaboration with NASA.
Everything you ever wanted to know about qubits, superpositioning, and spooky action at a distance.
The project took on greater scale and ambition when Martinis joined in 2014 to establish Googles quantum hardware lab in Santa Barbara, bringing along several members of his university research group. His nearby lab at UC Santa Barbara had produced some of the most prominent work in the field over the past 20 years, helping to demonstrate the potential of using superconducting circuits to build qubits, the building blocks of quantum computers.
Qubits are analogous to the bits of a conventional computer, but in addition to representing 1s and 0s, they can use quantum mechanical effects to attain a third state, dubbed a superposition, something like a combination of both. Qubits in superposition can work through some very complex problems, such as modeling the interactions of atoms and molecules, much more efficiently than conventional computer hardware.
How useful that is depends on the number and reliability of qubits in your quantum computing processor. So far the best demonstrations have used only tens of qubits, a far cry from the hundreds or thousands of high quality qubits experts believe will be needed to do useful work in chemistry or other fields. Googles supremacy experiment used 53 qubits working together. They took minutes to crunch through a carefully chosen math problem the company calculated would take a supercomputer on the order of 10,000 years, but does not have a practical application.
Martinis leaves Google as the company and rivals that are working on quantum computing face crucial questions about the technologys path. Amazon, IBM, and Microsoft, as well as Google offer their prototype technology to companies such as Daimler and JP Morgan so they can run experiments. But those processors are not large enough to work on practical problems, and it is not clear how quickly they can be scaled up.
When WIRED visited Googles quantum hardware lab in Santa Barbara last fall, Martinis responded optimistically when asked if his hardware team could see a path to making the technology practical. I feel we know how to scale up to hundreds and maybe thousands of qubits, he said at the time. Google will now have to do it without him.
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Google's Head of Quantum Computing Hardware Resigns - WIRED
COVID-19: Quantum computing could someday find cures for coronaviruses and other diseases – TechRepublic
While supercomputers are critical to researchers today, even they can't provide the massive computing power needed to map out the molecular structures of viruses to find cures.
When it comes to finding a vaccine that can halt and eradicate the deadly COVID-19 virus, today's supercomputers can only do so much. While supercomputers can do amazing things, they are not complex enough to find answers to nature's deepest and most complicated secrets, such as quickly and carefully mapping out the molecular structures of viruses so they can be defeated with modern medicines and treatments.
But an answer awaits perhaps five to 10 years away in the form of quantum computers, which are exponentially more powerful than traditional classic computers, according to computer scientists and other researchers.
SEE:Coronavirus: Critical IT policies and tools every business needs(TechRepublic Premium)
Recently a public-private partnership was formed to create a COVID-19 High Performance Computing Consortium, which is working to harness the power of high-performance computing resources to massively increase the speed and capacity of coronavirus research. And though that work is today welcome in the fight against COVID-19, it won't unlock all the incredibly difficult secrets that are held closely by such viruses.
For most pharmaceutical companies, supercomputers are used regularly to help research, find, and identify new drug treatments, including the identification of virus structures so cures can be found.
Yet supercomputers used today in virus and other pharmaceutical research are still based on classical computing architectures that view all data as a series of binary bits with a value of zero or one. Those machines face the limitations of modern bit-based computer architectures and power that is available today but can't theoretically or physically handle all the tremendously detailed research that is still needed.
That's where the future promise of quantum computing is expected to one day provide the vast computational power that could allow researchers to truly map out molecular structures in real time to solve medical mysteries and help quickly identify new drugs and treatments, said Chirag Dekate, a supercomputing and high-performance computing analyst with Gartner.
"If you're trying to do a quantum realistic simulation of the molecules and interactions of a virus, that is where classical computing starts falling short," Dekate said. "In classical computing, what you are able to simulate is only a fraction of what you can do with quantum computing."
The problem, though, is that true quantum computing capabilities are probably at least five to 10 years away from actual use, Dekate said.
"When two molecules or compounds interact, in order to do a quantum computing simulation, you have to be able to simulate the electrostatic forces of the interaction at the atomic level between those things," Dekate said. "This is where the computational complexity increases exponentially," requiring the power of quantum computing over traditional classical computing architecture.
SEE:Coronavirus: What business pros need to know(TechRepublic)
Quantum computers are based on qubits rather than bits, which are far more complex and allow information to be stored in new ways, giving them added dimensions of computing power. But that intense power requires many more technical requirements to make it possible, and much work is still to be done to enable the technology.
Dr. Itamar Sivan, a physicist and the founder and CEO of Quantum Machines, a quantum computing technology company, said the promise of quantum computing will someday help during times of crisis, such as today's coronavirus pandemic. Such machines are expected to be able to solve incredibly complex scientific problems in minutes in the future, compared with many years by even the most powerful supercomputers of 2020.
"Quantum computing is not a new field--it is already decades old," Sivan said. "In academia it is being investigated, and in the last five years in industry as well. The interest in quantum computing stems from a promise of immense computational power that we will never be able to achieve with classical computation."
SEE:Quantum computing: When to expect the next major leap(TechRepublic)
For researchers, quantum machines will provide power that will transform medical research and a wide range of other fields, he said. "If you would want to have an exact simulation of a molecule such as penicillin, you would never be able to do it with any classical computer because it is too complex. But quantum computers with hundreds of logical qubits will be able to do this task."
Just how much more powerful is a quantum computer compared with a classical computer?
"In order to explain the information in a quantum computer with 300 qubits you would need a classical processor which is built from more bits than there are atoms are in the universe," Sivan said. "It's one of the toughest moonshots that we face as a society, but if we can do it it's going to change the whole world."
Sivan agreed that such machines are easily a decade away before they would be able to perform the quantum simulations that are needed for virus research breakthroughs.
SEE:Quantum computing: Myths v. Realities(TechRepublic)
"For some problems, it's not about just running an algorithm faster, it's about making the impossible possible," he said. "This is why in drug discovery today, the majority of the process is done with the molecules themselves in test tubes and culture dishes, because you can't simulate them and look at their reactions and behavior using classic computers."
The challenges of achieving usable quantum computing are huge, including the extremely delicate state of quantum data when it is used. In operation, quantum data is rapidly lost in experiments being done over the last few years, preventing stable use of the machines.
"There are immense challenges all over the stack to get to the Holy Grail of quantum computing," Sivan said. "Once we solve the problem of loss of information, we will be fine."
The coronavirus has infected almost 2 million people and killed 121,000 around the world so far. While many patients with COVID-19 have mild symptoms and don't require hospitalization, with the incredibly wide scale of the pandemic, even at a 5% hospitalization rate large numbers of patients have been requiring emergency care in hospitals and other medical facilities that are struggling to keep up.
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COVID-19: Quantum computing could someday find cures for coronaviruses and other diseases - TechRepublic
The future of quantum computing in the cloud – TechTarget
AWS, Microsoft and other IaaS providers have jumped on the quantum computing bandwagon as they try to get ahead of the curve on this emerging technology.
Developers use quantum computing to encode problems as qubits, which compute multiple combinations of variables at once rather than exploring each possibility discretely. In theory, this could allow researchers to quickly solve problems involving different combinations of variables, such as breaking encryption keys, testing the properties of different chemical compounds or simulating different business models. Researchers have begun to demonstrate real-world examples of how these early quantum computers could be put to use.
However, this technology is still being developed, so experts caution that it could take more than a decade for quantum computing to deliver practical value. In the meantime, there are a few cloud services, such as Amazon Bracket and Microsoft Quantum, that aim to get developers up to speed on writing quantum applications.
Quantum computing in the cloud has the potential to disrupt industries in a similar way as other emerging technologies, such as AI and machine learning. But quantum computing is still being established in university classrooms and career paths, said Bob Sutor, vice president of IBM Quantum Ecosystem Development. Similarly, major cloud providers are focusing primarily on education at this early stage.
"The cloud services today are aimed at preparing the industry for the soon-to-arrive day when quantum computers will begin being useful," said Itamar Sivan, co-founder and CEO of Quantum Machines, an orchestration platform for quantum computing.
There's still much to iron out regarding quantum computing and the cloud, but the two technologies appear to be a logical fit, for now.
Cloud-based quantum computing is more difficult to pull off than AI, so the ramp up will be slower and the learning curve steeper, said Martin Reynolds, distinguished vice president of research at Gartner. For starters, quantum computers require highly specialized room conditions that are dramatically different from how cloud providers build and operate their existing data centers.
Reynolds believes practical quantum computers are at least a decade away. The biggest drawback lies in aligning the quantum state of qubits in the computer with a given problem, especially since quantumcomputersstill haven't been proven to solve problems better than traditional computers.
Coders also must learn new math and logic skills to utilize quantum computing. This makes it hard for them since they can't apply traditional digital programming techniques. IT teams need to develop specialized skills to understand how to apply quantum computing in the cloud so they can fine tune the algorithms, as well as the hardware, to make this technology work.
Current limitations aside, the cloud is an ideal way to consume quantum computing, because quantum computing has low I/O but deep computation, Reynolds said. Because cloud vendors have the technological resources and a large pool of users, they will inevitably be some of the first quantum-as-a-service providers and will look for ways to provide the best software development and deployment stacks.
Quantum computing could even supplement general compute and AI services cloud providers currently offer, said Tony Uttley, president of Honeywell Quantum Solutions.In that scenario, the cloud would integrate with classical computing cloud resources in a co-processing environment.
The cloud plays two key roles in quantum computing today, according to Hyoun Park, CEO and principal analyst at Amalgam Insights. The first is to provide an application development and test environment for developers to simulate the use of quantum computers through standard computing resources.
The second is to offer access to the few quantum computers that are currently available, in the way mainframe leasing was common a generation ago. This improves the financial viability of quantum computing, since multiple users can increase machine utilization.
It takes significant computing power to simulate quantum algorithm behavior from a development and testing perspective. For the most part, cloud vendors want to provide an environment to develop quantum algorithms before loading these quantum applications onto dedicated hardware from other providers, which can be quite expensive.
However, classical simulations of quantum algorithms that use large numbers of qubits are not practical. "The issue is that the size of the classical computer needed will grow exponentially with the number of qubits in the machine," said Doug Finke, publisher of the Quantum Computing Report.So, a classical simulation of a 50-qubit quantum computer would require a classical computer with roughly 1 petabyte of memory. This requirement will double with every additional qubit.
Nobody knows which approach is best, or which materials are best. We're at the Edison light bulb filament stage. Martin ReynoldsDistinguished vice president of research at Gartner
But classical simulations for problems using a smaller number of qubits are useful both as a tool to teach quantum algorithms to students and also for quantum software engineers to test and debug algorithms with "toy models" for their problem, Finke said.Once they debug their software, they should be able to scale it up to solve larger problems on a real quantum computer.
In terms of putting quantum computing to use, organizations can currently use it to support last-mile optimization, encryption and other computationally challenging issues, Park said. This technology could also aid teams across logistics, cybersecurity, predictive equipment maintenance, weather predictions and more. Researchers can explore multiple combinations of variables in these kinds of problems simultaneously, whereas a traditional computer needs to compute each combination separately.
However, there are some drawbacks to quantum computing in the cloud. Developers should proceed cautiously when experimenting with applications that involve sensitive data, said Finke. To address this, many organizations prefer to install quantum hardware in their own facilities despite the operational hassles, Finke said.
Also, a machine may not be immediately available when a quantum developer wants to submit a job through quantum services on the public cloud. "The machines will have job queues and sometimes there may be several jobs ahead of you when you want to run your own job," Finke said. Some of the vendors have implemented a reservation capability so a user can book a quantum computer for a set time period to eliminate this problem.
IBM was first to market with its Quantum Experience offering, which launched in 2016 and now has over 15 quantum computers connected to the cloud. Over 210,000 registered users have executed more than 70 billion circuits through the IBM Cloud and published over 200 papers based on the system, according to IBM.
IBM also started the Qiskit open source quantum software development platform and has been building an open community around it. According to GitHub statistics, it is currently the leading quantum development environment.
In late 2019, AWS and Microsoft introduced quantum cloud services offered through partners.
Microsoft Quantum provides a quantum algorithm development environment, and from there users can transfer quantum algorithms to Honeywell, IonQ or Quantum Circuits Inc. hardware. Microsoft's Q# scripting offers a familiar Visual Studio experience for quantum problems, said Michael Morris, CEO of Topcoder, an on-demand digital talent platform.
Currently, this transfer involves the cloud providers installing a high-speed communication link from their data center to the quantum computer facilities, Finke said. This approach has many advantages from a logistics standpoint, because it makes things like maintenance, spare parts, calibration and physical infrastructure a lot easier.
Amazon Braket similarly provides a quantum development environment and, when generally available, will provide time-based pricing to access D-Wave, IonQ and Rigetti hardware. Amazon says it will add more hardware partners as well. Braket offers a variety of different hardware architecture options through a common high-level programming interface, so users can test out the machines from the various partners and determine which one would work best with their application, Finke said.
Google has done considerable core research on quantum computing in the cloud and is expected to launch a cloud computing service later this year. Google has been more focused on developing its in-house quantum computing capabilities and hardware rather than providing access to these tools to its cloud users, Park said. In the meantime, developers can test out quantum algorithms locally using Google's Circ programming environment for writing apps in Python.
In addition to the larger offerings from the major cloud providers, there are several alternative approaches to implementing quantum computers that are being provided through the cloud.
D-Wave is the furthest along, with a quantum annealer well-suited for many optimization problems. Other alternatives include QuTech, which is working on a cloud offering of its small quantum machine utilizing its spin qubits technology. Xanadu is another and is developing a quantum machine based on a photonic technology.
Researchers are pursuing a variety of approaches to quantum computing -- using electrons, ions or photons -- and it's not yet clear which approaches will pan out for practical applications first.
"Nobody knows which approach is best, or which materials are best. We're at the Edison light bulb filament stage, where Edison reportedly tested thousands of ways to make a carbon filament until he got to one that lasted 1,500 hours," Reynolds said. In the meantime, recent cloud offerings promise to enable developers to start experimenting with these different approaches to get a taste of what's to come.
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The future of quantum computing in the cloud - TechTarget
Quantum computer chips demonstrated at the highest temperatures ever – New Scientist News
By Leah Crane
Credit: Luca Petit for QuTech
Quantum computing is heating up. For the first time, quantum computer chips have been operated at a temperature above -272C, or 1 kelvin. That may still seem frigid, but it is just warm enough to potentially enable a huge leap in the capabilities.
Quantum computers are made of quantum bits, or qubits, which can be made in several different ways. One that is receiving attention from some of the fields big players consists of electrons on a silicon chip.
These systems only function at extremely low temperatures below 100 millikelvin, or -273.05C so the qubits have to be stored in powerful refrigerators. The electronics that power them wont run at such low temperatures, and also emit heat that could disrupt the qubits, so they are generally stored outside the refrigerators with each qubit is connected by a wire to its electronic controller.
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Eventually, for useful quantum computing, we will need to go to something like a million qubits, and this sort of brute force method, with one wire per qubit, wont work any more, says Menno Veldhorst at QuTech in the Netherlands. It works for two qubits, but not for a million.
Veldhorst and his colleagues, along with another team led by researchers at the University of New South Wales in Australia, have now demonstrated that these qubits can be operated at higher temperatures. The latter team showed they were able to control the state of two qubits on a chip at temperatures up to 1.5 kelvin, and Veldhorsts group used two qubits at 1.1 kelvin in what is called a logic gate, which performs the basic operations that make up more complex calculations.
Now that we know the qubits themselves can function at higher temperatures, the next step is incorporating the electronics onto the same chip. I hope that after we have that circuit, it wont be too hard to scale to something with practical applications, says Veldhorst.
Those quantum circuits will be similar in many ways to the circuits we use for traditional computers, so they can be scaled up relatively easily compared with other kinds of quantum computers, he says.
Journal references: Nature, DOI: 10.1038/s41586-020-2170-7 and DOI: 10.1038/s41586-020-2171-6
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Quantum computer chips demonstrated at the highest temperatures ever - New Scientist News
Alex Garland on ‘Devs,’ free will and quantum computing – Engadget
Garland views Amaya as a typical Silicon Valley success story. In the world of Devs, it's the first company that manages to mass produce quantum computers, allowing them to corner that market. (Think of what happened to search engines after Google debuted.) Quantum computing has been positioned as a potentially revolutionary technology for things like healthcare and encryption, since it can tackle complex scenarios and data sets more effectively than traditional binary computers. Instead of just processing inputs one at a time, a quantum machine would theoretically be able to tackle an input in multiple states, or superpositions, at once.
By mastering this technology, Amaya unlocks a completely new view of reality: The world is a system that can be decoded and predicted. It proves to them that the world is deterministic. Our choices don't matter; we're all just moving along predetermined paths until the end of time. Garland is quick to point out that you don't need anything high-tech to start asking questions about determinism. Indeed, it's something that's been explored since Plato's allegory of the cave.
"What I did think, though, was that if a quantum computer was as good at modeling quantum reality as it might be, then it would be able to prove in a definitive way whether we lived in a deterministic state," Garland said. "[Proving that] would completely change the way we look at ourselves, the way we look at society, the way society functions, the way relationships unfold and develop. And it would change the world in some ways, but then it would restructure itself quickly."
The sheer difficulty of coming up with something -- anything -- that's truly spontaneous and isn't causally related to something else in the universe is the strongest argument in favor of determinism. And it's something Garland aligns with personally -- though that doesn't change how he perceives the world.
"Whether or not you or I have free will, both of us could identify lots of things that we care about," he said. "There are lots of things that we enjoy or don't enjoy. Or things that we're scared of, or we anticipate. And all of that remains. It's not remotely affected by whether we've got free will or not. What might be affected is, I think, our capacity to be forgiving in some respects. And so, certain kinds of anti-social or criminal behavior, you would start to think about in terms of rehabilitation, rather than punishment. Because then, in a way, there's no point punishing someone for something they didn't decide to do."
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Alex Garland on 'Devs,' free will and quantum computing - Engadget
RAND report finds that, like fusion power and Half Life 3, quantum computing is still 15 years away – The Register
Quantum computers pose an "urgent but manageable" threat to the security of modern communications systems, according to a report published Thursday by influential US RAND Corporation.
The non-profit think tank's report, "Securing Communications in the Quantum Computing Age: Managing the Risks to Encryption," urges the US government to act quickly because quantum code-breaking could be a thing in, say, 12-15 years.
If adequate implementation of new security measures has not taken place by the time capable quantum computers are developed, it may become impossible to ensure secure authentication and communication privacy without major, disruptive changes, said Michael Vermeer, a RAND scientist and lead author of the report in a statement.
Experts in the field of quantum computing like University of Texas at Austin computer scientist Scott Aaronson have proposed an even hazier timeline.
Noting that the quantum computers built by Google and IBM have been in the neighborhood of 50 to 100 quantum bits (qubits) and that running Shor's algorithm to break public key RSA cryptosystems would probably take several thousand logical qubits meaning millions of physical qubits due to error correction Aaronson recently opined, "I dont think anyone is close to that, and we have no idea how long it will take."
But other boffins, like University of Chicago computer science professor Diana Franklin, have suggested Shor's algorithm might be a possibility in a decade and a half.
So even though quantum computing poses a theoretical threat to most current public-key cryptography and less risk for lattice-based, symmetric, privacy key, post-quantum, and quantum cryptography there's not much consensus about how and when this threat might manifest itself.
Nonetheless, the National Institute of Standards and Technology, the US government agency overseeing tech standards, has been pushing the development of quantum-resistant cryptography since at least 2016. Last year it winnowed a list of proposed post-quantum crypto (PQC) algorithms down to a field of 26 contenders.
The RAND report anticipates quantum computers capable of crypto-cracking will be functional by 2033, with the caveat that experts propose dates both before and after that. PQC algorithm standards should gel within the next five years, with adoption not expected until the mid-to-late 2030s, or later.
But the amount of time required for the US and the rest of the world to fully implement those protocols to mitigate the risk of quantum crypto cracking may take longer still. Note that the US government is still running COBOL applications on ancient mainframes.
"If adequate implementation of PQC has not taken place by the time capable quantum computers are developed, it may become impossible to ensure secure authentication and communication privacy without major, disruptive changes to our infrastructure," the report says.
RAND's report further notes that consumer lack of awareness and indifference to the issue means there will be no civic demand for change.
Hence, the report urges federal leadership to protect consumers, perhaps unaware that Congress is considering the EARN-IT Act, which critics characterize as an "all-out assault on encryption."
"If we act in time with appropriate policies, risk reduction measures, and a collective urgency to prepare for the threat, then we have an opportunity for a future communications infrastructure that is as safe as or more safe than the current status quo, despite overlapping cyber threats from conventional and quantum computers," the report concludes.
It's worth recalling that a 2017 National Academy of Sciences, Engineering, and Medicine report, "Global Health and the Future Role of the United States," urged the US to maintain its focus on global health security and to prepare for infection disease threats.
That was the same year nonprofit PATH issued a pandemic prevention report urging the US government to "maintain its leadership position backed up by the necessary resources to ensure continued vigilance against emerging pandemic threats, both at home and abroad."
The federal government's reaction to COVID-19 is a testament to the impact of reports from external organizations. We can only hope that the threat of crypto-cracking quantum computers elicits a response that's at least as vigorous.
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RAND report finds that, like fusion power and Half Life 3, quantum computing is still 15 years away - The Register
Quantum computing: When to expect the next major leap – TechRepublic
What's up next for quantum computing? Possibly weather forecasting and online dating.
Dan Patterson, a Senior Producer for CBS News and CNET, interviewed futurist Isaac Arthur about what's next for quantum computing. The following is an edited transcript of the interview.
Isaac Arthur: It's always hard to guess with computers, and we're a little bit spoiled by Moore's Law from the fifties and sixties just taking us from these really simple devices to what we have nowadays.
We do not want to make the same mistake we made with, for instance, nuclear fission and fusion where we got the development in 20 years and just assume the next one will get to us in another 20.
Quantum computing might be many decades before we see any real major progress, but at the moment, we have made quite a few major leaps and actually are doing some real calculations with this.
SEE:Managing AI and ML in the enterprise 2020 (free PDF) (TechRepublic)
We have a whole bunch of problems in terms of making it better, though. The biggest one is actually getting the right answer out of it. As an example, if we were using the random source before--let's say I locked somebody inside a quantum box with a phone book, and I told them, 'I want you to find a phone number, and if you call this correct phone number and here's the phone number in this book, someone's going to come by and let you out of this box.'
That person is then given a random number generator, and we shut the box, and they search. A whole bunch of different quantum ghosts of them appear, searching various pages, but the one who finds the right one calls that, and the person comes and opens the door. That's one example of a data extraction, though that would never work in actual reality because quantum doesn't do both on the macroscopic scale, but you can get errors from things like that.
First, imagine one of these quantum people searching that page didn't call the right number, but instead accidentally called a pizza delivery place that showed up and opened the door to deliver a pizza. Now, we have a wrong answer. We have things like this happen with quantum computing where we have an error, in terms of the data. We used to have this with normal computing too, but we solved it fairly early on.
This is probably going to be a lot harder to do, and in many ways, it's the hardest part other than actually keeping all of these protocols entangled. It's not just trying to keep one particle like this. We have to keep several thousand potentially--or millions--all entangled with each other simultaneously. This also allows them to be at just a hair above absolute zero temperature-wise. And then, of course, we have our third problem that has to be overcome, which is the software.
SEE: Augmented reality for business: Cheat sheet (free PDF) (TechRepublic)
All this runs on algorithms being had on class computers fed into these things, and those algorithms are the only way that we still have to do a lot of work on to improve them because we're not quite using the original pure algorithms like Shor's [algorithm], but ones we've had to adapt along the way. Those are kind of three areas--the software and the hardware areas are the ones that are going to really control limitations on advancing.
How much bigger can we make the entangled system? How well can we actually pull the right answer, and how do we actually get the right algorithms to ask the right question, as well?
What we tend to think--you know, with the modern phone and the laptop--that this would be something you have at your home, that you'd have a quantum computer, but in fact you probably never actually have a quantum computer in someone's house. They have to be run at such very low temperatures. Even though they are very small devices in terms of the entanglement, there's so much associated equipment that isn't likely to get too miniaturized. Most likely, you would always have class computers, and people access it through the cloud, and you'd just buy time--or get time--on a quantum computer that you will link up to.
The thing that we're most likely--for one individual person to use, would probably be something like encryption, but for stuff that we would actually get to see on our computer would probably be stuff like weather forecasting, for instance. It has a lot of options to allow us to do way better weather forecasting than we do now.
There are a lot of other examples in terms of the science; there are great things. It might finally let us model how the lifestyle of abiogenesis in the deep oceans, which is one of those examples where our models can't really be. We have approximation algorithms that we use to cover these really huge numbers, but they don't really seem to be up to snuff for covering things like those chemical interactions in the early deep oceans, and then those same algorithms, ironically enough, would be the kind of things we'd use for dating services in terms of finding the most optimal match for a person based on not just a simplified number of traits.
We have to simplify traits, normally. Here, we could actually have a thousand different traits with a thousand different subtypes, and a quantum computer could actually match up and optimize all of those. And then of course, there's the possibility of using election modeling.
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Cambridge Quantum Computing Performs the World’s First Quantum Natural Language Processing Experiment – Quantaneo, the Quantum Computing Source
This is the first time that natural language processing has been executed on a quantum computer. Furthermore, by achieving the results without relying on quantum RAM, CQC scientists have created a path to truly applicable quantum advantage within the Noisy Intermediate-Scale Quantum (NISQ) era.
By using CQCs class-leading and platform-agnostic retargetable compiler t|ketTM, these programs were successfully executed on an IBM quantum computer, achieving meaning-aware and grammatically informed natural language processing - a dream of computer scientists since the earliest days of the computer age. CQC looks forward to providing further details in the near future including ways to scale the programs so that meaningfully large numbers of sentences can be used on NISQ machines as they themselves scale in quantum volume and using other types of quantum computers.
The full article with details and links to the appropriate GitHub repository is noted here.
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Cambridge Quantum Computing Performs the World's First Quantum Natural Language Processing Experiment - Quantaneo, the Quantum Computing Source
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
Research by University of Chicago PhD Student and EPiQC Wins IBM Q Best Paper – HPCwire
March 23, 2020 A new approach for using a quantum computer to realize a near-term killer app for the technology received first prize in the 2019 IBM Q Best Paper Awardcompetition, the company announced. The paper, Minimizing State Preparations in Variational Quantum Eigensolver (VQE) by Partitioning into Commuting Families, was authored by UChicago CS graduate studentPranav Gokhaleand fellow researchers from theEnabling Practical-Scale Quantum Computing (EPiQC)team.
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 inthis 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 theirPython and Qiskit codefor 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 theIBM Research Blog. Additional authors on the paper include ProfessorFred Chongand PhD studentYongshan Dingof 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.
About The University of Chicago
The University of Chicago is an urban research university that has driven new ways of thinking since 1890. Our commitment tofree and open inquirydraws inspired scholars to ourglobal campuses, where ideas are born that challenge and change the world. We empower individuals to challenge conventional thinking in pursuit of original ideas. Students in theCollegedevelop critical, analytic, and writing skills in ourrigorous, interdisciplinary core curriculum. Throughgraduate programs, students test their ideas with UChicago scholars, and become the next generation of leaders in academia, industry, nonprofits, and government.
Source: The University of Chicago
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Research by University of Chicago PhD Student and EPiQC Wins IBM Q Best Paper - HPCwire