Category: Quantum Computing


An international team of scientists has come up with a blueprint for a large-scale quantum computer

By Hugo Angel,

‘It is the Holy Grail of science … we will be able to do certain things we could never even dream of before’
Courtesy Professor Winfried Hensinger
Quantum computing breakthrough could help ‘change life completely‘, say scientists
Scientists claim to have produced the first-ever blueprint for a large-scale quantum computer in a development that could bring about a technological revolution on a par with the invention of computing itself.
Until now quantum computers have had just a fraction of the processing power they are theoretically capable of producing.
But an international team of researchers believe they have finally overcome the main technical problems that have prevented the construction of more powerful machines.
They are currently building a prototype and a full-scale quantum computer – many millions of times faster than the best currently available – could be built in about a decade.
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Scientists invent invisible underwater robots based on glass eels
Such devices work by utilising the almost magical properties found in the world of the very small, where an atom can apparently exist in two different places at the same time.
Professor Winfried Hensinger, head of the Ion Quantum Technology Group at Sussex University, who has been leading this research, told The Independent: “It is the Holy Grail of science, really, to build a quantum computer.
And we are now publishing the actual nuts-and-bolts construction plan for a large-scale quantum computer.
It is thought the astonishing processing power unleashed by quantum mechanics will lead to new, life-saving medicines, help solve the most intractable scientific problems, and probe the mysteries of the universe.
Life will change completely. We will be able to do certain things we could never even dream of before,” Professor Hensinger said.
You can imagine that suddenly the sky is the limit.
This is really, really exciting … it’s probably one of the most exciting times to be in this field.
He said small quantum computers had been built in the past but to test the theories.
This is not an academic study any more, it really is all the engineering required to build such a device,” he said.
Nobody has really gone ahead and drafted a full engineering plan of how you build one.
Many people questioned, because this is so hard to make this happen, that it can even be built.
We show that not only can it be built, but we provide a whole detailed plan on how to make it happen.
The problem is that existing quantum computers require lasers focused precisely on individual atoms. The larger the computer, the more lasers are required and the greater the chance of something going wrong.
But Professor Hensinger and colleagues used a different technique to monitor the atoms involving a microwave field and electricity in an ‘ion-trap’ device.

What we have is a solution that we can scale to arbitrary [computing] power,” he said.

Fig. 2. Gradient wires placed underneath each gate zone and embedded silicon photodetector.
(A) Illustration showing an isometric view of the two main gradient wires placed underneath each gate zone. Short wires are placed locally underneath each gate zone to form coils, which compensate for slowly varying magnetic fields and allow for individual addressing. The wire configuration in each zone can be seen in more detail in the inset.
(B) Silicon photodetector (marked green) embedded in the silicon substrate, transparent center segmented electrodes, and the possible detection angle are shown. VIA structures are used to prevent optical cross-talk from neighboring readout zones.
Source: Science Journals — AAAS. Blueprint for a microwave trapped ion quantum computer. Lekitsch et al. Sci. Adv. 2017;3: e1601540 1 February 2017
Fig. 4. Scalable module illustration. One module consisting of 36 × 36 junctions placed on the supporting steel frame structure: Nine wafers containing the required DACs and control electronics are placed between the wafer holding 36 × 36 junctions and the microchannel cooler (red layer) providing the cooling. X-Y-Z piezo actuators are placed in the four corners on top of the steel frame, allowing for accurate alignment of the module. Flexible electric wires supply voltages, currents, and control signals to the DACs and control electronics, such as field-programmable gate arrays (FPGAs). Coolant is supplied to the microchannel cooler layer via two flexible steel tubes placed in the center of the modules.
Source: Science Journals — AAAS. Blueprint for a microwave trapped ion quantum computer. Lekitsch et al. Sci. Adv. 2017;3: e1601540 1 February 2017
Fig. 5. Illustration of vacuum chambers. Schematic of octagonal UHV chambers connected together; each chamber is 4.5 × 4.5 m2 large and can hold >2.2 million individual X-junctions placed on steel frames.
Source: Science Journals — AAAS. Blueprint for a microwave trapped ion quantum computer. Lekitsch et al. Sci. Adv. 2017;3: e1601540 1 February 2017

We are already building it now. Within two years we think we will have completed a prototype which incorporates all the technology we state in this blueprint.

At the same time we are now looking for industry partner so we can really build a large-scale device that fills a building basically.
It’s extraordinarily expensive so we need industry partners … this will be in the 10s of millions, up to £100m.
Commenting on the research, described in a paper in the journal Science Advances, other academics praised the quality of the work but expressed caution about how quickly it could be developed.
Dr Toby Cubitt, a Royal Society research fellow in quantum information theory at University College London, said: “Many different technologies are competing to build the first large-scale quantum computer. Ion traps were one of the earliest realistic proposals. 
This work is an important step towards scaling up ion-trap quantum computing.
Though there’s still a long way to go before you’ll be making spreadsheets on your quantum computer.
And Professor Alan Woodward, of Surrey University, hailed the “tremendous step in the right direction”.
It is great work,” he said. “They have made some significant strides forward.

But he added it was “too soon to say” whether it would lead to the hoped-for technological revolution.

ORIGINAL: The Independent
Ian Johnston Science Correspondent
Thursday 2 February 2017

Quantum boost for artificial intelligence

By admin,

Quantum computers able to learn could attack larger sets of data than classical computers.


Peter Arnold/Stegerphoto/Getty Images

 

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Programs running on future quantum computers could dramatically speed up complex tasks such as face recognition.
Quantum computers of the future will have the potential to give artificial intelligence a major boost, a series of studies suggests.
These computers, which encode information in ‘fuzzy’ quantum states that can be zero and one simultaneously, have the ability to someday solve problems, such as breaking encryption keys, that are beyond the reach of ‘classical’ computers.
Algorithms developed so far for quantum computers have typically focused on problems such as breaking encryption keys or searching a list — tasks that normally require speed but not a lot of intelligence. But in a series of papers posted online this month the arXiv preprint server1, 2, 3, Seth Lloyd of the Massachusetts Institute of Technology in Cambridge and his collaborators have put a quantum twist on AI.
The team developed a quantum version of ‘machine learning’, a type of AI in which programs can learn from previous experience to become progressively better at finding patterns in data. Machine learning is popular in applications ranging from e-mail spam filters to online-shopping suggestions. The team’s invention would take advantage of quantum computations to speed up machine-learning tasks exponentially.
Quantum leap
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At the heart of the scheme is a simpler algorithm that Lloyd and his colleagues developed in 2009 as a way of quickly solving systems of linear equations, each of which is a mathematical statement, such as x + y = 4. Conventional computers produce a solution through tedious number crunching, which becomes prohibitively difficult as the amount of data (and thus the number of equations) grows. A quantum computer can cheat by compressing the information and performing calculations on select features extracted from the data and mapped onto quantum bits, or qubits.
Quantum machine learning takes the results of algebraic manipulations and puts them to good use. Data can be split into groups — a task that is at the core of handwriting- and speech-recognition software — or can be searched for patterns. Massive amounts of information could therefore be manipulated with a relatively small number of qubits.
We could map the whole Universe — all of the information that has existed since the Big Bang — onto 300 qubits,” Lloyd says.
Such quantum AI techniques could dramatically speed up tasks such as image recognition for comparing photos on the web or for enabling cars to drive themselves — fields in which companies such as Google have invested considerable resources. (One of Lloyd’s collaborators, Masoud Mohseni, is in fact a Google researcher based in Venice, California.)
It’s really interesting to see that there are new ways to use quantum computers coming up, after focusing mostly on factoring and quantum searches,” says Stefanie Barz at the University of Vienna, who recently demonstrated quantum equation-solving in action. Her team used a simple quantum computer that had two qubits to work out a high-school-level maths problem: a system consisting of two equations4. Another group, led by Jian Pan at the University of Science and Technology of China in Hefei, did the same using four qubits5.
Putting quantum machine learning into practice will be more difficult. Lloyd estimates that a dozen qubits would be needed for a small-scale demonstration.

Nature doi:10.1038/nature.2013.13453

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ORIGINAL: Nature
26 July 2013

Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Other Huge Engineering Efforts

By admin,

ORIGINAL: IEEE Spectrum
By Lee Gomes
20 Oct 2014
Big-data boondoggles and brain-inspired chips are just two of the things we’re really getting wrong
Photo-Illustration: Randi Klett

The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware designers creating chips based on the human brain are engaged in a faith-based undertaking likely to prove a fool’s errand. 

Despite recent claims to the contrary, we are no further along with computer vision than we were with physics when Isaac Newton sat under his apple tree.

Those may sound like the Luddite ravings of a crackpot who breached security at an IEEE conference. In fact, the opinions belong to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley. Jordan is one of the world’s most respected authorities on machine learning and an astute observer of the field. His CV would require its own massive database, and his standing in the field is such that he was chosen to write the introduction to the 2013 National Research Council report “Frontiers in Massive Data Analysis.” San Francisco writer Lee Gomes interviewed him for IEEE Spectrum on 3 October 2014.
Michael Jordan on…

 

1- Why We Should Stop Using Brain Metaphors When We Talk About Computing

… Continue reading

How D-Wave Built Quantum Computing Hardware for the Next Generation

By admin,

ORIGINAL: IEEE Spectrum
By Jeremy Hsu
11 Jul 2014

Photo: D-Wave Systems

One second is here and gone before most of us can think about it. But a delay of one second can seem like an eternity in a quantum computer capable of running calculations in millionths of a second. That’s why engineers at D-Wave Systems worked hard to eliminate the one-second computing delay that existed in the D-Wave One—the first-generation version of what the company describes as the world’s first commercial quantum computer.

Such lessons learned from operating D-Wave One helped shape the hardware design of D-Wave Two, a second-generation machine that has already been leased by customers such as Google, NASA, and Lockheed Martin. Such machines have not yet proven that they can definitely outperform classical computers in a way that would support D-Wave’s particular approach to building quantum computers. But the hardware design philosophy behind D-Wave’s quantum computing architecture points to how researchers could build increasingly more powerful quantum computers in the future.

We have room for increasing the complexity of the D-Wave chip,” says Jeremy Hilton, vice president of processor development at D-Wave Systems. “If we can fix the number of control lines per processor regardless of size, we can call it truly scalable quantum computing technology.

D-Wave recently explained the hardware design choices it made in going from D-Wave One to D-Wave Two in the June 2014 issue of the journal IEEE Transactions on Applied Superconductivity. Such details illustrate the engineering challenges that researchers still face in building a practical quantum computer capable of surpassing classical computers. (See IEEE Spectrum’s overview of the D-Wave machines’ performance from the December 2013 issue.)

  

Photo: D-Wave SystemsD-Wave’s Year of Computing Dangerously

Quantum computing holds the promise of speedily solving tough problems that ordinary computers would take practically forever to crack. Unlike classical computing that represents information as bits of either a 1 or 0, quantum computers take advantage of quantum bits (qubits) that can exist as both a 1 and 0 at the same time, enabling them to perform many simultaneous calculations.

Classical computer hardware has relied upon silicon transistors that can switch between “on” and “off” to represent the 1 or 0 in digital information. By comparison, D-Wave’s quantum computing hardware relies on metal loops of niobium that have tiny electrical currents running through them. A current running counterclockwise through the loop creates a tiny magnetic field pointing up, whereas a clockwise current leads to a magnetic field pointing down. Those two magnetic field states represent the equivalent of 1 or 0.

The niobium loops become superconductors when chilled to frigid temperatures of 20 millikelvin (-273 degrees C). At such low temperatures, the currents and magnetic fields can enter the strange quantum state known as “superposition” that allows them to represent both 1 and 0 states simultaneously. That allows D-Wave to use these “superconducting qubits” as the building blocks for making a quantum computing machine. Each loop also contains a number of Josephson junctions—two layers of superconductor separated by a thin insulating layer—that act as a framework of switches for routing magnetic pulses to the correct locations.

But a bunch of superconducting qubits and their connecting couplers—separate superconducting loops that allow qubits to exchange information—won’t do any computing all by themselves. D-Wave initially thought it would rely on analog control lines that could apply a magnetic field to the superconducting qubits and control their quantum states in that manner. However, the company realized early on in development that it would need at least six or seven control lines per qubit, for a programmable computer. The dream of eventually building more powerful machines with thousands of qubits would become an “impossible engineering challenge” with such design requirements, Hilton says.

The solution came in the form of digital-to-analog flux converters (DAC)—each about the size of a human red blood cell at 10 micrometers in width— that act as control devices and sit directly on the quantum computer chip. Such devices can replace control lines by acting as a form of programmable magnetic memory that produces a static magnetic field to affect nearby qubits. D-Wave can reprogram the DACs digitally to change the “bias” of their magnetic fields, which in turn affects the quantum computing operations.

Most researchers have focused on building quantum computers using the traditional logic-gate model of computing. But D-Wave has focused on a more specialized approach known as “quantum annealing —a method of tackling optimization problems. Solving optimization problems means finding the lowest “valley” that represents the best solution in a problem “landscape” with peaks and valleys. In practical terms, D-Wave starts a group of qubits in their lowest energy state and then gradually turns on interactions between the qubits, which encodes a quantum algorithm. When the qubits settle back down in their new lowest-energy state, D-Wave can read out the qubits to get the results.

Both the D-Wave One (128 qubits) and D-Wave Two (512 qubits) processors have DACs. But the circuitry setup of D-Wave One created some problems between the programming DAC phase and the quantum annealing operations phase. Specifically, the D-Wave One programming phase temporarily raised the temperature to as much as 500 millikelvin, which only dropped back down to the 20 millikelvin temperature necessary for quantum annealing after one second. That’s a significant delay for a machine that can perform quantum annealing in just 20 microseconds (20 millionths of a second).

By simplifying the hardware architecture and adding some more control lines, D-Wave managed to largely eliminate the temperature rise. That in turn reduced the post-programming delay to about 10 milliseconds (10 thousandths of a second)— a “factor of 100 improvement achieved within one processor generation,” Hilton says. D-Wave also managed to reduce the physical size of the DAC “footprint” by about 50 percent in D-Wave Two.

Building ever-larger arrays of qubits continues to challenge D-Wave’s engineers. They must always be aware of how their hardware design—packed with many classical computing components—can affect the fragile quantum states and lead to errors or noise that overwhelms the quantum annealing operations.

We were nervous about going down this path,” Hilton says. “This architecture requires the qubits and the quantum devices to be intermingled with all these big classical objects. The threat you worry about is noise and impact of all this stuff hanging around the qubits. Traditional experiments in quantum computing have qubits in almost perfect isolation. But if you want quantum computing to be scalable, it will have to be immersed in a sea of computing complexity.”

Still, D-Wave’s current hardware architecture, code-named “Chimera,” should be capable of building quantum computing machines of up to 8000 qubits, Hilton says. The company is also working on building a larger processor containing 1000 qubits.

The architecture isn’t necessarily going to stay the same, because we’re constantly learning about performance and other factors,” Hilton says. “But each time we implement a generation, we try to give it some legs so we know it’s extendable.”

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Google and NASA’s Quantum Artificial Intelligence Lab

By admin,

ORIGINAL: 33rdSquare
Announced in May, Google and NASA‘s Quantum Artificial Intelligence Lab has now been introduced in a film takes a look at various researchers working on the project, as well as the computer itself.
Google has produced a video introducing some of the people involved with the newly founded Quantum Artificial Intelligence Lab. In May, in partnership with NASA, Google announced the Quantum A.I. Lab, a place where researchers from around the world can experiment with the incredible powers and possibilities of quantum computing. The facility, located in a NASA research center uses D-Wave’s quantum computers. It is still early days, but Google thinks quantum computing can help solve some of the world’s most challenging computer science problems. The company is particularly interested in how quantum computing can advance machine learning, which can then be applied to virtually any field: from finding the cure for a disease to understanding changes in our climate.
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D-Wave’s Geordie Rose, who is featured in the video commented on his blog,
There are some great memes in the video. One of my favorites was raised by Sergio Boixo. He says at 4:25, ‘… [this machine] teaches us that we shouldn’t be naive about the world, and we shouldn’t think about the world as a simple machine. It forces us to consider more sophisticated notions of how the reality around us is actually shaped.’
In the video, Rose comments that the ultimate problem for quantum computers to work on are optimization problems. Speaking non-technically he also offers,
How amazing is it that we, with our monkey heritage and monkey brains and monkey fingers, have lucked into a brain that allows us to ask legitimate questions about the nature of physical reality. That’s so cool.
The film also features commentator Jason Silva ruminating on the possibilities of the technology. Silva also gets the poetic last words:
It’s that human risk to go forth into that unknown frontier, whether it’s space exploration or quantum exploration. We do it because we must. We do it because that what it means to be human.

NASA And Google Partner To Work With A D-Wave Quantum Computer

By admin,

D-Wave 512-Qubit Bonded Processor – Recent Generation (Credit: D-Wave)

D-Wave, the Canadian-based company that is the first to offer a commercial quantum computer, announced today that it’s sold its second $10 million D-Wave Two system. The contract is between the Universities Space Research Association and D-Wave. Google, USRA, and NASA will be collaborating on the use of the machine.

The system will be installed at a new lab, which will be located at NASA’s Ames Research Center. The computer is expected to go online in the third quarter of 2013. In addition to the sale, D-Wave will also be providing ongoing services such as maintenance. The company also expects to work closely with NASA, Google and USRA on the system.

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“I expect this to be a collaboration,” D-Wave’s U.S. President Bo Ewald told me. “Some of our scientists, mathematicians and computer scientists will be working at the Center.

Prior to selecting the contract with D-Wave, the partnership first conducted a series of benchmarks on the 512-qubit D-Wave Two system, and found that its specifications were met or exceeded. The computer will be upgraded to a 2,048 qubit system once D-Wave has perfected that chip.

It’s important to note that the D-Wave system is not a general computer like your PC. Rather, it’s optimized to solve particular types of problem, and it likely uses quantum effects to solve those problems.

(Whether the D-Wave system uses a quantum process for its computation has been a matter of hot dispute in academia. However, recent research by a USC team working with Lockheed Martin LMT -0.05%‘s D-Wave system appears to show that there are, indeed, quantum effects happening with the system. Whether those quantum effects produce a “speedup” – that is, computation faster than classical methods – is still an open question.)

The laboratory at Ames will be using the D-Wave System for a number of applications, but they’ll be focused on improving algorithms that are used to improve machine learning and artificial intelligence. The lab will also investigate whether the system can optimize the search for planets outside of our solar system.

We hope it helps researchers construct more efficient, effective models for everything from speech recognition, to web search, to protein folding,” Google said in a statement.

Under the terms of the agreement, 20% of the usage of the computer will be granted to University research. Research teams will compete to have their proposal use the machine selected. Once they’ve passed through that selection process, however, they’ll be granted use of the system free of charge.

For his part, Ewald is pretty excited about this step for the fledgling company. “For a company that’s just starting out, having Lockheed Martin as our first customer, then Google and NASA as number two? Well, that’s just a great way to start.

Update: An earlier version of this article indicated that NASA had partnered to purchase the D-Wave System. A NASA spokesperson clarified that while NASA is partnered with Google and USRA to use the system, NASA is “not purchasing or leasing it”.