Tag Archives: D-Wave

Machine learning software and quantum computers that think

A Sept. 14, 2017 news item on phys.org sets the stage for quantum machine learning by explaining a few basics first,

Language acquisition in young children is apparently connected with their ability to detect patterns. In their learning process, they search for patterns in the data set that help them identify and optimize grammar structures in order to properly acquire the language. Likewise, online translators use algorithms through machine learning techniques to optimize their translation engines to produce well-rounded and understandable outcomes. Even though many translations did not make much sense at all at the beginning, in these past years we have been able to see major improvements thanks to machine learning.

Machine learning techniques use mathematical algorithms and tools to search for patterns in data. These techniques have become powerful tools for many different applications, which can range from biomedical uses such as in cancer reconnaissance, in genetics and genomics, in autism monitoring and diagnosis and even plastic surgery, to pure applied physics, for studying the nature of materials, matter or even complex quantum systems.

Capable of adapting and changing when exposed to a new set of data, machine learning can identify patterns, often outperforming humans in accuracy. Although machine learning is a powerful tool, certain application domains remain out of reach due to complexity or other aspects that rule out the use of the predictions that learning algorithms provide.

Thus, in recent years, quantum machine learning has become a matter of interest because of is vast potential as a possible solution to these unresolvable challenges and quantum computers show to be the right tool for its solution.

A Sept. 14, 2017 Institute of Photonic Sciences ([Catalan] Institut de Ciències Fotòniques] ICFO) press release, which originated the news item, goes on to detail a recently published overview of the state of quantum machine learning,

In a recent study, published in Nature, an international team of researchers integrated by Jacob Biamonte from Skoltech/IQC, Peter Wittek from ICFO, Nicola Pancotti from MPQ, Patrick Rebentrost from MIT, Nathan Wiebe from Microsoft Research, and Seth Lloyd from MIT have reviewed the actual status of classical machine learning and quantum machine learning. In their review, they have thoroughly addressed different scenarios dealing with classical and quantum machine learning. In their study, they have considered different possible combinations: the conventional method of using classical machine learning to analyse classical data, using quantum machine learning to analyse both classical and quantum data, and finally, using classical machine learning to analyse quantum data.

Firstly, they set out to give an in-depth view of the status of current supervised and unsupervised learning protocols in classical machine learning by stating all applied methods. They introduce quantum machine learning and provide an extensive approach on how this technique could be used to analyse both classical and quantum data, emphasizing that quantum machines could accelerate processing timescales thanks to the use of quantum annealers and universal quantum computers. Quantum annealing technology has better scalability, but more limited use cases. For instance, the latest iteration of D-Wave’s [emphasis mine] superconducting chip integrates two thousand qubits, and it is used for solving certain hard optimization problems and for efficient sampling. On the other hand, universal (also called gate-based) quantum computers are harder to scale up, but they are able to perform arbitrary unitary operations on qubits by sequences of quantum logic gates. This resembles how digital computers can perform arbitrary logical operations on classical bits.

However, they address the fact that controlling a quantum system is very complex and analyzing classical data with quantum resources is not as straightforward as one may think, mainly due to the challenge of building quantum interface devices that allow classical information to be encoded into a quantum mechanical form. Difficulties, such as the “input” or “output” problems appear to be the major technical challenge that needs to be overcome.

The ultimate goal is to find the most optimized method that is able to read, comprehend and obtain the best outcomes of a data set, be it classical or quantum. Quantum machine learning is definitely aimed at revolutionizing the field of computer sciences, not only because it will be able to control quantum computers, speed up the information processing rates far beyond current classical velocities, but also because it is capable of carrying out innovative functions, such quantum deep learning, that could not only recognize counter-intuitive patterns in data, invisible to both classical machine learning and to the human eye, but also reproduce them.

As Peter Wittek [emphasis mine] finally states, “Writing this paper was quite a challenge: we had a committee of six co-authors with different ideas about what the field is, where it is now, and where it is going. We rewrote the paper from scratch three times. The final version could not have been completed without the dedication of our editor, to whom we are indebted.”

It was a bit of a surprise to see local (Vancouver, Canada) company D-Wave Systems mentioned but i notice that one of the paper’s authors (Peter Wittek) is mentioned in a May 22, 2017 D-Wave news release announcing a new partnership to foster quantum machine learning,

Today [May 22, 2017] D-Wave Systems Inc., the leader in quantum computing systems and software, announced a new initiative with the Creative Destruction Lab (CDL) at the University of Toronto’s Rotman School of Management. D-Wave will work with CDL, as a CDL Partner, to create a new track to foster startups focused on quantum machine learning. The new track will complement CDL’s successful existing track in machine learning. Applicants selected for the intensive one-year program will go through an introductory boot camp led by Dr. Peter Wittek [emphasis mine], author of Quantum Machine Learning: What Quantum Computing means to Data Mining, with instruction and technical support from D-Wave experts, access to a D-Wave 2000Q™ quantum computer, and the opportunity to use a D-Wave sampling service to enable machine learning computations and applications. D-Wave staff will be a part of the committee selecting up to 40 individuals for the program, which begins in September 2017.

For anyone interested in the paper, here’s a link to and a citation,

Quantum machine learning by Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, & Seth Lloyd. Nature 549, 195–202 (14 September 2017) doi:10.1038/nature23474 Published online 13 September 2017

This paper is behind a paywall.

D-Wave upgrades Google’s quantum computing capabilities

Vancouver-based (more accurately, Burnaby-based) D-Wave systems has scored a coup as key customers have upgraded from a 512-qubit system to a system with over 1,000 qubits. (The technical breakthrough and concomitant interest from the business community was mentioned here in a June 26, 2015 posting.) As for the latest business breakthrough, here’s more from a Sept. 28, 2015 D-Wave press release,

D-Wave Systems Inc., the world’s first quantum computing company, announced that it has entered into a new agreement covering the installation of a succession of D-Wave systems located at NASA’s Ames Research Center in Moffett Field, California. This agreement supports collaboration among Google, NASA and USRA (Universities Space Research Association) that is dedicated to studying how quantum computing can advance artificial intelligence and machine learning, and the solution of difficult optimization problems. The new agreement enables Google and its partners to keep their D-Wave system at the state-of-the-art for up to seven years, with new generations of D-Wave systems to be installed at NASA Ames as they become available.

“The new agreement is the largest order in D-Wave’s history, and indicative of the importance of quantum computing in its evolution toward solving problems that are difficult for even the largest supercomputers,” said D-Wave CEO Vern Brownell. “We highly value the commitment that our partners have made to D-Wave and our technology, and are excited about the potential use of our systems for machine learning and complex optimization problems.”

Cade Wetz’s Sept. 28, 2015 article for Wired magazine provides some interesting observations about D-Wave computers along with some explanations of quantum computing (Note: Links have been removed),

Though the D-Wave machine is less powerful than many scientists hope quantum computers will one day be, the leap to 1000 qubits represents an exponential improvement in what the machine is capable of. What is it capable of? Google and its partners are still trying to figure that out. But Google has said it’s confident there are situations where the D-Wave can outperform today’s non-quantum machines, and scientists at the University of Southern California [USC] have published research suggesting that the D-Wave exhibits behavior beyond classical physics.

A quantum computer operates according to the principles of quantum mechanics, the physics of very small things, such as electrons and photons. In a classical computer, a transistor stores a single “bit” of information. If the transistor is “on,” it holds a 1, and if it’s “off,” it holds a 0. But in quantum computer, thanks to what’s called the superposition principle, information is held in a quantum system that can exist in two states at the same time. This “qubit” can store a 0 and 1 simultaneously.

Two qubits, then, can hold four values at any given time (00, 01, 10, and 11). And as you keep increasing the number of qubits, you exponentially increase the power of the system. The problem is that building a qubit is a extreme difficult thing. If you read information from a quantum system, it “decoheres.” Basically, it turns into a classical bit that houses only a single value.

D-Wave claims to have a found a solution to the decoherence problem and that appears to be borne out by the USC researchers. Still, it isn’t a general quantum computer (from Wetz’s article),

… researchers at USC say that the system appears to display a phenomenon called “quantum annealing” that suggests it’s truly operating in the quantum realm. Regardless, the D-Wave is not a general quantum computer—that is, it’s not a computer for just any task. But D-Wave says the machine is well-suited to “optimization” problems, where you’re facing many, many different ways forward and must pick the best option, and to machine learning, where computers teach themselves tasks by analyzing large amount of data.

It takes a lot of innovation before you make big strides forward and I think D-Wave is to be congratulated on producing what is to my knowledge the only commercially available form of quantum computing of any sort in the world.

ETA Oct. 6, 2015* at 1230 hours PST: Minutes after publishing about D-Wave I came across this item (h/t Quirks & Quarks twitter) about Australian researchers and their quantum computing breakthrough. From an Oct. 6, 2015 article by Hannah Francis for the Sydney (Australia) Morning Herald,

For decades scientists have been trying to turn quantum computing — which allows for multiple calculations to happen at once, making it immeasurably faster than standard computing — into a practical reality rather than a moonshot theory. Until now, they have largely relied on “exotic” materials to construct quantum computers, making them unsuitable for commercial production.

But researchers at the University of New South Wales have patented a new design, published in the scientific journal Nature on Tuesday, created specifically with computer industry manufacturing standards in mind and using affordable silicon, which is found in regular computer chips like those we use every day in smartphones or tablets.

“Our team at UNSW has just cleared a major hurdle to making quantum computing a reality,” the director of the university’s Australian National Fabrication Facility, Andrew Dzurak, the project’s leader, said.

“As well as demonstrating the first quantum logic gate in silicon, we’ve also designed and patented a way to scale this technology to millions of qubits using standard industrial manufacturing techniques to build the world’s first quantum processor chip.”

According to the article, the university is looking for industrial partners to help them exploit this breakthrough. Fisher’s article features an embedded video, as well as, more detail.

*It was Oct. 6, 2015 in Australia but Oct. 5, 2015 my side of the international date line.

ETA Oct. 6, 2015 (my side of the international date line): An Oct. 5, 2015 University of New South Wales news release on EurekAlert provides additional details.

Here’s a link to and a citation for the paper,

A two-qubit logic gate in silicon by M. Veldhorst, C. H. Yang, J. C. C. Hwang, W. Huang,    J. P. Dehollain, J. T. Muhonen, S. Simmons, A. Laucht, F. E. Hudson, K. M. Itoh, A. Morello    & A. S. Dzurak. Nature (2015 doi:10.1038/nature15263 Published online 05 October 2015

This paper is behind a paywall.