Tag Archives: Patrick Rebentrost

Machine learning software and quantum computers that think

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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.

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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.

100 percent efficiency transporting the energy of sunlight from receptors to reaction centers

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Genetic engineering has been combined with elements of quantum physics to find a better way of transferring the energy derived from sunlight from the receptors to the reaction centers (i.e., photosynthesis). From an Oct. 15, 2015 news item on Nanowerk,

Nature has had billions of years to perfect photosynthesis, which directly or indirectly supports virtually all life on Earth. In that time, the process has achieved almost 100 percent efficiency in transporting the energy of sunlight from receptors to reaction centers where it can be harnessed — a performance vastly better than even the best solar cells.

One way plants achieve this efficiency is by making use of the exotic effects of quantum mechanics — effects sometimes known as “quantum weirdness.” These effects, which include the ability of a particle to exist in more than one place at a time [superposition], have now been used by engineers at MIT to achieve a significant efficiency boost in a light-harvesting system.

Surprisingly, the MIT [Massachusetts Institute of Technology] researchers achieved this new approach to solar energy not with high-tech materials or microchips — but by using genetically engineered viruses.

An Oct. 15, 2015 MIT news release (also on EurekAlert), which originated the news item, recounts an exciting tale of interdisciplinary work and an international collaboration,

This achievement in coupling quantum research and genetic manipulation, described this week in the journal Nature Materials, was the work of MIT professors Angela Belcher, an expert on engineering viruses to carry out energy-related tasks, and Seth Lloyd, an expert on quantum theory and its potential applications; research associate Heechul Park; and 14 collaborators at MIT and in Italy.

Lloyd, a professor of mechanical engineering, explains that in photosynthesis, a photon hits a receptor called a chromophore, which in turn produces an exciton — a quantum particle of energy. This exciton jumps from one chromophore to another until it reaches a reaction center, where that energy is harnessed to build the molecules that support life.

But the hopping pathway is random and inefficient unless it takes advantage of quantum effects that allow it, in effect, to take multiple pathways at once and select the best ones, behaving more like a wave than a particle.

This efficient movement of excitons has one key requirement: The chromophores have to be arranged just right, with exactly the right amount of space between them. This, Lloyd explains, is known as the “Quantum Goldilocks Effect.”

That’s where the virus comes in. By engineering a virus that Belcher has worked with for years, the team was able to get it to bond with multiple synthetic chromophores — or, in this case, organic dyes. The researchers were then able to produce many varieties of the virus, with slightly different spacings between those synthetic chromophores, and select the ones that performed best.

In the end, they were able to more than double excitons’ speed, increasing the distance they traveled before dissipating — a significant improvement in the efficiency of the process.

The project started from a chance meeting at a conference in Italy. Lloyd and Belcher, a professor of biological engineering, were reporting on different projects they had worked on, and began discussing the possibility of a project encompassing their very different expertise. Lloyd, whose work is mostly theoretical, pointed out that the viruses Belcher works with have the right length scales to potentially support quantum effects.

In 2008, Lloyd had published a paper demonstrating that photosynthetic organisms transmit light energy efficiently because of these quantum effects. When he saw Belcher’s report on her work with engineered viruses, he wondered if that might provide a way to artificially induce a similar effect, in an effort to approach nature’s efficiency.

“I had been talking about potential systems you could use to demonstrate this effect, and Angela said, ‘We’re already making those,'” Lloyd recalls. Eventually, after much analysis, “We came up with design principles to redesign how the virus is capturing light, and get it to this quantum regime.”

Within two weeks, Belcher’s team had created their first test version of the engineered virus. Many months of work then went into perfecting the receptors and the spacings.

Once the team engineered the viruses, they were able to use laser spectroscopy and dynamical modeling to watch the light-harvesting process in action, and to demonstrate that the new viruses were indeed making use of quantum coherence to enhance the transport of excitons.

“It was really fun,” Belcher says. “A group of us who spoke different [scientific] languages worked closely together, to both make this class of organisms, and analyze the data. That’s why I’m so excited by this.”

While this initial result is essentially a proof of concept rather than a practical system, it points the way toward an approach that could lead to inexpensive and efficient solar cells or light-driven catalysis, the team says. So far, the engineered viruses collect and transport energy from incoming light, but do not yet harness it to produce power (as in solar cells) or molecules (as in photosynthesis). But this could be done by adding a reaction center, where such processing takes place, to the end of the virus where the excitons end up.

MIT has produced a video explanation of the work,

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

Enhanced energy transport in genetically engineered excitonic networks by Heechul Park, Nimrod Heldman, Patrick Rebentrost, Luigi Abbondanza, Alessandro Iagatti, Andrea Alessi, Barbara Patrizi, Mario Salvalaggio, Laura Bussotti, Masoud Mohseni, Filippo Caruso, Hannah C. Johnsen, Roberto Fusco, Paolo Foggi, Petra F. Scudo, Seth Lloyd, & Angela M. Belcher. Nature Materials (2015) doi:10.1038/nmat4448 Published online 12 October 2015

This paper is behind a paywall.