Tag Archives: mathematical algorithms

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.

For first time: high-dimensional quantum encryption performed in real world city conditions

Having congratulated China on the world’s first quantum communication network a few weeks ago (August 22, 2017 posting), this quantum encryption story seems timely. From an August 24, 2017 news item on phys.org,

For the first time, researchers have sent a quantum-secured message containing more than one bit of information per photon through the air above a city. The demonstration showed that it could one day be practical to use high-capacity, free-space quantum communication to create a highly secure link between ground-based networks and satellites, a requirement for creating a global quantum encryption network.

Quantum encryption uses photons to encode information in the form of quantum bits. In its simplest form, known as 2D encryption, each photon encodes one bit: either a one or a zero. Scientists have shown that a single photon can encode even more information—a concept known as high-dimensional quantum encryption—but until now this has never been demonstrated with free-space optical communication in real-world conditions. With eight bits necessary to encode just one letter, for example, packing more information into each photon would significantly speed up data transmission.

This looks like donuts on a stick to me,

For the first time, researchers have demonstrated sending messages in a secure manner using high dimensional quantum cryptography in realistic city conditions. Image Credit: SQO team, University of Ottawa.

An Aug. 24, 2017 Optical Society news release (also on EurekAlert), which originated the news item, describes the work done by a team in Ottawa, Canada, (Note: The ‘Congratulate China’ piece (August 22, 2017 posting) includes excerpts from an article that gave a brief survey of various national teams [including Canada] working on quantum communication networks; Links have been removed),

“Our work is the first to send messages in a secure manner using high-dimensional quantum encryption in realistic city conditions, including turbulence,” said research team lead, Ebrahim Karimi, University of Ottawa, Canada. “The secure, free-space communication scheme we demonstrated could potentially link Earth with satellites, securely connect places where it is too expensive to install fiber, or be used for encrypted communication with a moving object, such as an airplane.”

For the first time, researchers have demonstrated sending messages in a secure manner using high dimensional quantum cryptography in realistic city conditions. Image Credit: SQO team, University of Ottawa.

As detailed in Optica, The Optical Society’s journal for high impact research, the researchers demonstrated 4D quantum encryption over a free-space optical network spanning two buildings 0.3 kilometers apart at the University of Ottawa. This high-dimensional encryption scheme is referred to as 4D because each photon encodes two bits of information, which provides the four possibilities of 01, 10, 00 or 11.

In addition to sending more information per photon, high-dimensional quantum encryption can also tolerate more signal-obscuring noise before the transmission becomes unsecure. Noise can arise from turbulent air, failed electronics, detectors that don’t work properly and from attempts to intercept the data. “This higher noise threshold means that when 2D quantum encryption fails, you can try to implement 4D because it, in principle, is more secure and more noise resistant,” said Karimi.

Using light for encryption

Today, mathematical algorithms are used to encrypt text messages, banking transactions and health information. Intercepting these encrypted messages requires figuring out the exact algorithm used to encrypt a given piece of data, a feat that is difficult now but that is expected to become easier in the next decade or so as computers become more powerful.

Given the expectation that current algorithms may not work as well in the future, more attention is being given to stronger encryption techniques such as quantum key distribution, which uses properties of light particles known as quantum states to encode and send the key needed to decrypt encoded data.

Although wired and free-space quantum encryption has been deployed on some small, local networks, implementing it globally will require sending encrypted messages between ground-based stations and the satellite-based quantum communication networks that would link cities and countries. Horizontal tests through the air can be used to simulate sending signals to satellites, with about three horizontal kilometers being roughly equal to sending the signal through the Earth’s atmosphere to a satellite.

Before trying a three-kilometer test, the researchers wanted to see if it was even possible to perform 4D quantum encryption outside. This was thought to be so challenging that some other scientists in the field said that the experiment would not work. One of the primary problems faced during any free-space experiment is dealing with air turbulence, which distorts the optical signal.

Real-world testing

For the tests, the researchers brought their laboratory optical setups to two different rooftops and covered them with wooden boxes to provide some protection from the elements. After much trial and error, they successfully sent messages secured with 4D quantum encryption over their intracity link. The messages exhibited an error rate of 11 percent, below the 19 percent threshold needed to maintain a secure connection. They also compared 4D encryption with 2D, finding that, after error correction, they could transmit 1.6 times more information per photon with 4D quantum encryption, even with turbulence.

“After bringing equipment that would normally be used in a clean, isolated lab environment to a rooftop that is exposed to the elements and has no vibration isolation, it was very rewarding to see results showing that we could transmit secure data,” said Alicia Sit, an undergraduate student in Karimi’s lab.

As a next step, the researchers are planning to implement their scheme into a network that includes three links that are about 5.6 kilometers apart and that uses a technology known as adaptive optics to compensate for the turbulence. Eventually, they want to link this network to one that exists now in the city. “Our long-term goal is to implement a quantum communication network with multiple links but using more than four dimensions while trying to get around the turbulence,” said Sit.

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

High-dimensional intracity quantum cryptography with structured photons by Alicia Sit, Frédéric Bouchard, Robert Fickler, Jérémie Gagnon-Bischoff, Hugo Larocque, Khabat Heshami, Dominique Elser, Christian Peuntinger, Kevin Günthner, Bettina Heim, Christoph Marquardt, Gerd Leuchs, Robert W. Boyd, and Ebrahim Karimi. Optica Vol. 4, Issue 9, pp. 1006-1010 (2017) •https://doi.org/10.1364/OPTICA.4.001006

This is an open access paper.