Session A14
Paper # 6210
THE INTEGRATION OF QUANTUM COMPUTING IN MACHINE LEARNING
AND NEURAL SYSTEMS
Brian Borig, btb32@pitt.edu, 4:00-5:50 Mena, Chris Gardiner, cjg70@pitt.edu, 4:00-5:50 Mena
Abstract
Through the integration of quantum computing in machine
learning and neural systems, some of the world’s most difficult
problems can be solved through computers that adapt and learn
much like humans except at a hyper-accelerated pace. Modern
computers lack the processing power required to solve many
of the complex problems that scientists and mathematicians
need them to. The limitations that are apparent with standard
computers can be overcome with quantum computers. These
take advantage of a different style of processing. Current day
computers use bits to process information, whereas quantum
computers operate using qubits to allow for a higher
processing capacity. Some problems, such as advanced
optimization problems which include thousands of variables,
would take thousands or millions of years for a standard
computer to solve but could be solved by a quantum computer
in a matter of only hours or days. An example of one of these
optimization problems that a quantum computer could render
is seen in space exploration where the computer needs to
consider variables such as changing temperature, fluctuating
gravitational fields, rapid pressure changes, and then decide
the optimal course of action to explore that planet. This intense
problem solving ability has powerful applications in machine
learning, where it can process the enormous quantity of
variables required to solve these problems.
Artificial Intelligence is a subset of machine learning
which, “refers to an area of computer science in which patterns
are derived (‘learned’) from data with the goal to make sense
of previously unknown inputs.”[1] Through machine learning,
the computer is given a series of known truths on which it can
build through experience, similar to the human brain would
function hence the name artificial intelligence. On a
rudimentary level, artificial intelligence tries a series of
solutions and then assess which solution provided the optimal
outcome. It then remembers the pathway to get to this
outcome, and thus learns like the human brain does. For
example, in checkers the artificial intelligence is given the
basic rules of the game to guide it in its move selection. With
this in mind, the computer determines the optimal plays it
should make in order of achieving the final goal of taking all
the opponent’s checkers learning along the way how best to
optimally beat the opponent. This is where the computer
begins acting much like a human would. Through a series of
pattern recognition programs the computer begins to adapt to
perceived patterns just as a human would. This electronic
University of Pittsburgh Swanson School of Engineering 1
1/29/2016
‘brain’ is known as the computers neural system and behaves
much like the human brain would with these pattern
recognition concepts.
Machine is often totally absorbed by one problem
such as checkers, the processing power of modern day
computers cannot yet handle multiple problems at once. Using
quantum computers, the pattern recognition involved in a
computer’s neural system could theoretically handle hundreds
and thousands of problems just like the human brain because
of the enormous processing potential of these computers.
In this paper we will research and discuss first the
theory behind both quantum computing and machine learning
eventually combining the two technologies through the
concept of neural systems and discussing the benefits as well
as ethics of the combination.
ANNOTATED BIBLIOGRAPHY
[1] M. Brooks. (2014). “Quantum computing best buys.” New
Scientist.
(online
article)
http://web.a.ebscohost.com/ehost/detail/detail?sid=8735b600116e-446e-979195a0eaba244e%40sessionmgr4002&vid=21&hid=4201&bdat
a=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=aph&AN=98
969742. p. 43-47
This online article comes from the UK based New
Scientist magazine providing information about latest sciences
and technologies. It describes the concepts of quantum
computing such as qubits and entanglement which ultimately
allow the computer to process information at a far faster rate
than classical computers. The information of this article helps
us explain the complex concepts of qubits and entanglement at
a more rudimentary level as this magazine is designed for
readers of all backgrounds.
[2] B. O’Gorman, A. Perdomo-Ortiz, R. Babbush, A. AspuruGuzik, V. Smelyanskiy, (2014). “Bayesian Network Structure
Learning Using Quantum Annealing.” ArXiv. (Online
Article). http://arxiv.org/pdf/1407.3897.pdf
Written by a group of NASA scientists in the
Quantum Artificial Intelligence Laboratory (QuAIL) and
Harvard University professors, this online article explores the
power and use of quantum computing in relation to problem
solving. Specifically, it focuses on Bayesian networks, which
is a system that relates different variables graphically to see
their dependence. This article will help us explain how
quantum computers and algorithms can be specifically used for
machine learning and artificial intelligence.
Brian Borig
Chris Gardiner
Francis Online. (online article) DOI: 10.1080/00107514. pp.
172-185
This journal, written by two members of the South
African National Institute for Theoretical Physics and a PhD
graduate of Technical University of Berlin, details the process
of machine learning and its possibilities in pattern recognition
using quantum computing theory also outlining the basics of
hidden quantum markov models. We will use this information
to describe the basic theory behind the markov models,
supplement the sources we already have on machine learning
and quantum computing, and point out the possibilities in
pattern recognition.
[3] M. Shuld, I. Sinayskiy, F. Petruccione. (2014).
“Quantum Computing for Pattern Classification.” Springer
International Publishing. (Conference Paper). p. 208-220
https://www.engineeringvillage.com/search/doc/detailed.url?
pageType=quickSearch&searchtype=Quick&SEARCHID=e4
b56413M6f57M4267M9c30Me9c61ffb9c16&DOCINDEX=
4&database=3&format=quickSearchDetailedFormat&dedup
ResultCount=&SEARCHID=e4b56413M6f57M4267M9c30
Me9c61ffb9c16
This paper was written by three members of the Quantum
Res. Group at the University of KwaZulu-Natal in Durban,
South Africa, for the 13th Pacific Rim International
Conference on Artificial Intelligence. They outline ways that a
quantum computer could improve classical machine learning
methods, basics on quantum computing, and pattern
recognition in computing. It could be used as a resource for
quantum computing in general, as well as a more specific
source of information for one way to utilize artificial
intelligence.
[4] G. Zhang, L. Hu, W. Jin. (2004). “Quantum Computing
Based Machine Learning Method and Its Application in Radar
Emitter Signal Recognition.” Modeling Decisions for
Artificial Intelligence. (Conference Paper). p. 92-103
https://www.engineeringvillage.com/search/doc/detailed.url?
pageType=quickSearch&searchtype=Quick&SEARCHID=e4
b56413M6f57M4267M9c30Me9c61ffb9c16&DOCINDEX=
1&database=3&format=quickSearchDetailedFormat&dedup
ResultCount=&SEARCHID=e4b56413M6f57M4267M9c30
Me9c61ffb9c16
Written by graduate level students in China in 2004 for the
First International Conference on Modeling Decisions for
Artificial Intelligence in Barcelona, Spain, this conference
paper focuses on machine learning specifically with regard to
feature selection in radar pulse emissions. It describes in depth
the algorithms behind its recognition and experimental data of
data analysis. It could be used to further specify on our topic
of machine learning and pattern recognition within that.
[7] No Author. (2015). “Quantum Artificial Intelligence
Laboratory.”
NASA.
(online
article).
http://www.nas.nasa.gov/quantum/.
NASA documents their latest project in this article,
the D-Wave Vesuvius. This quantum computer, monitored and
tested on by the NASA QuAIL (Quantum Artificial
Intelligence Laboratory) team, serves as real life example and
a measurement plausibility of the quantum computer
pertaining to artificial intelligence. In the paper, this source
will be shown as an example of the quantum computer’s
viability and reliability.
[8] H. Cao, F. Cao, D. Wang. (2014). “Quantum artificial
neural networks with applications.” ScienceDirect. (online
article). DOI: 10.2016/j.ins.
Written by researchers in various universities
studying computer engineering, this article introduces the
concept of quantum artificial neural networks (QANN) which
essentially replicates the neural systems of an organism
through an interconnected system of ‘quantum neurons’ that is
processed through a quantum computer. This will serve to
describe the last aspect of our paper in neural networks.
[5] N. Bostrom, E. Yudkowsky. (2011). “The Ethics of
Artificial Intelligence.” Cambridge Handbook of Artificial
Intelligence.
(Print).
http://www.nickbostrom.com/ethics/artificial-intelligence.pdf
Published in a Cambridge University compilation of
novels and written in conjunction by a professor at the
University of Oxford and a researcher at the Machine
Intelligence Research Institute. In their essay, they outline
many different issues that could potentially arise in regards to
artificial intelligence -- safety of AI as it becomes “smarter,”
whether AI should have rights like humans, and how we
should consider AI in regards to our own ethics. It will be very
useful for us in writing, as it talks about many different ethical
issues related to artificial intelligence.
[6] M. Schuld, I. Sinayskiy, F. Petruccione. (2014). “An
introduction to quantum machine learning.” Taylor and
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