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Alright, so I'd like to first off thank
you guys for having me here. This is an
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amazing conference. So far, during lunch,
someone gave me multiple high-fives in a
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conversation about hypervisors, so I feel
like I'm in a room of kindred spirits.
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Just to test that out, who wants to hear
about math? (room cheers)
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That would only work here. I'll tell you a
bit about myself, and then a bit about the
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talk, and then I'll give you the talk.
I'm a security researcher first, but also
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I'm a PhD candidate in mathematics at
the University of Waterloo; if you haven't
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heard of it, it's in Canada. I work on
quantum algorithms for cryptanalysis,
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so what this means is we look at the
difficulty of computational problems on
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which cryptography is built, and we find
sometimes that the problems that we thought
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were very hard on a classical computer are
in fact very easy on our proposed quantum
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systems. What I'm going to talk to you about
today is a bit related to that, but I'd
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like to ground it in programming and
thinking about programming languages and
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systems that we can actually construct.
What I hope you can take away from the talk
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I'm about to give is an understanding of
what -- It's not quite the network stack,
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but what the various levels of abstraction
are in quantum computing and how a room
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full of people inclined toward the use of
classical conventional computing could make
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use of quantum resources for speedups in
their algorithms.
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Whenever I'm getting a big head about how
I feel about my research, I have to remind
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myself that I spend all day thinking about
imaginary computers, and that brings me
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right back down. Quantum computing, a very
simple definition is using quantum mechanical
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phenomena like entanglement or superposition
to perform computational tasks. Why do we
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do this? It wouldn't make a lot of sense if
it were just as good as the computers we
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have, because quantum computers are very,
very expensive, and so far we have 15 or so
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qubits, and maybe that's not as useful as
what we're looking for, so why do we have
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these very expensive computers? Because
we think that we can have some tradeoff and
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have less computationally expensive algorithms.
This is only true if we can scale it up.
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To be useful, a quantum computer needs to
scale, but this is very challenging, because
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the things that make a quantum computer
useful are the exact things that are getting
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in its way. Quantum computers are very
sensitive to environmental noise. If we
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have a qubit in a room, and someone walks
down a hall in a non-specialized building
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many floors away, it will disturb our qubit
and it will collapse, and maybe we're not
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doing our computation. So we have to have
very specialized buildings. Another thing
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is we have a challenge with measurement in
quantum systems. When we measure a quantum
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state, we destroy a superposition, which
is something I'll explain, and it's really
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hard to create memory. Beyond this, there
are other things, but I don't want to use
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too much time.
I stole this from Scott Aaronson, who is a
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phenomenal phsysicist. Quantum mechanics
in one slide:
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You can think of quantum mechanics as
probability theory, but you're doing it over
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the complex numbers, and this allows us to
compute things in a really interesting way.
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The basic unit for a quantum computer is a
qubit. We have regular bits, 1s and 0s, and
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a quantum bit is a superposition, which is
kind of when we put them all together, of
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values 0 and 1. We're creating some linear
combination of things, and that's when
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something is both values at the same time.
With measurement or noise, we can collapse
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this superposition, and what's interesting
about it is sometimes we'll get a 0 out
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and sometimes we'll get a 1 out, but we
can set the same superposition up over and
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over again, and sometimes get a 1 and some
times get a 0 and not really know what's
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going to happen.
