[MUSIC PLAYING]
Hey, I'm Diana, and you're watching "Physics Girl."
There's this thing that you see happen.
You see it happen every single day.
Phase transitions.
Liquid to solid.
Solid to liquid.
Liquid to gas.
And scientists understand these phase changes.
We understand them.
But there are some phase transitions in nature
that scientists don't fully understand because they
involve quantum mechanics.
The most mysterious phase changes
are called quantum phase transitions,
and they happen at the extremes.
For example, if you get a magnet hot enough,
you can get it to suddenly lose its magnetism.
Or in the coldest temperatures of, say, outer space,
you could get insulating materials to suddenly turn
into superconductors.
And so, yeah, we understand what's
going on with the individual particles
inside of, for example, water when water boils.
But we don't understand what the individual particles are
doing inside a superconductor.
It's an exciting frontier of physics
because it's a problem not yet solved by humans.
But what if something else could solve this problem?
There is something that's better at solving certain problems
than humans are.
Something that could give us better product recommendations
than we could give each other.
Something that could diagnose medical conditions better
than doctors could.
That thing is a machine Yeah, it's a weird thought.
But what if I told you there's a technique that scientists
and engineers are researching that can
help us solve this quantum phase transition problem?
That technique is quantum machine learning.
This is my editor Jabril.
Hi.
But he also has an awesome YouTube channel
about computer science.
And since I know nothing about machine learning,
he's gonna help us--
me-- figure out what machine learning is.
Yeah?
Oh yeah.
Yeah.
Oh yeah.
So you want to know what machine learning is, right?
Yes.
So, simply put, it's teaching an algorithm
to learn almost anything that you want it to learn.
An algorithm.
So, for example--
OK, for example, let's create this fantasy scenario in which,
like, you're a gardener who loves their plants, OK?
I have a garden!
Yes, that's right.
All those garden photos that you post.
OK, so let's use you as an example.
OK.
So you're a gardener.
You love your plants.
And let's say that you run into this problem
where you notice every once in a while your plants die, OK?
It happens.
After a while, you deduce that the problem is either from one
of two things, all right?
A, it's either from insects.
You know, ruining your plants.
Or B, dehydration.
Maybe you're not watering them good enough or something
like that, right?
You love your garden, and your plants are mysteriously dying.
OK.
If you could find out their cause of death
while they're dying, then you can treat them.
However, there's a problem.
The only way that you can find out the cause of death
is if you pull them from the roots and snap them in half.
You're not a very good gardener, but--
I'm sorry, I'm sorry.
We'll roll with this scenario.
JABRIL: And so what you do then is you start taking thousands
of photos of your dead plants.
So now you have this data set of a bunch of images
and their cause of death, OK?
So you will then train an algorithm
by inputting all of the photos you
took matched with their labels.
You know, the insects.
Insects.
Dehydration.
So on and so forth.
And the algorithm learns the differences
in features of images labeled as dehydration and the images
label as insects.
Yeah, so the process in which the algorithm learns all this
is actually really fascinating.
And I actually made a video over on my channel
if you want to come and check it out.
Jabril, focus!
Sorry, I'm sorry.
But actually it is really good.
I'll put a link in the description to it.
Thank you.
You can now-- this is the exciting part.
You can now take a new photo that the algorithm's never
even seen before.
Feed it to the algorithm, and it'll
give you a likely probability on the cause of death.
OK.
So now you don't have to kill your plants anymore.
Problem solved.
Yes!
Thanks to the machine learning.
So machine learning is this process
of training an algorithm on images
that you input with labels to learn enough
about the features of the images so that if you input
a new image, it can look at that image's features
and then figure out which of the categories it fits into.
Correct?
Kind of.
That's only one type of machine learning
that we went over today.
There are more?
There are lots more, yes.
OK, cool.
That's awesome.
But now to bring it back full circle to quantum phase
transitions, was it?
Yes!
Right.
We're gonna unpack more of that.
Thank you for your help, Jabril!
Yeah, any time.
Get outta here.
[LAUGHING]
OK, so, how can we apply machine learning
to quantum phase transitions?
I talked to a researcher at San Jose State University who
works on this exact problem.
Yeah, so I study properties of quantum systems
of few particles by doing simulations,
numerical simulations of these systems.
When you mention quantum phase transition,
I just want to make a distinction that those are
strictly at zero temperature.
DIANA: So technically to be quantum phase transitions,
they have to happen at absolute zero,
the coldest theoretical temperature in the universe.
But we can't actually get anything down
to that temperature besides in simulations.
And Ehsan's group does use simulations in their work,
but even their simulations incorporate some thermal
or heat energy.
You generate a large number of system configurations.
And by analyzing them, you can infer where a phase transition
might be taking place.
DIANA: The phase transitions that Ehsan's group studies
are magnetic phase transitions.
They simulate a number of electrons
and then see what happens with different concentrations
of electrons and what the magnetic fields
of the electrons do.
These systems act differently just
below a specific critical temperature, the phase
transition temperature.
Above that temperature, they see no order in the system.
And below it, they get kind of a messy checkerboard pattern.
But it's hard to see that transition.
Something goes on in these configurations
that are not possible to be seen by naked eye.
But what we realized was that we could
design an artificial neural network that
can distinguish these configurations at very
high temperatures.
There is no order in my system.
From of course the other type of configurations
at temperatures below the critical temperature
where I have an order.
So this artificial neural network can look at these,
and you can train it by showing it
lots and lots of these images.
So, in conclusion, if we can figure out
what's going on with the individual particles
and transitions to superconductors,
we could potentially make a room temperature superconductor.
That's a huge field of research right now.
Because if we made a room temperature superconductor,
well, any electronic components like your laptop use
or your phone, neither of them would get hot
because there would be no resistance.
It's a superconductor.
So as the electricity goes through,
none of the electronic components would heat up.
That would be amazing!
Probably far off in the distant future.
But it is incredibly exciting physics.
All right.
I hope you learned something fun in this video.
I know that I did.
I have a few announcements.
Please stay for them.
We made another fun video over on Jabril's channel
where I competed with this AI that he wrote.
Go check that out.
I'll put the link in the description.
And huge announcement!
After four years of being on YouTube,
I finally graduated to YouTuber with merch.
I have a t-shirt!
I designed it over many, many months of procrastination
with my animator Kyle Norby.
And I love it so much.
But I don't own it yet because it's still in preorders.
But I'm super excited for it, so I'll put the link
to buy that in the description.
And keep an eye out for it when I wear it in my videos.
Let me know if you like it.
But not if you don't like it.
And one last announcement.
As you know, my channel is part of the PBS Digital Studios
network.
And they have a bunch of new shows, one of which
is the "Origin Of Everything."
They've got videos on the origin of blue for boys
and pink for girls and on, where did grades in school come from?
They've got great videos.
Check them out.
I'll put a link to their channel in the description.
Or Google them.
I don't know.
You guys are good at that.
All right, that's it.
Thank you so much for watching, and happy physicsing.
[MUSIC PLAYING]