Hi, welcome to another cold fusion video.
In the last video, complete with its own quantum upload,
we talked a bit about how quantum computers work and
their implications for the world.
But some of you either didn't quite grasp it or
wanted a deeper, more literal explanation, and I agree.
So I thought I'd make this video to bring you
up to speed.
You are watching Cold Fusion TV.
The following information is from my book New Thinking.
I talked to some people in my university and had
someone who actually worked with quantum computers read over it
to make sure that it was all technically correct.
So from the last video, we know that quantum computers
can solve problems that would take infinitely long on a
classical computer.
Things such as modelling the brain and modelling weather systems,
etc.
But let's take a deeper look at how they work.
We covered that regular computers process data as binary bits,
represented either as A1 or A0A.
Quantum computers equivalent data is known as quantum bits or
qubits, which can exist in both zero and one states
at once, allowing multiple computations to happen simultaneously.
These states in a particle are called spin, but there's
really no need to know what that is for the
purpose of this video.
This simultaneous state of both zero and one is like
a kind of soup of probabilities called a superposition.
This makes it possible to store and manipulate vast amounts
of information with a relatively small amount of particles.
So what is a qubit made from?
While a normal computer's bits are made from tiny transistors,
a qubit can be anything that exhibits quantum behaviour, an
electron, an atom, or even a molecule.
That is, if the environment is right.
More on this later.
So I just want to solidify why the superposition aspect
is so important.
As I said in the last video, our world and
reality itself is quantum in nature.
Real world quantum systems can't be modelled on a classical
computer without making poor approximations for the two simple molecules
in this example.
Here a classical computer is off by a factor of
70 to 200%.
Think about it.
Modelling such a simple compound as calcium monofluoride makes our
best supercomputers completely useless.
Our current machines are so inaccurate because the electrons in
the real world that are orbiting around these atoms are
themselves in superposition.
These electrons exist in multiple states at once, and we
can't model that properly with classical computers.
Calculating all the possibilities is just too much for them.
Quantum computers, on the other hand, can leverage the fact
that they themselves also operate using superposition.
This kind of modelling, which is impossible today, will become
routine because of the sheer amount of data that quantum
computers can hold.
Richard Feynman, who was instrumental in the very formation of
quantum physics, first proposed this concept.
To get that exponential processing speed up, the qubits must
be able to communicate.
The fate of all qubits are linked together in a
process called quantum entanglement.
Entanglement is just a relationship between two superposition particles.
The state of one determines the state of the other.
So basically, all this means that in quantum computers, entanglement
along with superposition helps the system store all possible solutions
at once.
So what are quantum computers good at?
As it turns out, as far as we know, quantum
computers are good at things that have a small input
and output while having a vast array of possibilities.
Let's take a look at a few examples to solidify
this idea.
So for modelling complex molecules, the input is the number
of particles in their starting state, While the output is
what happens after a given time.
The possibilities of what happens in between that time are
almost infinite.
Another example, breaking encryption.
In 1994, American mathematician Peter Shaw of Bell Labs found
a theoretical way to use quantum computers to break codes
that relied on the factorization of large numbers into primes.
This doesn't sound like much at first, but many online
security systems, from banking to encryption, rely on the principle
that it's currently almost impossible to take a very large
number and figure out what the prime factors are.
The best algorithm known for classical computers uses a number
of steps that keeps increasing exponentially until it takes billions
of years.
Using the algorithm by Peter Shaw, called Shaw's Algorithm, a
quantum computer could perform the task in just a number
of hours.
In this case, the input is a single large number,
and the output is how many sets of primary numbers
multiply together to give that large number.
Again, the possibilities here are almost infinite.
One last but simpler example, finding the longest distance between
two cities in Japan.
The input is the map data, Rd.
distances, etcetera.
It's large, but nothing that a classical computer couldn't handle.
The output is just one number, the longest distance and
the possibilities.
Well, it seems like a simple problem, but if you
think about it, there's an almost infinite number of routes
you can take.
In fact, this very class of problem is forever unsolvable
using classical computers.
This class of problem is mathematically called an NP complete
problem.
There are many other real world problems of this type,
from weather patterns and climate change to boosting artificial intelligence
training from looking at patterns and stock market data, or
in recordings of brain activity, not to mention gene analysis
and drug delivery, as well as many other things.
Unlike with today's computers, finding these patterns could become routine,
requiring no detailed understanding of the subject problem.
And this is what makes quantum computers so interesting.
So this all sounds good, but there are still some
hurdles.
The first problem is due to quantum physics itself, an
observer can never directly know all the vast number of
qubit states at the same time.
All an observer knows is the probability of what state
the qubits will be.
In the very act of observing or measuring the overall
state of the quantum computers, qubits will force the system
to decide on which state they're in.
So instead of the quadrillions of answers, we can only
see one.
And that's another quirk of the quantum world.
So how do we make this useful?
Well, the answers that come out of quantum computers are
in the form of a probability.
If you repeat the question, the answer will change slightly
and the quantum computer stay will begin to approach the
theoretical percentage or correct answer the more times you repeat.
You might ask the quantum computer several times, but getting
a good answer on the second or third attempt may
still be much faster than waiting for a certain answer
on a classical computer.
So this next part is pretty crazy.
In order for a quantum system to push towards the
right answer when asked repeatedly, the code must be designed
so that the qubits are likely to be in the
correct state for a given problem, hence give us the
right answer.
Fascinatingly, the quantum code is designed to use the wave
like properties found in particle physics to cancel out the
wrong answers and amplify the correct answer.
The answer can then be detected and viewed.
The manipulation of nature through code is called a quantum
algorithm.
Mathematicians and scientists around the world are on a race
to build these algorithms for when capable quantum computers arrive.
So with all of this said, why don't we have
powerful quantum computers now?
It's mostly because of the second problem with building them,
the practicality of maintaining a quantum environment.
In my book, I refer to qubits as very shy
Divas.
To be in their quantum superposition state, that is, being
in multiple states at once, they need to be comfortable.
Cubits must be free from all radiation and kept at
a temperature just above that of absolute 0.
If the particle interacts with anything, the quantum effects are
scared away.
Any slight disturbances such as light particles, radiation, or even
quantum vibrations can snap the particles out of their superposition
state.
This voids the entire advantage of the machine.
Right now we can only achieve a quantum superposition for
a tiny fraction of a second, not long enough to
carry out a useful algorithm.
Mastering this fragile quantum state still remains one of the
biggest challenges for engineers and scientists to build a practical
quantum computer.
So that brings us to the next and final point.
How do you actually build these things?
Our current quantum computer designs consists of two main types
for construction, the superconductor type and the spin type, that
is the use of small particles.
The most popular spin method uses single electrons within silicon
to create qubits.
The reason for this is that the infrastructure for nanoscale
silicon components is already established, so it makes economic sense
to just leverage what we already have for a new
kind of computing.
These electrons within the silicon are called quantum dots.
Only several nanometers in size, they can operate at slightly
higher temperatures, making them more stable.
Research on this method only started to pick up in
2017.
The older, more established qubit uses a metallic superconductor to
create a quantum state.
Superconductor qubits defy the misconception that the strange quantum effects
are impossible for larger objects.
This is not strictly true.
The way we get large objects to produce quantum states
is by letting the material reach that fragile Deaver state.
This is done by subjecting them to a super cold
vacuum environment.
The result is a cubit that's visible to the naked
eye.
The most popular method is a super cold metallic ring
almost at absolute zero.
At these temperatures, electrons can flow around forever without ever
bumping into anything.
Strange as still all these electrons flow clockwise, but at
the same time all the electrons also flow counterclockwise.
If the clockwise flow is red as a one and
the counterclockwise flow is red as a zero, it's essentially
a superposition qubit.
The superconductor method is how D wave, IBM and Google
quantum computers work.
So to round out, let's take a look at a
few achievements.
So right now the best quantum computer we have is
using the quantum dot method, though most of those qubits
are used for error detection.
On the road to building a quantum computer, MIT scientists
have reported the discovery of a new triple photon form
of light.
An Australian team has set the record for the most
accurate 1 cubic gate in a silicon quantum dot.
They had a remarkable accuracy of 99.96%.
And also many other companies are joining in the quantum
race, from startups all the way to established names like
Honeywell.
IBM also wants a commercial quantum computer in as little
as three years.
So where does this all leave us?
So as concluded in the last video, quantum computers hold
great promise, but it's still going to be multiple decades
before we have a meaningful machine.
And I'd say that when the first breakthroughs start to
happen, we'll enter a hype cycle where the media and
everyone picks it up thinking that quantum computers are around
the corner, but they're just not ready yet.
And expectations will probably follow before the real breakthroughs actually
start to happen in a couple of decades.
Hopefully this video answered a lot of your questions from
the last video.
If you do want to know more about this and
other technology all the way from the industrial revolution to
quantum computers and AI, be sure to check out my
book.
The audiobook version is also available in the description below.
So that wraps up the video.
Thanks for watching.
It might take a few listens to process all of
this information, but once you do, it becomes clear just
how amazing this topic of quantum computers is.
It's one of the most fascinating topics I've ever come
across.
Anyway, this has been to Go Go and you've been
watching Cold Fusion and I'll catch you again soon for
the next video.
Cheers guys, Have a good one Cold Fusion.
It's new thinking.