Why We Need Quantum Computers
It’s Not Just Because They’re Cool.
Here’s the short answer:
- The math and science department of the world have a wall in front of them called ‘really hard problems’ (think: curing cancer, energy-efficient batteries [or energy efficient anything for that matter], and material design).
- Human brains are smol. So we created computers to calculate things faster. (Computers are a representation of how humans think, just faster.)
- Even the most powerful supercomputer don’t have enough computational power to break down the wall called ‘really hard problems’.
- Quantum Computers ‘think’/operate differently. They can save the day!
Here’s the long answer:
- read the article
(includes why we can’t break down previously mentioned wall)
(good read. do continue.)
Einstein’s kinda smart.
The icon of theoretical physics came up with the theory of relativity (a pillar of modern physics), created a weapon that would end the greatest war of all time, and fostered the most famous equation.
It would kinda take something big to baffle the master of atoms.
Or something really small.
Like…smaller than an atom.
Einstein might have been the master of atoms, but he was thoroughly confuzzled when it came to understanding quantum particles (sub-atomic particles).
Luckily, I can help with that.
Physics as we know and see it, is defined by Newtonian and Einsteinian laws. They are the observable traits that the atoms around us seem to portray. These laws represent how we physically experience the world, and have been defined by mathematical means that are logical in our human mind.
But when we pay a visit to particles that sit within an atom, we come across observations that are so strange, they go completely against our natural intuition.
These sub-atomic particles play within the realm of quantum mechanics.
Back in the 1920s, Einstein and Niels Bohr made headlines with their constant dispute over the legitimacy of quantum mechanics. While Einstein believed that a physical reality exists independent of our ability to observe it, Bohr and his followers believed it made no sense speculating about an ‘ultimate reality’ that exists beyond our perceptions — that all we can, and should know are the results of observations and measurements.
Modern scientists are way past debating the legitimacy of quantum mechanics, and are onto using computers to exploit their properties.
While they (hopefully) won’t create another fashionable line of death-machines, quantum computers have even governments looking pretty interested. Sorry, Bohr.
But understanding quantum mechanics to use for computations, does more for us than tingle our curious minds (like it did for Einstein).
Quantum computers have the potential to break barriers holding back innovations in math, medicine, and material design.
Understanding the high-level basics of quantum mechanics isn’t actually too difficult to understand, and to really get into how quantum computers can save your grandmother, you should read this:
Back already? What a smarty pants. Let’s get started.
Humans love to give credit to our cognitive abilities, and constantly boast about the technologically advanced world we live in. Each piece of technology is faster and smaller than ever before, and has helped us achieve a greater standard of living that could not have been imagined even 25 years ago.
This is all rainbows and unicorns until we realize — there are so many problems scientists grapple with that trickle into our everyday lives — that haven’t been solved…and we simply can’t keep developing computers any faster or smaller to solve them.
Why can’t we just throw on another couple of years of scientists and nerds doing their thing and wait for things like personalized medicine to come to us?
Yea sure maybe that’ll work — except it won’t.
There are 2 main issues:
- How computers work
- Computers are really small
Bits on bits on bits
Conventional computers are limited to doing one thing at a time.
This is because fundamentally, digital computers only receive and process information in a linear, orderly fashion — through bits that only read 0s and 1s. (As you may now know through my article).
When encountering a problem that necessitates our computers to consider many parameters and situations — it can’t do so efficiently. To get to a result, a digital computer must work through every single possibility before coming to a conclusion.
Therefore,
The more complex the problem, the longer it takes to solve…
The process of using classical bits to consider multiple configurations can take even the greatest supercomputer on earth years — and if we’re trying to keep innovating well into my young adult years…something needs to change.
Reason numéro deux:
Our technology physically won’t let us.
We’re at a point in our lives when Moore’s law is no longer relevant.
We can understand how classical computers have reached their computational limit by understanding transistors.
Computers are basically a (very complex) electronic circuit made up of connecting wires and a bunch of switches that can be switched ON or OFF. These switches are controlled electronically (duh, it’s called an electronic circuit), and are called transistors.
By placing transistors on a circuit, we’re stopping and starting the flow of the electric current.
You can also say transistors place a condition on the circuit, deciding whether or not the electric current can flow.
So now your circuit has the ability to decide the flow of current, based on these conditions.
So basically,
more transistors = more conditions = more gates= more complicated computations
👍 Got it? Nice.
Pile on a bunch of gates on a circuit and you basically have your laptop you’re probably reading this article on.
Time (and a bunch of nerds) allowed us to find new manufacturing techniques to create smaller and smaller transistors — and to fit them on smaller spaces (like circuit boards).
As transistors got smaller, the space needed to place got smaller, so much so that we started calling them ‘chips’.
Companies like Intel are mass-producing transistors that are just 14 nanometers across. That’s just 14 times wider than your DNA molecules. That’s crazy.
We’ve made transistors so small, that we can now fit 4.3 billion of them on a chip as small as a penny (if those things existed anymore).
With this many transistors, we’ve created a crap ton of logic gates that slave away to do highly complex computations for us. Sometimes, we use this highly complex computational power for not-so-complex or intellectual reasons — like scrolling through Reddit.
Anyways.
Transistors are made of silicon.
Silicon’s atomic size is about 0.2 nanometers, making our transistors about 70 silicon atoms wide. That makes the possibility of making them even smaller — smaller than ever.
At a scale this small, we start to deal with some abnormalities. Particles, like electrons, start behaving under the laws of Quantum Physics (yay for you! you know what this means!).
Therefore, the limits of computational power are pretty directly correlated to the limit of how small we can make our transistors.
So…our current computers can’t solve the problems we want them to, and we can’t even make computers any more powerful or complex to accommodate.
So what the hell?
Here’s the hell:
Scientists are making computers that use the properties of quantum physics for computations. Meaning, our computational process will be able to operate on an exponential, rather than linear, plane.
What Quantum Computers Exist For
Quantum computers play with particles in the Quantum Realm…
Makes sense.
Because quantum computers can simulate states simultaneously, it can consider multiple configurations at once — and process an extraordinary amount of information.
In fact, it can store exponentially more information than a classical bit.
The ‘exponential’ power lies in a quantum computer’s capacity to double the states the system can simultaneously store — as you add a single qubit.
Two qubits can store four states, three qubits can store eight states, four qubits can store 16 states…you get the idea.
For a situation where you’d need 50 entangled qubits to model quantum states, you would need to encode 1.125 quadrillion classical bits to store the same amount of information.
Why Do I Care Tho.
Throughout this article, I’ve done this really messy thing called ‘being really vague’, and haven’t actually defined any problems that classical computers can’t solve (and quantum computers can).
I think it’s time to fix this.
Consider a real-life situation: molecular simulation.
Ok, ok. Stay with me here. I know this seems like something only researchers and high school textbooks have to deal with…but realize this: molecules make up all materials around us.
That leaves abundant room for applications of molecular simulation.
Let’s take a problem that affects everybody (pun intended) on this planet: medicine.
As of right now, it takes at least 12 years for a drug to travel from the laboratory, into your medicine cabinet. (If it even makes it that far.)
That’s a lifespan for some, and a luxury for others that don’t have 12 years waiting for a drug that might not even restore their health.
12 years and at least $648 million (to 2.7 BILLION) dollars for the slight chance you can be cured of a disease you probably don’t want.
The slight chance might be worth it for you, but I’m interested in opening that opportunity gap.
Models of Chemicals
The way we try to create life-saving drugs today, is by using chemical modellers.
These models for chemicals continuously attempt the creation of compounds by being forced to approximate how an unknown molecule might behave, then testing it in the real world to see if it works as expected.
This constant back-and-forth process is both time-consuming and resource-intensive (hello, 12 years and a few million dollars).
It also doesn’t really work.
Molecular simulation is all about finding a compound’s ground state — its most stable configuration. With all that cross-over practice in chem class, this can sound easy enough. But in order to really know a molecule’s ground state, you have to consider more than balancing a skeleton equation:
- how each electron in each atom will interact with all of the other atoms’ nuclei
- the quantum effects that occur on such small scales
These parameters become exponentially harder to deal with as the size of the molecule increases.
Our binary brains and computers can’t pump out an optimal configuration in a reasonably useful time.
There’s even a word for it: polynomial time. Poly-time is the time it takes for a classical computer to solve a problem.
We use the idea of ‘being able to compute a problem within poly-time’, because yes — classical computers can actually simulate molecules — it’ll just take them an atrocious amount of time to do so.
Even the most powerful supercomputers today (whomst are quite super) very quickly struggle with simulating a molecule with three or more elements.
Keeping track of the exponential nature and quantum interactions of each new electron in a molecule’s bonds is extremely labour intensive for a classical computer (and current chemical modellers).
But for a quantum computer dealing with quantum interactions? Sounds just about reasonable.
So there.
Classical computers aren’t the big bois they’re hyped up to be. And quantum computers are cool.
A lot of people assume the rising of quantum computers will mean you’ll have a 16-qubit computer sitting on your lap in about a decade or so. This is pretty much (f̵a̵k̵e̵ ̵n̵e̵w̵s̵ ) false because classical computers are great…they’re just not-so-capable in some aspects. Some important aspects. Aspects that hold us back from making cool things. Like molecules. And then personalized medicine. And energy efficient batteries. And maybe efficient architecture.
You get the point.
Because of the way conventional computers inherently operate, they can’t compute certain configurations. And because of this article, you now know why. 😃
Contrary to what Mr. Bohr believed, it actually is important to understand how something as unobservable as electron movement occurs.
Molecules make up the material around us, and if we want to optimize how we use our resources…we can’t keep doing what hasn’t been working — using classical computers to simulate materials for innovation. We need to invest in efficiency.
I know I haven’t actually explained how Quantum Computers will actually go about doing so — but trust me, that’s coming. I just didn’t want to bore you guys with a mathematical explanation some of you might not be here for.
