Quantum computing is a major trend in computer science. It’s jaw-dropping to think that it all started from observing the weird properties of light! There have been several pioneers in quantum computing, the main one being Richard Feynman—he explained that quantum computers are feasible and that they are the future of computing.

Quantum computers have existed since way before you think. The first quantum computation was carried out in 1997, using NMR on chloroform molecules.^[1] Nowadays, we’ve been trying to slap the “quantum” buzzword on just about anything. Even then, there are still a few applications—in the endless list of quantum technologies—that are really mind-boggling.

10 Improving Cancer Treatment

Cancer is one of the leading causes of death around the world. In fact, according to a recent survey from the World Health Organization (WHO), respiratory cancers alone claimed 1.7 million lives in 2016. However, if cancer is recognized at an early stage, the chances of recovery through treatment are much higher. There are many ways cancer can be treated. One is to remove it by surgery; another is through radiotherapy.

Beam optimization is critical in radiotherapy, as it is important to make sure that the radiation damages as little healthy cells and tissues near the cancer region as possible. There have been many optimization methods for radiotherapy in the past that use classical computers. In 2015, researchers at the Roswell Park Cancer Institute came up with a new technique that uses quantum annealing computers, like the ones manufactured by D-Wave, to optimize radiotherapy in a manner that is three to four times faster than that of a regular computer ^[2]

9 Better Traffic Flow

Many of us are familiar with waking up early and setting off for work, only to find a traffic jam waiting on the way. And then comes the terrifying feeling that you’re going to be late for work. Google has been working on fixing this problem by monitoring traffic and suggesting alternative routes to its users. However, Volkswagen is taking it to another level with their research.

In a 2017 experiment, Volkswagen tried to tackle the issue of traffic, not through monitoring but rather by optimizing traffic flow itself. They used the Quadratic Unconstraint Binary Optimization (QUBO) technique with quantum annealing computers to find the optimal route for a select number of cars and possible routes in consideration.^[3]

So far, they have tested this with 10,000 taxis in Beijing to show how their method can optimize traffic flow significantly faster than a classical computer. However, many people are skeptical of Volkswagen’s claims, since they used a D-Wave quantum annealing computer to do the processing. Many scientists state that the quantum annealers D-Wave manufactures do not offer a speedup as significant as Volkswagen claims.

8 Better Mobile Data Coverage

We have all been in a spot where the mobile data reception is excessively bad, and we’d rather just use that slow WiFi hotspot in that nearby coffee shop. Well, it seems that a company called Booz Allen Hamilton might just have found the solution to the horrible network coverage problem, with the help of quantum computers, of course!

In a 2017 publication, they suggested that optimal satellite coverage is pretty tough to figure out. This is because there are a lot of possible alignment combinations, and it is really hard to check all these combinations with classical computers.

The solution? They suggest that using the QUBO technique, as previously mentioned, with the help of D-Wave’s quantum annealing computers, can help find the optimal satellite coverage position required.^[4] This would not mean that the satellites would be able to cover all the bad reception spots, but the likelihood of being able to find a spot with better reception can be increased significantly.

7 Simulate Molecules

Molecule simulation has been a crucial field in biology and chemistry, as it helps us understand the structure of molecules and how they interact with each other. But it also helps us discover new molecules.

Although classical computers nowadays may be able to simulate these molecular dynamics, there is a limitation on the complexity of molecules in a given simulation. Quantum computers are able to effectively break this barrier. So far, they’ve only been used to simulate small molecules, like beryllium hydride (BeH₂), for example. It might not seem like much, but that fact that it was simulated by a seven-qubit chip shows that if we had more qubits at our disposal, we might be able to run extremely complex molecular simulations.^[5] This is because the processing power of quantum computers increases exponentially as the number of qubits increase.

Other hardware—like D-Wave’s quantum annealing computers—has also been used by researchers to come up with simulation methods that might be just as good, if not faster, than current methods.

6 Break Currently Used Cryptosystems Other Than RSA

Some of us might have heard of the scare about quantum computers being able to break cryptosystems such as RSA or DSA. This seems to be true for some cryptosystems, as they rely on prime numbers to generate a key based on prime factors. An algorithm, called Shor’s algorithm, can be used by quantum computers to find the prime factors used to generate the key, and they can do it much more efficiently.

But what about the other cryptosystems which do not rely on prime numbers to generate keys? There is another algorithm called Grover’s algorithm which might be used to brute force a key faster than a classical computer. However, this is not as big of a speedup as Shor’s algorithm would offer, compared to a classical computer (quadratic vs. exponential speedup). This would mean that we would need significantly faster quantum computers than the ones that currently exist to even attempt to break these cryptosystems.

Even with that, there are some cryptosystems that would be impossible for quantum computers to break. These cryptosystems are categorized within the field of “post-quantum cryptography.” Overall, though, it would seem that at least RSA—which is often used in digital signatures—would be obsolete.^[6]

5 More Humanlike AI

Artificial intelligence is an extremely trending field in computer science. Scientists have been trying to make AI more humanlike through the means of machine learning and neural networks. Seems terrifying, but now add quantum computers to the concoction, and it is taken to a whole new level.

Neural networks run on matrix-based data sets, and the processing done in neural networks is computed through the means of matrix algebra. However, quantum computing itself fundamentally works in such a nature that matrices are often used to define and determine the quantum states of qubits.^[7] So with that, any computational process done on the neural network would be similar to using transformational quantum gates on qubits. Hence, quantum computers seem like the perfect fit for neural networks incorporated in AI.

Not only that, but quantum computers can also help to significantly speed up machine learning compared to a classical computer. This is why Google has been investing in quantum computer research to improve Google AI by means of quantum hardware.

4 Quantum Cryptography

This is very different from post-quantum cryptography, as it is not meant to prevent quantum computers from breaking cryptosystems, though it does that, anyway. This type of cryptography uses the means of quantum mechanics itself. But how is it more versatile than other forms of cryptography?

Quantum cryptography mainly focuses on the key distribution part of a cryptosystem, here two pairs of entangled qubits are used. One is sent to the receiver, while the sender keeps the other. Entangled particles in a superposition, when measured, affect the other qubit. Send a stream of these qubits, and you have a key usable for encryption.^[8]

The best part about it is that eavesdropping is impossible, as the qubits cannot be copied. They can’t be measured, either, as there are methods to determine whether the qubit has been tampered with before being received by the intended recipient. This makes it a robust method for cryptography, which is why scientists are still researching this field.

3 Forecasting Weather

We’ve all had that time where we’ve checked the weather forecast, and it said that it was going to be a wonderful, sunny day. Then, only moments later, it starts to pour, and you didn’t bring your umbrella. Well, it seems quantum computers might have a solution for that.

In 2017, a Russian researcher published a paper about the possibility of using quantum computers to predict the weather more accurately than classical computers. There are a few limitations with current computers in predicting all the intricate changes in weather.^[9] This is because large amounts of data are involved, but quantum computers seem to offer a big speedup compared to classical means because of Dynamic Quantum Clustering (DQC) methodology, which is claimed to generate useful datasets that classical techniques cannot.

Even so, it must be noted that not even quantum computers can predict the weather with absolute accuracy, but at least it will be less likely that we will regret not bringing an umbrella on suspicious sunny days!

2 More Efficient Customized Advertisements

We all hate it when we search for an article, only to find it to be littered with advertisements. Most of it doesn’t even seem relevant! Luckily, Recruit Communications has found a solution for one of those two problems—the relevancy of ads.

In their research, they explained how quantum annealing can be used to help companies wanting to advertise to reach a wider range of people without spending too much. The quantum annealing can be used to match relevant advertisements to customers so that they’re more likely to click them.^[10]

1 Gaming With Quantum Computers

With all the speedup quantum computers offer in the computing field, one thing gamers might be curious about is whether they can be used to make a sweet gaming rig which can run games at blazing high framerates. The answer would be, “Sort of.”

At this point, the field of quantum computers is still at its infancy, and current hardware still hasn’t reached “quantum supremacy”—which is when quantum hardware can compute faster than the current best computers, though the definition is still vague. This is because quantum computer algorithms work very differently from classical ones. Even with that, quantum gaming still seems to be possible.

There have been a few games which have been developed to utilize quantum computers. One of them is called Quantum Battleships, which is based on the Battleships board game.^[11] Furthermore, Microsoft has been working on a programming language called Q#, which uses both classical and quantum hardware to compute. It is also very similar to C#, which would mean that it is very possible to develop games using Q# that take advantage of quantum hardware. Maybe we’ll have Call of Duty Q one day!

I am a small music producer from the UK with a newly acquired side hobby for writing articles!

Science fiction has a curious way of being prophetic. We can only hope that stories like The Matrix, Terminator, I, Robot and others missed the mark when they suggested robots and computers are going to rise up against us and either use them as batteries or fertilizer for plants they’ll crush under their robot treads. 

If you’re concerned about the rise of the machines, take heart in the fact that there are still some things humans do better than machines. Even one thing a bird can do better. They may not save our species, but you never know.

10. Veery Birds are Better at Predicting Hurricanes Than Machines

Predicting hurricanes is important business. Currently, we rely on a number of different systems to help determine if a hurricane is on the way, including things like satellites, radar, even ships and buoys in the oceans. We can predict a hurricane about 36 to 48 hours in advance thanks to all of this technology. 

When it comes to long range predictions, we have little better than chance to guide us. We can predict hurricanes will come in hurricane season, but so what? That’s like predicting the sun will rise tomorrow. For greater reliability, we can turn away from computers and turn to the birds. Veeries have an uncanny knack for predicting severe hurricane weather as demonstrated by their breeding seasons. These birds live in Southern Canada and the Northern US and have one clutch of eggs per breeding season. 

In years when hurricane seasons are severe, the veeries cut their breeding seasons short, even if they haven’t been successful yet. They will do this months in advance of hurricane season, but the two have been demonstrably linked. 

In 2018, an ornithologist predicted a particularly heavy hurricane season while meteorological data predicted the exact opposite. Scientists who deal in weather were insisting that it would be a mild year. They used a number, ACE, which stands for accumulated cyclone energy, and predicted anywhere from 60, which is quite low, to 103 at most, which is below average. 

The ornithologist, who had never predicted weather in his life, suggested 70 to as much as 150. It turned out to be 129. He got his prediction from observing the behavior of veeries, based on 20 years of observing their behavior in the wild.

9. Humans Are Superior Gamers But They Suck at Teamwork

The global gaming market is an absolute juggernaut. It’s predicted that by 2025 it will be worth about $257 billion. That’s 93% of an Elon Musk! That kind of money has inspired a lot of amazing innovations in technology, including graphics and artificial intelligence. And computers aren’t just behind the games, sometimes they get out in front.

Artificial intelligence has proven itself to be a better gamer than the average human player, even though this is a fairly recent development.  But that does need to be taken with a grain of salt. In a strict, by-the-numbers style of play, a computer can often accomplish its tasks better than some guy in Idaho who keeps insulting your mother while you play Call of Duty. However, when a game gets collaborative and requires teamwork, artificial intelligence starts to show some flaws. Namely, AI sucks at teamwork.

Human players routinely express frustration when it comes to dealing with AI teammates. Research found that the AI teammate didn’t improve the results of a game over playing with a pre-programmed computer partner that was designed to just know the rules of the game and play in a certain way. But the big difference was that the human partners hated working with the AI. The computer was considered unreliable, unpredictable and untrustworthy, three things you don’t want in a gaming partner. 

8. Computer Translations Tend to be Pretty Sloppy

Have you ever run across a word or phrase in another language and then translated it online? And then did you take the time to translate it back to English and discover it was essentially gibberish? This happens because computers are remarkably bad at translations. Translation programs are notoriously bad at picking up on context and other nuances of language, making computer translators basic at best and useless at worst. 

Things like slang, cultural context, proper names and more are lost on machines. Consider something like the word “set.” According to Guinness, there are over 430 potential meanings for that one word. A machine needs context clues to figure out how that would be used in a given translation, and that’s not an easy task. 

Idioms are generally translated literally by machine, even highly advanced ones. Even single words can alter the tone of whole sentences, and that can be a problem with AI translators. You may get the gist of a work, but that’s not necessarily what you want, especially if you’re reading fiction because you want the story and you’re not just trying to clean basic facts.

7. Pick Fruit 

Machines can build cars and computers and all manner of machines for us these days, but ironically it’s some of the simpler tasks they have trouble with. For instance, they’re not that great at picking strawberries. And many other fruits and vegetables, for that matter.

The reason behind the robotic failure is all too easy to guess. A robot isn’t very good at guessing if it’s being too rough or not. For fruits like strawberries and other berries, a light touch is needed. A robot can probably harvest nuts until the cows come home, but berries need to be handled gently. Robot harvesters have no way of knowing if they’re squeezing too hard and destroying the produce. 

As it happens, harvesting robots are being designed to sidestep this issue by picking whole plants rather than just the berries. They can do the work of 30 people in the same amount of time. But for now, the picking robots have to scan fields and figure out where to go to find the ripe fruit. So far they’re only able to harvest about 50% of the ripe fruit while humans can get up to 90%. 

6. AI Isn’t Good at Reading Emotions

Facial recognition technology is something that has been big in the news for years. People are leery of it because it smacks of a surveillance state and constantly being watched. But another aspect people fear is computers being able to look at you and read you, essentially determining how you feel from one moment to the next. This could be used to exploit people for marketing, advertising, and additional purposes towards the goal of making money. Schools in China used it to determine how children felt while they learned remotely, ostensibly to improve their overall learning experience. 

The thing about emotion detecting computers is that they’re not very good. Despite what people who market the technology insist, there is little evidence that it’s very effective. Neuroscientists have flat out stated that you cannot accurately judge a person’s emotional state based on facial expression. 

5. Humans are Better Soldiers than Robots 

One of the most controversial uses of AI in the world today is related to warfare. Should we entrust machines to make life and death decisions in a war zone? Is it ethical to allow a robot to take a human life? It seems like most people are against the idea and the US has already assured us that humans will always be making the final decision.That said, there is speculation that the ship has sailed and autonomous killing machines have already been used in the field. So are robots better soldiers than humans? That depends on what you mean by better.

A machine, even an artificial intelligence, will do what it’s tasked with doing. Without human emotion and ethics, an AI would have likely made a different decision than Stanislav Petrov did back in 1983, when he got word that the American military had launched a nuclear strike on the Soviet Union. Petrov did not alert his government about the attack that his monitoring station had detected, as he was required to do, and instead investigated further, determining that it was a false alarm. AI likely would have done the opposite and none of us would be here now to discuss it. They lack morality and can be unpredictable in how they process data. 

Everyone from Elon Musk to Stephen Hawking has warned that AI could doom us all. That’s not what a good soldier does at all. 

4. AI Hasn’t Mastered Common Sense Yet

Most of us have met someone in life who is very smart but has no common sense. We distinguish between the two. You can be a math wizard but still act like an idiot. That’s kind of what AI is like. It can be very smart, but it has no common sense. 

Common sense is how we describe abductive inference. This is what lets us ignore a million silly explanations for things that happen in life and focus on the ones that make the most sense. If you hear a noise upstairs, it’s why you might think it’s your spouse or the cat and not an elephant or Gordon Ramsay. Those last two options sound stupid because you have common sense. AI doesn’t, so it needs to consider those as possibilities. 

Current AI relies on symbolic logic and deep learning. These explain a lot, but ignore common sense and are the reasons why AI is not able to come close to duplicating real human intelligence. 

3. AI Writing Programs Haven’t Perfected Human Writing Yet

The future of writing may be overtaken by machines but, for now, they’re still not quite up to snuff. AI is adept at writing prose, especially things like journalistic articles, but it hasn’t fully mastered a human voice yet. 

Computers using something called GPT-3 or Generative Pre-trained Transformer 3 can produce text that very nearly mimics writing from a real human. It’s very good at certain kinds of writing, but not other kinds. If you want it to mimic the speech of a real human, for instance, it would be more likely to generate nonsense. It could write a fact-based article with ease, but if you want it to mimic a Stephen King story that reads like genuine King, it would seem off. The phrasing would be suspect or it would need serious editing. 

The flaw is in how the tech works. It’s based on prediction and pattern matching. So, at large, it can generate general writing well. But when you want specific writing, like Stephen King, it limits the tech’s ability to understand what it’s trying to say. 

2. Fulfilling Factory Orders 

Believe it or not, the one thing most of us would guess a robot would do vastly better than a human just isn’t true. In warehouse settings, like at Amazon, robots are just not as good at fulfilling orders as human workers. 

In 2019, it was suggested it’d be at least a decade before robots usurped the human workforce. Robots can pick items for orders if they’re big, but smaller items in bins tend to get damaged and are not as efficient as when picked by humans. 

Elon Musk admitted that Tesla pushed automation too far as well, and it needed to be scaled back because humans are just better at being flexible and dealing with inconsistencies.

1. Captchas 

If there’s one thing everyone on the internet knows, it’s that robots can’t look at nine squares and pick out the ones with traffic lights. Captcha tests are a website’s last defense against robotic invasion and they make use of numerous layers of data including your screen size and resolution, IP address, browsers, plugins, keystrokes and more to determine you’re you and not a machine. 

If you’ve noticed these tests get harder, and the ones where you have to identify garbled looking text can sometimes even trick you, it’s because robots are actually getting better at the tests, so they have to get harder and harder to beat. In fact, for some tests robots are much better than humans already. But we’re still ahead of the curve for basic programs and, until we develop something better like various game-like tests, or ink blot puzzles which have been attempted, that’ll have to be good enough.