The Secret Skill You Need To Becoming A Quantum Expert | by Frank Zickert | Quantum Machine Learning | May, 2022
It’s neither math nor physics
The secret ingredient to becoming an expert in quantum computing is not a degree in math or physics. If it were, one of these clever physicists would have figured out how to solve the urgent problems of humanity. But they didn’t yet.
What the industry needs are experts who understand the specificity of an urgent problem while identifying issues that could benefit from quantum speedup.
The biggest misconception is that quantum computers are faster than classical computers. They are not.
All quantum computing does is make it possible to implement non-deterministic algorithms.
But don’t worry. It’s not a bad thing! If quantum computing were a general-purpose tool that solved all the world’s problems, we would be at the mercy of the few mathematicians who understand quantum computing. And the knowledge that is limited to a few people is dangerous. Depriving large parts of the population of knowledge is the death of democracy and the road to autocracy.
But it’s not just good for our society that quantum computing is not a magic tool with mathematicians pulling the strings. It is, on the contrary, your chance to cut a slice of the pie.
You just need to understand what non-deterministic algorithms are and how to identify problems that might benefit from them.
Let’s consider the following problem. You are standing in front of two doors (level 1). You select one and see two doors again (at level 2). This continues until you reach level n. Finally, on the last level n, there is a door behind which there is a prize.
Now, let’s create an algorithm to solve this problem.
Classically, we use deterministic algorithms. Such an algorithm can open a single door in one step. So it opens door by door from step 1 to n and sees if it contains the price. If not, it continues with the next one.
How many steps does it take to solve this problem?
In the best case, it takes n steps. This is the case when we open the doors leading to the door with the price on the first attempt.
However, in the worst case, it takes 2⁰+2¹+…+2^(n-1)+2^n steps. This is the total number of doors. In this unfortunate case, the last door we open contains the prize.
In both cases, the number of steps we need to solve the problem increases as n increases. However, they grow pretty differently. Since the first grows linearly, we say the algorithm has polynomial growth. In contrast, the second one grows exponentially. And you must not underestimate this difference.
Solutions whose number of steps grows polynomially can be solved quickly, even for large n. And even if we don’t have the computational power to solve a problem with a large value of n, we should have it in a few years.
So let us return to the doors. Obviously, the assumption that we will find the price on the first try is naive. This is especially the case for large n, because the more doors we have, the less likely it is that any particular door contains the prize. Nevertheless, in complexity theory, we usually assume the worst case.
But it would be a whole different story if someone told you the right door. Then, whatever door it is, you could check the solution with only n steps.
Let’s consider another algorithm — a non-deterministic one. Such an algorithm can open all doors on a level simultaneously in a single step. In the first step, it opens two doors on level 1. In the second step, it opens four doors on level 2, and so on. Consequently, it opens 2^n doors on level n. Eventually, this algorithm finds the price after n (non-deterministic) steps. Thus, the complexity of solving the problem is polynomial — it is tractable.
Now, all we need is a computer capable of executing non-deterministic algorithms. Such a computer could solve the door problem because the algorithm’s complexity grows only polynomially.
Some clever mathematicians have indeed developed non-deterministic algorithms. Some dealt with mathematical toy problems, like Deutsch and Simon. Others, like Peter Shor, dealt with relevant problems like the factorization of numbers.
But why haven’t they solved humanity’s pressing problems? Or at least problems that are important for companies?
They developed algorithms that solved the problems they knew best. There’s a simple reason. To create a non-deterministic algorithm that solves a problem, you have to know the structure of the problem inside out.
And what do mathematicians know best? Right, mathematical problems.
The simultaneously good and bad news is that these mathematicians do not know much about the problems you face in your daily work. So if you want to solve them, you have to do it yourself.
Thus, to become an expert in quantum computing, you need to gain expertise in a real-world domain. Then, supplement it with the ability to identify problems that could benefit from non-deterministic algorithms.
It’s neither math nor physics
The secret ingredient to becoming an expert in quantum computing is not a degree in math or physics. If it were, one of these clever physicists would have figured out how to solve the urgent problems of humanity. But they didn’t yet.
What the industry needs are experts who understand the specificity of an urgent problem while identifying issues that could benefit from quantum speedup.
The biggest misconception is that quantum computers are faster than classical computers. They are not.
All quantum computing does is make it possible to implement non-deterministic algorithms.
But don’t worry. It’s not a bad thing! If quantum computing were a general-purpose tool that solved all the world’s problems, we would be at the mercy of the few mathematicians who understand quantum computing. And the knowledge that is limited to a few people is dangerous. Depriving large parts of the population of knowledge is the death of democracy and the road to autocracy.
But it’s not just good for our society that quantum computing is not a magic tool with mathematicians pulling the strings. It is, on the contrary, your chance to cut a slice of the pie.
You just need to understand what non-deterministic algorithms are and how to identify problems that might benefit from them.
Let’s consider the following problem. You are standing in front of two doors (level 1). You select one and see two doors again (at level 2). This continues until you reach level n. Finally, on the last level n, there is a door behind which there is a prize.
Now, let’s create an algorithm to solve this problem.
Classically, we use deterministic algorithms. Such an algorithm can open a single door in one step. So it opens door by door from step 1 to n and sees if it contains the price. If not, it continues with the next one.
How many steps does it take to solve this problem?
In the best case, it takes n steps. This is the case when we open the doors leading to the door with the price on the first attempt.
However, in the worst case, it takes 2⁰+2¹+…+2^(n-1)+2^n steps. This is the total number of doors. In this unfortunate case, the last door we open contains the prize.
In both cases, the number of steps we need to solve the problem increases as n increases. However, they grow pretty differently. Since the first grows linearly, we say the algorithm has polynomial growth. In contrast, the second one grows exponentially. And you must not underestimate this difference.
Solutions whose number of steps grows polynomially can be solved quickly, even for large n. And even if we don’t have the computational power to solve a problem with a large value of n, we should have it in a few years.
So let us return to the doors. Obviously, the assumption that we will find the price on the first try is naive. This is especially the case for large n, because the more doors we have, the less likely it is that any particular door contains the prize. Nevertheless, in complexity theory, we usually assume the worst case.
But it would be a whole different story if someone told you the right door. Then, whatever door it is, you could check the solution with only n steps.
Let’s consider another algorithm — a non-deterministic one. Such an algorithm can open all doors on a level simultaneously in a single step. In the first step, it opens two doors on level 1. In the second step, it opens four doors on level 2, and so on. Consequently, it opens 2^n doors on level n. Eventually, this algorithm finds the price after n (non-deterministic) steps. Thus, the complexity of solving the problem is polynomial — it is tractable.
Now, all we need is a computer capable of executing non-deterministic algorithms. Such a computer could solve the door problem because the algorithm’s complexity grows only polynomially.
Some clever mathematicians have indeed developed non-deterministic algorithms. Some dealt with mathematical toy problems, like Deutsch and Simon. Others, like Peter Shor, dealt with relevant problems like the factorization of numbers.
But why haven’t they solved humanity’s pressing problems? Or at least problems that are important for companies?
They developed algorithms that solved the problems they knew best. There’s a simple reason. To create a non-deterministic algorithm that solves a problem, you have to know the structure of the problem inside out.
And what do mathematicians know best? Right, mathematical problems.
The simultaneously good and bad news is that these mathematicians do not know much about the problems you face in your daily work. So if you want to solve them, you have to do it yourself.
Thus, to become an expert in quantum computing, you need to gain expertise in a real-world domain. Then, supplement it with the ability to identify problems that could benefit from non-deterministic algorithms.
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