If you know anything about quantum computing, you may have heard of Scott Aaronson. Scott is Professor of Computer Science at The University of Texas at Austin, and director of its Quantum Information Center. Prior to The University of Texas at Austin, he taught for nine years in Electrical Engineering and Computer Science at MIT. His research interests center around the capabilities and limits of quantum computers, and computational complexity theory more generally.
I have listened to most of his online interviews. I like to meet him someday or watch him in action when he talks, because I have good reasons to travel to Texas to visit with my grandson. May be I can listen and watch him doing lectures in his classes at UT.
Here is the most accurate explanation of quantum superposition. I challenge anyone to explain superposition better.
While everyone with some knowledge of QC they say that superposition means "both at once," "both 0 and 1 at the same time," while "classical bits can be only one or the other.".
"They go on to say that a quantum computer would achieve its speed by using qubits to try all possible solutions in superposition — that is, at the same time, or in parallel." Scott says.
Then Scott explains his perspective of quantum computing that makes sense and it is profoundly satisfying answer to a strange concept such as superposition -a mind boggling concept -a reality that is hard to explain. I admit when i learned about it, I had to sit down and think about what I just heard.
Here it is:
"What superposition really means is “complex linear combination.” Here, we mean “complex” not in the sense of “complicated” but in the sense of a real plus an imaginary number, while “linear combination” means we add together different multiples of states. So a qubit is a bit that has a complex number called an amplitude attached to the possibility that it’s 0, and a different amplitude attached to the possibility that it’s 1. These amplitudes are closely related to probabilities, in that the further some outcome’s amplitude is from zero, the larger the chance of seeing that outcome; more precisely, the probability equals the distance squared.
But amplitudes are not probabilities. They follow different rules. For example, if some contributions to an amplitude are positive and others are negative, then the contributions can interfere destructively and cancel each other out, so that the amplitude is zero and the corresponding outcome is never observed; likewise, they can interfere constructively and increase the likelihood of a given outcome. The goal in devising an algorithm for a quantum computer is to choreograph a pattern of constructive and destructive interference so that for each wrong answer the contributions to its amplitude cancel each other out, whereas for the right answer the contributions reinforce each other. If, and only if, you can arrange that, you’ll see the right answer with a large probability when you look. The tricky part is to do this without knowing the answer in advance, and faster than you could do it with a classical computer."
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