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Quantum computers are super awesome at matrix multiplication, with some limitations. Quantum superposition allows each bit to be in a lot more states than just zero or one, and quantum gates can fiddle those bits in many different ways. Because of that, a quantum computer can process a lot of information at once for certain applications. One of those ...


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Until we can make a quantum computer with a lot more qubits, the potential to further develop AI will remain just that. D-Wave (which has just made a 2,000+ qubit system around 2015) is an adiabatic quantum computer, not a general-purpose quantum computer. It is restricted to certain optimization problems (at which its effectiveness has reportedly been ...


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AlphaFold (version 1 and 2) predicts (so estimates) the 3D shape of the protein from the sequence of amino acids. AlphaFold's performance is measured with the global distance test (GDT), which is a measure of similarity between two protein structures (the prediction and the ground-truth) that ranges from 0 to 100. There is a short video and a longer one (...


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Handwritten digit recognition is a standard benchmark in Machine Learning in the form of the MNIST dataset. For example, scikit-learn, a python package for Machine Learning uses it as a tutorial example. The paper you cite uses this standard task as a proof of concept, to show that their system works.


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Quantum computers can help further develop A.I. algorithms and solve the problems to the extent of our creativity and ability to define the problem. For example breaking cryptography can take seconds, where it can takes thousands of years for standard computers. The same with artificial intelligence, it can predict all the combinations for the given problem ...


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Generally, if one googles "quantum machine learning" or anything similar the general gist of the results is that quantum computing will greatly speed up the learning process of our "classical" machine learning algorithms. This is correct. A lot of machine learning methods involve linear algebra, and it often takes far fewer quantum ...


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I think you are looking for quantum machine learning (QML), which is a relatively new field that sits at the intersection of quantum computing and machine learning. If you are not familiar with quantum computing (QC) and you are interested in QML, I suggest that you follow this course by prof. Umesh Vazirani and read the book Quantum Computing for Computer ...


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No, quantum computers (as understood by mainstream scientists) cannot solve the halting problem. We can already simulate quantum circuits with normal computers; it just takes a really long time when you get a decent number of qubits involved. (Quantum computing provides exponential speedups for some problems.) Therefore, if quantum computers could solve the ...


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It depends a bit on what you mean by 'quantum computer'. The 'conventional' notion is that quantum computation buys a (in some cases, exponential) speedup - it doesn't change what can be computed, just how quickly. In contrast, advocates of hypercomputation claim that quantum effects may make it possible to do infinite computations in finite time. Note, ...


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Preface "Before answering this question, let me preface by stating that the following is simply MY answer as a Machine Learning Researcher and "Hobbyist" Theoretical Physicist, although I have strong feelings that my answer will most certainly be proven as true, I am more than sure that others will have differing opinions as with everything else ...


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The difference is much simpler than you might have anticipated: In the quantum computing community, machine learning algorithms designed to be used on quantum computers as opposed to classical computers, would fall under "quantum machine learning". There's really nothing more to it! There is a short paper published in Nature called "Quantum ...


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Well, this is quite deep question. I would recommend you to have a look on the UK physicist Roger Penrose: https://en.wikipedia.org/wiki/Roger_Penrose He has some books on the theme of consciousness and quantum mechanics. There are however critics of his ideas. One of them is that the quantum effects are too small to direct any considerable influence on the ...


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Digital and Analog The question about analog computing is important. Digital circuitry gained popularity as a replacement for analog circuitry during the four decades between 1975 to 2015 due to three compelling qualities. Greater noise immunity Greater drift immunity (accuracy) No leakage of stored values This quickly led to digital signaling standards, ...


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Welcome to the AI stack Jake. This probably isn't going to be possible. Modern Psudo-random number generators, like Mersenne Twister, are designed not to have any patterns in them, so there's nothing to learn from. You could however, try something like predicting the values of a broken random number generator, like RANDU. These aren't used anymore, ...


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Direct Answer to Your Question:-- The field where quantum computing and A.I. intersect is called quantum machine learning. A.I. is a developing field, with some background (ala McCarthy of LISP fame). Quantum computing is a virgin field that is largely unexplored. A particular type of complexity interacts with another type of complexity to create a very ...


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