The Case for Quantum Machine Learning
Discover the fundamental case for quantum machine learning, focusing on how quantum computing can speed up machine learning optimization. Learn about the current limitations posed by small, noisy quantum devices in the NISQ era and how parameterized quantum circuits enable hybrid approaches combining classical and quantum computations for challenging tasks.
Quantum machine learning is the use of quantum computing for the computation of machine learning algorithms.
Classical machine learning algorithm
We’ve learned that machine learning algorithms contain three components: representation, evaluation, and optimization.
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When we look at the representation, current machine learning algorithms such as the GPT-3 network, published in 2020, come to mind. GPT-3 produces human-like text, but it has 175 billion parameters. In comparison, the IBM Q quantum computer has 27 quantum bits only. So even though quantum bits store a lot more information than a classical bit, since it is not 0 or 1, quantum computers are far from advancing machine learning for their representation ability.
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During the evaluation, the machine learning algorithm tries to predict the label of a thing. Classically, this involves measuring and transforming data points. For instance, neural networks rely on matrix multiplications. These are tasks classical computers are good at. However, if we have 175 billion parameters, calculating the resulting prediction takes many matrix multiplications.
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Finally, the algorithm needs to improve the parameters in a meaningful way. The problem is to find a set of parameter values that result in better performance. With 175 billion ...