If we believe both that quantum computers may be able to do some computations exponentially faster than their classical counterparts and that we live in a quantum world, then our ability to learn from observational data as scientists may fundamentally change what we can do. Here, we will first review some recent results in quantum machine learning that allow us to put these ideas on a rigorous footing. We then show that quantum computers, and more specifically quantum memory, offer us an opportunity to learn from a quantum world with exponentially less data than traditional experiments. Taking an alternative perspective, we then consider the extent to which data can be put on the same footing as precomputation, and what this may mean for future distributed systems that consider quantum data and computation on the similar footings.
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