# Quantum Learning Algorithms Imply Circuit Lower Bounds

Alex Grilo (CNRS and Sorbonne Université)

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We establish the first general connection between the design of quantum algorithms and circuit lower bounds. Specifically, let C be a class of polynomial-size concepts, and suppose that C can be learned under the uniform distribution with error 1/2 - gamma by a time T quantum algorithm. We prove that if gamma^2*T<<2^n/n, then BQE is not included in C, where BQE=BQTIME[2^(O(n))] is an exponential-time analogue of BQP. This result is optimal in both γ and , since it is not hard to learn any class C of functions in (classical) time T=2^n*T (with no error), or in quantum time T=poly(n) with error at most 1/2−Omega(2^(-n/2)) via Fourier sampling. In other words, even a marginal improvement on these generic learning algorithms would lead to major consequences in complexity theory.

Our proof builds on several works in learning theory, pseudorandomness, and computational complexity, and crucially, on a connection between non-trivial classical learning algorithms and circuit lower bounds established by Oliveira and Santhanam (CCC 2017). Extending their approach to quantum learning algorithms turns out to create significant challenges. To achieve that, we show among other results how pseudorandom generators imply learning-to-lower-bound connections in a generic fashion, construct the first conditional pseudorandom generator secure against uniform quantum computations, and extend the local list-decoding algorithm of Impagliazzo, Jaiswal, Kabanets and Wigderson (SICOMP 2010) to quantum circuits via a delicate analysis. We believe that these contributions are of independent interest and might find other applications.

This is a joint work with Srinivasan Arunachalam, Tom Gur, Igor C. Oliveira and Aarthi Sundaram.

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