The memory capacity of any quantum neural network is at most the information content of its trainable parameters, so classically-parameterized QNNs lack capacity advantage over classical NNs.
Information content of queries in training Parameterized Quantum Circuits
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abstract
Parameterized quantum circuits (PQC, aka, variational quantum circuits) are among the proposals for a computational advantage over classical computation of near-term (not fault tolerant) digital quantum computers. PQCs have to be "trained" -- i.e., the expectation value function has to be maximized over the space of parameters. This paper deals with the number of samples (or "runs" of the quantum computer) which are required to train the PQC, and approaches it from an information theoretic viewpoint. The main take-away is a disparity in the large amount of information contained in a single exact evaluation of the expectation value, vs the exponentially small amount contained in the random sample obtained from a single run of the quantum circuit.
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quant-ph 1years
2019 1verdicts
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The Capacity of Quantum Neural Networks
The memory capacity of any quantum neural network is at most the information content of its trainable parameters, so classically-parameterized QNNs lack capacity advantage over classical NNs.