Introduces a class-margin metric connecting data-encoding randomness to quantum classification accuracy, and argues that near-random encodings fundamentally limit performance.
Input Redundancy for Parameterized Quantum Circuits
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abstract
The topic area of this paper parameterized quantum circuits (quantum neural networks) which are trained to estimate a given function, specifically the type of circuits proposed by Mitarai et al. (Phys. Rev. A, 2018). The input is encoded into amplitudes of states of qubits. The no-cloning principle of quantum mechanics suggests that there is an advantage in redundantly encoding the input value several times. We follow this suggestion and prove lower bounds on the number of redundant copies for two types of input encoding. We draw conclusions for the architecture design of QNNs.
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quant-ph 1years
2024 1verdicts
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The role of data-induced randomness in quantum machine learning classification tasks
Introduces a class-margin metric connecting data-encoding randomness to quantum classification accuracy, and argues that near-random encodings fundamentally limit performance.