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Input Redundancy for Parameterized Quantum Circuits

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arxiv 1901.11434 v2 pith:Z5UUO4ID submitted 2019-01-31 quant-ph cs.LG

Javier Gil Vidal , Dirk Oliver Theis This is my paper · ORCID
classification quant-phcs.LG
keywords inputquantumcircuitsencodingparameterizedadvantageamplitudesarchitecture
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The role of data-induced randomness in quantum machine learning classification tasks

    quant-ph 2024-11 conditional novelty 5.0 of 10

    Introduces a class-margin metric connecting data-encoding randomness to quantum classification accuracy, and argues that near-random encodings fundamentally limit performance.

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