Quantum Principal Component Analysis

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Quantum machine learning (2016, unknown): “As quantum physics offers certain enhancements in the storage and processing of information, a clear research track is to develop and tailor these quantum methods to apply to problems when facing ‘big data’ sets [14, 15, 18–20, 48, 49, 54–59].”
Quantum machine learning: a classical perspective (2017, methodology): “Q : Õ(s2κ 2 log (N)/ ) [57]b Q :O(log (N) −3)[58] .”
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits (2021, methodology): “The variational method has shown huge potentials in applications such as quantum machine learning [4], [7], [56], [63], numerical analysis [37], [39], quantum simulation [30], [31], [52], and optimizations [18], [47].”
Quantum error mitigation (2022, background): “Lostaglio and Ciani (2021) also discuss applying QEM to noisy logical T gates but with more emphasis on the resource theoretic aspect.”
Training deep quantum neural networks (2019, background): “(iii) A final possibility is to use a modified version of a trick from [8].”
Dual Principal Component Pursuit (2015, methodology): “…techniques in data analysis, with ubiquitous applications in engineering (Moore, 1981), economics and sociology (Vyas and Kumaranayake, 2006), chemistry (Ku et al., 1995), physics (Loyd et al., 2014) and genetics (Price et al., 2006) to name a few; see Jolliffe (2002) for more applications.”