The reported 40 to 60 percent encoding-time reduction from three quantum-inspired relabeling strategies is contradicted by the paper's own measurements for several embedding types.
Quantum computing overview: discrete vs. continuous variable models
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abstract
In this Near Intermediate-Scale Quantum era, there are two types of near-term quantum devices available on cloud: superconducting quantum processing units (QPUs) based on the discrete variable model and linear optics (photonics) QPUs based on the continuous variable (CV) model. Quantum computation in the discrete variable model is performed in a finite dimensional quantum state space and the CV model in an infinite dimensional space. In implementing quantum algorithms, the CV model offers more quantum gates that are not available in the discrete variable model. CV-based photonic quantum computers provide additional flexibility of controlling the length of the output vectors of quantum circuits, using different methods of measurement and the notion of cutoff dimension.
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Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations
The reported 40 to 60 percent encoding-time reduction from three quantum-inspired relabeling strategies is contradicted by the paper's own measurements for several embedding types.