mlsanityData to 14 Oct 2026

Quantum computing overview: discrete vs. continuous variable models

arXiv 2206.07246 ↗First posted 15 Jun 2022quant-ph cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 3 of its 40 references that are in the map. First posted on arXiv 15 Jun 2022.

Builds on

3 of 40 references.

References · 3

  1. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  2. 2021Supervised quantum machine learning models are kernel methodscited by 200
  3. 2018Continuous-variable quantum neural networkscited by 74

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variablescited by 0
  2. 2025Continuous-Variable Quantum Encoding Techniques: A Comparative Study of Embedding Techniques and Their Impact on Machine Learning Performancecited by 0
  3. 2025Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representationscited by 0