Quantum computing overview: discrete vs. continuous variable models
This overview helps researchers and practitioners in quantum computing understand trade-offs between near-term device types, but it is incremental as it synthesizes existing knowledge without new results.
The paper compares discrete variable (superconducting) and continuous variable (photonic) quantum computing models, noting that the CV model offers more quantum gates and flexibility in controlling output vectors, measurements, and cutoff dimensions.
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.