Continuous-variable quantum kernel method on a programmable photonic quantum processor
arXiv:2405.01086 · doi:10.1103/PhysRevA.110.022404
Abstract
Among various quantum machine learning (QML) algorithms, the quantum kernel method has especially attracted attention due to its compatibility with noisy intermediate-scale quantum devices and its potential to achieve quantum advantage. This method performs classification and regression by nonlinearly mapping data into quantum states in a higher dimensional Hilbert space. Thus far, the quantum kernel method has been implemented only on qubit-based systems, but continuous-variable (CV) systems can potentially offer superior computational power by utilizing its infinite-dimensional Hilbert space. Here, we demonstrate the implementation of the classification task with the CV quantum kernel method on a programmable photonic quantum processor. We experimentally prove that the CV quantum kernel method successfully classifies several datasets robustly even under the experimental imperfections, with high accuracies comparable to the classical kernel. This demonstration sheds light on the utility of CV quantum systems for QML and should stimulate further study in other CV QML algorithms.
8 pages and 4 figures
References in corpus (23)
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum Machine Learning
- Supervised learning with quantum enhanced feature spaces
- Quantum computational advantage using photons
- Quantum machine learning in feature Hilbert spaces
- Quantum Circuit Learning
- Data re-uploading for a universal quantum classifier
- A rigorous and robust quantum speed-up in supervised machine learning
- Squeezing as an irreducible resource
- Efficient Classical Simulation of Continuous Variable Quantum Information Processes
- Continuous-variable quantum neural networks
- Quantum generalisation of feedforward neural networks
- Generation of one-million-mode continuous-variable cluster state by unlimited time-domain multiplexing
- Quantum machine learning over infinite dimensions
- Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
- One-hundred step measurement-based quantum computation multiplexed in the time domain with 25 MHz clock frequency
- Quantum machine learning of large datasets using randomized measurements
- Experimental quantum kernel machine learning with nuclear spins in a solid
- Experimental realization of a dynamic squeezing gate
- Quantum kernels with squeezed-state encoding for machine learning
- Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavour Tagging
- Continuous-variable quantum approximate optimization on a programmable photonic quantum processor