Introduction to Quantum Classifiers

Main Components

The main data structure describing the quantum classifier setting is in qclassifier.py. The implementation allows for modular design of the following components of a quantum classifier (Figure 1):

  1. Encoder: transforms a classical data vector into a quantum state. See encoder.py.

    • 1.1 Classical preprocessor: maps an input data vector to circuit parameters. See preprocessing.py.
    • 1.2 Quantum state preparation: applies the parametrized circuit to an all-zero input state to generate a quantum state encoding the input data. See encoding_circ.py.

  2. Processor: extracts classical information from the encoded quantum state. See processor.py.

    • 2.1 Quantum state transformation: applies a parametrized circuit to the encoded quantum state to transform it into a form more amenable for information extraction by measurement and classical postprocessing. See proc_circ.py.
    • 2.2 Information extraction: extract classical information from the output quantum state. See postprocessing.py.
      • 2.2.1 Measurement: repeatedly run the quantum circuit, perform measurements and collect measurement statistics
      • 2.2.2 Classical postprocessing: Glean information from the measurement statistics and produce the output label of the quantum classifier.

Examples

We provide a Jupyter notebook to demonstrate the utility of QClassify.

Notebook Feature(s)
qclassify_demo.ipynb Uses a simple two-qubit circuit to learn the XOR dataset.

How to cite QClassify

When using QClassify for research projects, please cite:

Sukin Sim, Yudong Cao, Jonathan Romero, Peter D. Johnson and Alán Aspuru-Guzik. A framework for algorithm deployment on cloud-based quantum computers. arXiv:1810.10576. 2018.