Tool Reference

Tool Reference#

Load Datasets with OpenML#

In QXMT, the OpenML API can be utilized via a config file to simplify dataset preparation. This section explains the process from searching for the desired dataset to setting it up in the config file for use.

Let’s assume you want to use the “Fashion-MNIST” dataset for an experiment. First, search for the relevant dataset on OpenML’s dataset search page and navigate to its detail page. On the detail page, you will be able to see information about the dataset, as shown below. From this information, take note of the “ID” and add it to the config (in this case, the ID is “40996”).

Dataset Detail Page

Based on the information from the search, configure the Run settings in the config as shown below. (Only the necessary parts are extracted for clarity)

dataset:
  type: "openml"
  openml:
    name: "Fashion-MNIST"
    id: 40996
    return_format: "numpy"
    save_path: "data/openml/Fashion-MNIST/dataset.npz"

  • type: Specifies the dataset type to be used in QXMT. Set this to openml when using a dataset from OpenML.

  • openml.name: The name of the dataset on OpenML.

  • openml.id: The ID of the dataset on OpenML.

  • openml.return_format: Specifies the format of the dataset. It can be either pandas or numpy.

  • openml.save_path: Specifies the path to save the downloaded dataset. If set to null, the dataset will not be saved.

Both openml.name and openml.id can be used individually. If only openml.name is specified, the dataset will be searched internally via the API. Since openml.id uniquely identifies the dataset, it is recommended to use this value. If both openml.name and openml.id are set, the value of openml.id will take precedence.

Chain Processing of Raw Processing Logic and Transform Logic#

Not limited to the default logic provided by QXMT, users can also apply custom Raw Processing Logic and Transform Logic defined by themselves in a chain processing manner, where multiple processes are sequentially applied. In the configuration, the definition is done by listing each logic in order as a sequence.

In the example below, a process is defined that first applies normalization to the dataset, followed by dimensionality reduction using dimension_reduction_by_pca. There are several ways to represent lists in YAML, but as long as the syntax is permitted by YAML, any method can be used.

transform_logic:
- module_name: qxmt.datasets.transform.normalization
  implement_name: normalization
  params: null
- module_name: qxmt.datasets.transform.reduction_by_pca
  implement_name: dimension_reduction_by_pca
  params:
    n_components: 2

Using Projected Kernel#

In kernel-based machine learning models, such as QSVC, there are various algorithms available for kernel computation. This section explains how to configure the settings when using the Projected Kernel [1].

A simple Projected Kernel is expressed by the following equation, where the scale parameter γ and the method of projecting quantum states into classical states can be specified for distance computation.

Definition of Projected Kernel

Source: Equation (9) from “Power of data in quantum machine learning” [1]

The kernel-related settings are managed collectively under the kernel section in the config file. Here, you can specify the type of kernel to use and its parameters. To use the Projected Kernel, configure the settings in the config file as follows.

kernel:
  module_name: "qxmt.kernels.pennylane"
  implement_name: "ProjectedKernel"
  params:
    gamma: 1.0
    projection: "xyz_sum"

  • module_name: Specifies the name of the module where the kernel method is implemented. In this case, use the one provided by QXMT as indicated above.

  • implement_name: Specifies the class name that implements the kernel method. In this case, use the one provided by QXMT as indicated above.

  • params.gamma: The scale parameter for kernel computation.

  • params.projection: The method for projecting quantum states into classical states (available options are “x”, “y”, “z”, “xyz”, and “xyz_sum”).

[1] Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, and Jarrod R McClean, “Power of data in quantum machine learning,” Nature Communications 12, 1–9 (2021).

Version Information#

Environment

Version

document

2024/10/16

QXMT

v0.3.0