Classification of P300 datasets from MOABB using Quantum MDM

The mean and the distance in MDM algorithm are formulated as optimization problems. These optimization problems are translated to Qiskit using Docplex and additional glue code. These optimizations are enabled when we use cpm mean or cpm distance. This is set using the ‘metric’ parameter of the QuantumMDMWithRiemannianPipeline.

Classification can be run either on emulation or real quantum computer.

If you want to use GPU, you need to use qiskit-aer-gpu that will replace qiskit-aer. It is only available on Linux.

pip install qiskit-aer-gpu

pip install moabb==0.5.0

# Author: Anton Andreev
# Modified from plot_classify_EEG_tangentspace.py of pyRiemann
# License: BSD (3-clause)

# import warnings

import seaborn as sns
from matplotlib import pyplot as plt
from moabb import set_log_level
from moabb.datasets import BNCI2014009
from moabb.evaluations import WithinSessionEvaluation
from moabb.paradigms import P300

from pyriemann_qiskit.pipelines import (
    QuantumMDMVotingClassifier,
    QuantumMDMWithRiemannianPipeline,
)

# inject cpm distance and mean to pyriemann (if not done already)
from pyriemann_qiskit.utils import distance, mean  # noqa

print(__doc__)

getting rid of the warnings about the future warnings.simplefilter(action=”ignore”, category=FutureWarning) warnings.simplefilter(action=”ignore”, category=RuntimeWarning)

# warnings.filterwarnings("ignore")

set_log_level("info")

Initialization

1. Create paradigm

2. Load datasets

paradigm = P300()

# Datasets:
# name, electrodes, subjects
# bi2013a       16  24 (normal)
# bi2014a           16      64 (usually low performance)
# BNCI2014009       16      10 (usually high performance)
# BNCI2014008        8       8
# BNCI2015003        8      10
# bi2015a        32  43
# bi2015b        32  44

datasets = [BNCI2014009()]

# reduce the number of subjects, the Quantum pipeline takes a lot of time
# if executed on the entire dataset
n_subjects = 2
title = "Datasets: "
for dataset in datasets:
    title = title + " " + dataset.code
    dataset.subject_list = dataset.subject_list[0:n_subjects]

# Change this to true to test the quantum optimizer
quantum = False

We have to do this because the classes are called ‘Target’ and ‘NonTarget’ but the evaluation function uses a LabelEncoder, transforming them to 0 and 1

labels_dict = {"Target": 1, "NonTarget": 0}

Create Pipelines

Pipelines must be a dict of sklearn pipeline transformer.

pipelines = {}

pipelines["mean=qlogeuclid/distance=logeuclid"] = QuantumMDMWithRiemannianPipeline(
    metric="mean", quantum=quantum
)

pipelines["mean=logeuclid/distance=qlogeuclid"] = QuantumMDMWithRiemannianPipeline(
    metric="distance", quantum=quantum
)

pipelines["Voting qlogeuclid"] = QuantumMDMVotingClassifier(quantum=quantum)

Run evaluation

Compare the pipeline using a within session evaluation.

evaluation = WithinSessionEvaluation(
    paradigm=paradigm,
    datasets=datasets,
    overwrite=True,
)

results = evaluation.process(pipelines)

print("Averaging the session performance:")
print(results.groupby("pipeline").mean("score")[["score", "time"]])


# ##############################################################################
# # Plot Results
# # ----------------
# #
# # Here we plot the results to compare two pipelines

fig, ax = plt.subplots(facecolor="white", figsize=[8, 4])

sns.stripplot(
    data=results,
    y="score",
    x="pipeline",
    ax=ax,
    jitter=True,
    alpha=0.5,
    zorder=1,
    palette="Set1",
)
sns.pointplot(data=results, y="score", x="pipeline", ax=ax, palette="Set1").set(
    title=title
)

ax.set_ylabel("ROC AUC")
ax.set_ylim(0.3, 1)

plt.show()