All Plugins
Plugin |
Type |
Tags |
|---|---|---|
|
distance-aggregator@v0-2-1 |
processing |
distance-calculation preprocessing |
AmazonBraket_LocalSimulator (@v1.0.0) AmazonBraket_LocalSimulator@v1-0-0 |
processing |
braket_local circuit-executor qasm qasm-3 qc-simulator |
|
classical-k-means@v0-1-1 |
processing |
ML classical clustering |
|
classical-k-medoids@v0-1-1 |
processing |
ML classical clustering |
[Clustered Scatter Plot Visualization (@v1.0.0)](#Clustered Scatter Plot Visualization) Clustered Scatter Plot Visualization@v1-0-0 |
visualization |
cluster scatter visualization |
[Confusion Matrix Visualization (@v1.0.0)](#Confusion Matrix Visualization) Confusion Matrix Visualization@v1-0-0 |
visualization |
cluster confusion-matrix visualization |
|
costume-loader@v0-2-1 |
dataloader |
MUSE data-loading |
|
data-creator@v0-2-3 |
dataloader |
data-loading data-synthesizing |
|
deploy-workflow@v0-1-1 |
processing |
bpmn camunda-engine workflow |
Entity loader/filter (@v0.2.1) entity-filter@v0-2-1 |
processing |
filter preprocessing sample |
[Histogram Visualization (@v1.0.1)](#Histogram Visualization) Histogram Visualization@v1-0-1 |
visualization |
histogram non-default visualization |
|
hybrid-autoencoder@v0-2-1 |
processing |
QML feature-engineering preprocessing quantum |
|
low-code-modeler@v0-0-0 |
interaction |
low-code-modeler |
|
muse-for-music-loader@v1-0-0 |
dataloader |
MUSE4Music data-loading |
Manual Classification (@v0.2.1) manual-classification@v0-2-1 |
processing |
data-annotation manual preprocessing |
|
max_cut@v0-1-1 |
processing |
QML clustering quantum |
Multidimensional Scaling (MDS) (@v0.2.1) mds@v0-2-1 |
processing |
distance-calculation embedding feature-engineering preprocessing |
|
one-hot-encoding@v0-2-1 |
processing |
encoding one-hot-encoding preprocessing |
|
optics@v0-1-1 |
processing |
ML classical clustering |
|
optimizer@v1-0-1 |
processing |
optimization |
Pandas Preprocessing (@v0.1.1) pandas-preprocessing@v0-1-1 |
processing |
data-cleaning preprocessing |
Principle Component Analysis (PCA) (@v0.2.2) pca@v0-2-2 |
processing |
feature-engineering preprocessing |
|
qiskit-executor@v0-1-1 |
processing |
circuit-executor qasm qasm-2 qasm-3 qc-executor qiskit |
Qiskit Quantum Kernel Estimation (@v0.2.1) qiskit-quantum-kernel-estimation@v0-2-1 |
processing |
QML kernel mapping quantum |
|
quantum-cnn@v0-1-1 |
processing |
QML classification neural-network quantum |
Quantum Kernel Estimation (@v0.2.1) quantum-kernel-estimation@v0-2-1 |
processing |
QML kernel mapping quantum |
Quantum Neutral Network (QNN) (@v0.1.1) qnn@v0-1-1 |
processing |
QML classical classification neural-network quantum |
Quantum Parzen Window (@v0.2.1) quantum-parzen-window@v0-2-1 |
processing |
QML classification quantum supervised-learning |
Quantum Variational Classifier (@v0.1.1) vqc@v0-1-1 |
processing |
QML classification quantum |
Quantum k Nearest Neighbours (@v0.2.1) quantum-k-nearest-neighbours@v0-2-1 |
processing |
QML clustering quantum supervised-learning |
|
quantum-k-means@v0-2-1 |
processing |
QML clustering quantum |
|
rest-connector@v0-1-0 |
interaction |
rest |
|
sql-loader@v0-1-1 |
processing |
data-loading mariadb mysql postgresql sql |
|
svm@v0-1-1 |
processing |
ML QML classical classification quantum supervised-learning |
Scipy Minimizer Plugin (@v1.0.0) scipy-minimizer@v1-0-0 |
processing |
minimizer optimization |
Scipy Minimizer with Gradient Plugin (@v1.0.0) scipy-minimizer-grad@v1-0-0 |
processing |
gradient minimizer optimization |
Similarities to distances transformers (@v0.2.1) sim-to-dist-transformers@v0-2-1 |
processing |
distance-calculation preprocessing similarity-calculation |
Sym Max Mean attribute comparer (@v0.1.2) sym-max-mean@v0-1-2 |
processing |
preprocessing similarity-calculation |
Time tanh similarities (@v0.2.1) time-tanh@v0-2-1 |
processing |
preprocessing similarity-calculation |
|
workflow-editor@v0-1-0 |
interaction |
camunda quantme workflow |
|
workflow-management@v0-1-1 |
interaction |
bpmn camunda-engine workflow |
Wu Palmer similarities (@v0.2.1) wu-palmer@v0-2-1 |
processing |
preprocessing similarity-calculation |
[ZX-Calculus Visualization (@v1.0.1)](#ZX-Calculus Visualization) ZX-Calculus Visualization@v1-0-1 |
visualization |
circuit non-default visualization zxcalculus |
|
zip-merger@v0-2-0 |
processing |
utility |
|
circuit-demo@v1-0-1 |
processing |
circuit-demo demo quantum |
|
cirq-simulator@v1-0-0 |
processing |
circuit-executor cirq qasm qasm-2 qc-simulator |
|
csv-visualization@v0-1-1 |
visualization |
csv visualization |
|
data-join@v1-0-0 |
processing |
join preprocessing |
|
file-upload@v0-2-0 |
dataloader |
data-loading |
|
hello-world@v0-2-1 |
processing |
demo hello-world |
hello-world-multi-step (@v0.2.1) hello-world-multi-step@v0-2-1 |
processing |
demo hello-world multistep |
|
hinge-loss@v1-0-0 |
processing |
objective-function optimization |
|
json-visualization@v0-2-1 |
visualization |
json visualization |
|
mqt-simulator@v1-0-1 |
processing |
circuit-executor mqt qasm qasm-2 qasm-3 qc-simulator |
|
neural-network@v1-0-1 |
processing |
ML classical gradient neural-network objective-function optimization |
|
nisq-analyzer@v0-2-0 |
processing |
nisq-analyzer |
|
pennylane-simulator@v1-0-1 |
processing |
circuit-executor pennylane qasm qasm-2 qasm-3 qc-simulator |
pytket_qulacsBackend-simulator (@v1.0.0) pytket_qulacsBackend-simulator@v1-0-0 |
processing |
circuit-executor pytket_qulacsBackend qasm qasm-2 qc-simulator |
|
qasm-visualization@v0-3-1 |
visualization |
qasm visualization |
|
qiskit-simulator@v1-0-1 |
processing |
circuit-executor qasm qasm-2 qasm-3 qc-simulator qiskit |
|
ridge-loss@v1-0-0 |
processing |
objective-function optimization |
Overview
Used tags: ML, MUSE, MUSE4Music, QML, bpmn, braket_local, camunda, camunda-engine, circuit, circuit-demo, circuit-executor, cirq, classical, classification, cluster, clustering, confusion-matrix, csv, data-annotation, data-cleaning, data-loading, data-synthesizing, demo, distance-calculation, embedding, encoding, feature-engineering, filter, gradient, hello-world, histogram, join, json, kernel, low-code-modeler, manual, mapping, mariadb, minimizer, mqt, multistep, mysql, neural-network, nisq-analyzer, non-default, objective-function, one-hot-encoding, optimization, pennylane, postgresql, preprocessing, pytket_qulacsBackend, qasm, qasm-2, qasm-3, qc-executor, qc-simulator, qiskit, quantme, quantum, rest, sample, scatter, similarity-calculation, sql, supervised-learning, utility, visualization, workflow, zxcalculus
Input formats: application/X-lines+json, application/csv, application/json, application/zip, text/csv, text/x-qasm
Output formats: */*, application/csv, application/json, application/qasm, application/zip, image/svg+xml, text/csv, text/html, text/plain, text/x-qasm
Input datatypes: */*, custom/attribute-distances, custom/attribute-similarities, custom/element-similarities, custom/entity-distances, entity/*, entity/attribute-metadata, entity/label, entity/list, entity/matrix, entity/shaped_vector, entity/vector, executable/circuit, graph/taxonomy, provenance/execution-options
Output datatypes: */*, circuit/*, custom/attribute-distances, custom/attribute-similarities, custom/clusters, custom/element-similarities, custom/entity-distances, custom/hello-world-output, custom/kernel-matrix, custom/nisq-analyzer-result, custom/pca-metadata, custom/plot, entity/*, entity/attribute-metadata, entity/label, entity/list, entity/vector, executable/circuit, graph/taxonomy, image/html, plot/*, provenance/execution-options, provenance/trace, qnn-weights/*, representative-circuit/*, table/html, txt/*, vqc-metadata/*
Plugins
Aggregators (@v0.2.1)
processing – distance-calculation, preprocessing
Path: stable_plugins/classical_ml/data_preparation/aggregators.py
Aggregates attribute distances to entity distances.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
custom/attribute-distances |
application/zip |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/entity-distances |
application/zip |
✓ |
AmazonBraket_LocalSimulator (@v1.0.0)
processing – braket_local, circuit-executor, qasm, qasm-3, qc-simulator
Path: plugins/circuit_executors/braket_local.py
Allows execution of quantum circuits using a simulator packaged with braket_local.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
executable/circuit |
text/x-qasm |
✓ |
provenance/execution-options |
text/csv, application/json, application/X-lines+json |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
entity/vector |
application/json |
╳ |
provenance/trace |
application/json |
✓ |
provenance/execution-options |
application/json |
✓ |
Classical k Means (@v0.1.1)
processing – ML, classical, clustering
Path: stable_plugins/classical_ml/scikit_ml/classical_k_means/__init__.py
Clusters data with classical k means algorithm.
The entity points should be saved in the entity/vector format and they may be stored in either a csv or a json file. The data-creator plugin can generate some entity points.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json, text/csv |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/label |
application/json |
✓ |
plot/* |
text/html |
╳ |
Classical k Medoids (@v0.1.1)
processing – ML, classical, clustering
Path: stable_plugins/classical_ml/scikit_ml/classical_k_medoids/__init__.py
Clusters data with classical k medoids algorithm.
The entity points should be saved in the entity/vector format and they may be stored in either a csv or a json file. The data-creator plugin can generate some entity points.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
text/csv, application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/label |
application/json |
✓ |
plot/* |
text/html |
╳ |
Clustered Scatter Plot Visualization (@v1.0.0)
visualization – cluster, scatter, visualization
Path: stable_plugins/visualization/complex/cluster_scatter_visualization/__init__.py
A visualization plugin that creates a scatter plot using the provided data. When an Entity Point URL is provided, a simple scatter plot will be created.To include clustering, provide an appropriate Cluster URL. All plots are interactive and created using plotly. Supports 2D and 3D data visualization.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json, application/csv |
✓ |
entity/label |
application/json, application/csv |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
image/html |
text/html |
✓ |
Confusion Matrix Visualization (@v1.0.0)
visualization – cluster, confusion-matrix, visualization
Path: stable_plugins/visualization/complex/confusion_matrix/__init__.py
A visualization plugin that creates a confusion matrix using the provided data. Accepts two cluster URLs as inputs and outputs an HTML table showing the matrix. If desired the matrix can be optimized, trying to maximize the amount of true positives, by reordering the columns. The new column order will be shown at the bottom.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/label |
application/json |
✓ |
entity/label |
application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
table/html |
text/html |
✓ |
Costume loader (@v0.2.1)
dataloader – MUSE, data-loading
Path: stable_plugins/muse/costume_loader_pkg/__init__.py
Loads all the costumes or base elements from the MUSE database.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/list |
application/json |
✓ |
entity/attribute-metadata |
application/json |
✓ |
graph/taxonomy |
application/zip |
✓ |
Data Creation (@v0.2.3)
dataloader – data-loading, data-synthesizing
Path: stable_plugins/data_synthesis/data_creator/__init__.py
A plugin to create datasets.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
entity/label |
application/json |
✓ |
entity/vector |
application/json |
✓ |
entity/label |
application/json |
✓ |
Deploy Workflow (@v0.1.1)
processing – bpmn, camunda-engine, workflow
Path: stable_plugins/workflow/workflows/__init__.py
Deploys a BPMN workflow to Camunda and exposes it as a plugin.
Entity loader/filter (@v0.2.1)
processing – filter, preprocessing, sample
Path: stable_plugins/classical_ml/data_preparation/entity_filter.py
Loads and filters entities from a file that contains a list of entities.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/list |
application/json, text/csv |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/list |
application/json, text/csv |
✓ |
Histogram Visualization (@v1.0.1)
visualization – histogram, non-default, visualization
Path: stable_plugins/visualization/complex/histogram_visualization/__init__.py
A visualization plugin for creating Historgrams using the counts of different labels.The labels are shown on the x Axis and the counts on the y Axis.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
image/html |
text/html |
✓ |
Hybrid Autoencoder (@v0.2.1)
processing – QML, feature-engineering, preprocessing, quantum
Path: stable_plugins/quantum_ml/pennylane_qiskit_ml/hybrid_ae_pkg/__init__.py
Reduces the dimensionality of a given dataset with a combination of classical and quantum neural networks.
The entity points should be saved in the entity/vector format and they may be stored in either a csv or a json file. The data-creator plugin can generate some entity points.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json, text/csv |
✓ |
entity/vector |
application/json, text/csv |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
entity/vector |
application/json |
✓ |
qnn-weights/* |
application/json |
✓ |
LCM (@v0.0.0)
interaction – low-code-modeler
Path: plugins/low_code_modeler/plugin.py
low code modeler plugin
MUSE4Music Loader (@v1.0.0)
dataloader – MUSE4Music, data-loading
Path: stable_plugins/muse/muse_for_music/__init__.py
Load data from a MUSE4Music instance.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/list |
application/json |
✓ |
entity/attribute-metadata |
application/json |
✓ |
graph/taxonomy |
application/zip |
✓ |
Manual Classification (@v0.2.1)
processing – data-annotation, manual, preprocessing
Path: stable_plugins/muse/manual_classification/__init__.py
Manually annotate classes for data sets from MUSE database.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/list |
application/json, text/csv |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/list |
application/json, text/csv |
✓ |
Max Cut (@v0.1.1)
processing – QML, clustering, quantum
Path: stable_plugins/quantum_ml/max_cut/max_cut/__init__.py
Clusters data with the max cut algorithm
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/matrix |
application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/label |
application/json |
✓ |
plot/* |
text/html |
╳ |
Multidimensional Scaling (MDS) (@v0.2.1)
processing – distance-calculation, embedding, feature-engineering, preprocessing
Path: stable_plugins/classical_ml/scikit_ml/mds.py
Converts distance values (distance matrix) to points in a space.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
custom/entity-distances |
application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
One-Hot Encoding (@v0.2.1)
processing – encoding, one-hot-encoding, preprocessing
Path: stable_plugins/classical_ml/data_preparation/one-hot_encoding.py
Converts Data to One-Hot Encodings
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/list |
application/json |
✓ |
graph/taxonomy |
application/zip |
✓ |
entity/attribute-metadata |
application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/csv |
✓ |
Optics (@v0.1.1)
processing – ML, classical, clustering
Path: stable_plugins/classical_ml/scikit_ml/optics-clustering/__init__.py
Clusters data with the OPTICS algorithm. The plugin uses the implementation by scikit-learn v1.1. More information about the algorithm can be found here.
The entity points should be saved in the entity/vector format and they may be stored in either a csv or a json file. The data-creator plugin can generate some entity points.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/label |
application/json |
✓ |
plot/* |
text/html |
╳ |
Optimizer plugin (@v1.0.1)
processing – optimization
Path: plugins/optimization_coordinator/__init__.py
This plugin provides an API to optimize data given an user selected objective-function and a minimization algorithm.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
txt/* |
text/plain |
✓ |
txt/* |
text/plain |
✓ |
Pandas Preprocessing (@v0.1.1)
processing – data-cleaning, preprocessing
Path: stable_plugins/classical_ml/data_preparation/pandas_preprocessing/__init__.py
Uses pandas preprocessing methods, to preprocess csv files.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
/ |
text/csv, application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
/ |
text/csv |
✓ |
Principle Component Analysis (PCA) (@v0.2.2)
processing – feature-engineering, preprocessing
Path: stable_plugins/classical_ml/scikit_ml/pca/__init__.py
The PCA Plugin reduces the number of dimensions by computing the principle components. The new orthonormal basis consists of the k first principle components. The methods implemented here are from scikit-learn. Currently this plugin uses scikit-learn version 1.1.
The entity points should be saved in the entity/vector format and they may be stored in either a csv or a json file. The data-creator plugin can generate some entity points.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
text/csv, application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/plot |
text/html |
╳ |
custom/pca-metadata |
application/json |
✓ |
entity/vector |
text/csv |
✓ |
Qiskit Executor (@v0.1.1)
processing – circuit-executor, qasm, qasm-2, qasm-3, qc-executor, qiskit
Path: plugins/qiskit_executor/__init__.py
Allows execution of quantum circuits using IBM Quantum backends.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
executable/circuit |
text/x-qasm |
✓ |
provenance/execution-options |
text/csv, application/json, application/X-lines+json |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
provenance/trace |
application/json |
✓ |
provenance/execution-options |
application/json |
✓ |
Qiskit Quantum Kernel Estimation (@v0.2.1)
processing – QML, kernel, mapping, quantum
Path: stable_plugins/quantum_ml/qiskit_ml/qiskit_quantum_kernel_estimation/__init__.py
Produces a kernel matrix from a quantum kernel. Specifically qiskit’s feature maps are used, combined with qiskit_machine_learning.kernels.QuantumKernel. These feature maps are ZFeatureMap, ZZFeatureMap, PauliFeatureMap from qiskit.circuit.library. These feature maps all use the proposed kernel by Havlíček [0]. The following versions were used qiskit~=0.43 and qiskit-machine-learning~=0.4.0.
The entity points should be saved in the entity/vector format. They may be stored in either a csv or a json file. The plugin data-creator can generate these entities.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json, text/csv |
✓ |
entity/vector |
application/json, text/csv |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/kernel-matrix |
application/json |
✓ |
Quantum CNN (@v0.1.1)
processing – QML, classification, neural-network, quantum
Path: stable_plugins/quantum_ml/pennylane_qiskit_ml/qcnn/__init__.py
Labels data with the help of a quantum convolutional neural network
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/shaped_vector |
application/json, text/csv |
✓ |
entity/label |
application/json, text/csv |
✓ |
entity/shaped_vector |
application/json, text/csv |
✓ |
entity/label |
application/json, text/csv |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/label |
application/json |
✓ |
plot/* |
text/html |
✓ |
plot/* |
text/html |
✓ |
qnn-weights/* |
application/json |
✓ |
Quantum Kernel Estimation (@v0.2.1)
processing – QML, kernel, mapping, quantum
Path: stable_plugins/quantum_ml/pennylane_qiskit_ml/quantum_kernel_estimation/__init__.py
This plugin produces the matrix of a quantum kernel. Since this depends on the expected values of the quantum circuit, we can only estimate it and therefore call it Quantum Kernel Estimation. The Plugin implements the kernels by Havlíček et al [0] and Suzuki et al [1].
The entity points should be saved in the entity/vector format. They may be stored in either a csv or a json file. The plugin data-creator can generate these entities.
Source: [0] Havlíček, V., Córcoles, A.D., Temme, K. et al. Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019). [1] Suzuki, Y., Yano, H., Gao, Q. et al. Analysis and synthesis of feature map for kernel-based quantum classifier. Quantum Mach. Intell. 2, 9 (2020).
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json, text/csv |
✓ |
entity/vector |
application/json, text/csv |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/kernel-matrix |
application/json |
✓ |
Quantum Neutral Network (QNN) (@v0.1.1)
processing – QML, classical, classification, neural-network, quantum
Path: stable_plugins/quantum_ml/pennylane_qiskit_ml/qnn/__init__.py
Simple QNN with variable number of variational quantum layers
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json, text/csv |
✓ |
entity/label |
application/json, text/csv |
✓ |
entity/vector |
application/json, text/csv |
✓ |
entity/label |
application/json, text/csv |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/label |
application/json |
✓ |
plot/* |
text/html |
╳ |
plot/* |
text/html |
╳ |
qnn-weights/* |
application/json |
✓ |
representative-circuit/* |
application/qasm |
╳ |
Quantum Parzen Window (@v0.2.1)
processing – QML, classification, quantum, supervised-learning
Path: stable_plugins/quantum_ml/pennylane_qiskit_ml/quantum_parzen_window/__init__.py
This plugin implements a quantum parzen window. A parzen window labels an unlabeled data point via a majority vote of all labeled points that are at most a certain distance away from the unlabeled point.The Plugin implements the algorithm by Ruan et al. [0].
The entity points should be saved in the entity/vector format and labels in the entity/label format. Both may be stored in either a csv or a json file. Both can be generated with the data-creator plugin.
Source:
[0] Ruan, Y., Xue, X., Liu, H. et al. Quantum Algorithm for K-Nearest Neighbors Classification Based on the Metric of Hamming Distance. Int J Theor Phys 56, 3496–3507 (2017).
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json, text/csv |
✓ |
entity/label |
application/json, text/csv |
✓ |
entity/vector |
application/json, text/csv |
✓ |
entity/label |
application/json, text/csv |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/label |
application/json |
✓ |
plot/* |
text/html |
╳ |
plot/* |
text/html |
╳ |
representative-circuit/* |
application/qasm |
✓ |
Quantum Variational Classifier (@v0.1.1)
processing – QML, classification, quantum
Path: stable_plugins/quantum_ml/qiskit_ml/variational_quantum_classifier/__init__.py
This plugin implements the Variational Quantum Classifier (VQC) by Qiskit [0]. It’s currently using version 0.4.0 of qiskit’s machine learning library.
The entity points should be saved in the entity/vector format and labels in the entity/label format. Both may be stored in either a csv or a json file. Both can be generated with the data-creator plugin.
Source: [0] Qiskit documentation, Variational Quantum Classifier [1] Havlíček, V., Córcoles, A.D., Temme, K. et al. Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019).
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json, text/csv |
✓ |
entity/label |
application/json, text/csv |
✓ |
entity/vector |
application/json, text/csv |
✓ |
entity/label |
application/json, text/csv |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/label |
application/json |
✓ |
plot/* |
text/html |
╳ |
plot/* |
text/html |
╳ |
representative-circuit/* |
application/qasm |
✓ |
vqc-metadata/* |
application/json |
✓ |
Quantum k Nearest Neighbours (@v0.2.1)
processing – QML, clustering, quantum, supervised-learning
Path: stable_plugins/quantum_ml/pennylane_qiskit_ml/quantum_k_nearest_neighbours/__init__.py
This plugin implements quantum k nearest neighbours algorithms. Given a set of already labeled data and an integer k, a new data point is labeled by a majority vote of the k nearest training points.
The entity points should be saved in the entity/vector format and labels in the entity/label format. Both may be stored in either a csv or a json file. Both can be generated with the data-creator plugin.
Source: [0] Schuld, M., Sinayskiy, I., Petruccione, F. (2014). Quantum Computing for Pattern Classification. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. [1] Basheer, Afrad and Afham, A. and Goyal, Sandeep K. (2020). Quantum k-nearest neighbors algorithm. In arXiv. [2] Ruan, Y., Xue, X., Liu, H. et al. Quantum Algorithm for K-Nearest Neighbors Classification Based on the Metric of Hamming Distance. Int J Theor Phys 56, 3496–3507 (2017).
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json, text/csv |
✓ |
entity/label |
application/json, text/csv |
✓ |
entity/vector |
application/json, text/csv |
✓ |
entity/label |
application/json, text/csv |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/label |
application/json |
✓ |
plot/* |
text/html |
╳ |
plot/* |
text/html |
╳ |
representative-circuit/* |
application/qasm |
✓ |
Quantum k-means (@v0.2.1)
processing – QML, clustering, quantum
Path: stable_plugins/quantum_ml/pennylane_qiskit_ml/quantum_k_means/__init__.py
This plugin groups the data into different clusters, with the help of quantum algorithms. Currently there are four implemented algorithms. Destructive interference and negative rotation are from [0], positive correlation is from [1] and state preparation is from a previous colleague. The entity points should be saved in the entity/vector format in either a csv or a json file. Source: [0] S. Khan and A. Awan and G. Vall-Llosera. K-Means Clustering on Noisy Intermediate Scale Quantum Computers.arXiv. [1] https://towardsdatascience.com/quantum-machine-learning-distance-estimation-for-k-means-clustering-26bccfbfcc76
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
application/json, text/csv |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/clusters |
application/json |
✓ |
REST API Connector (@v0.1.0)
interaction – rest
Path: plugins/rest_connector/plugin.py
Integrate REST APIs as plugins.
SQL Loader (@v0.1.1)
processing – data-loading, mariadb, mysql, postgresql, sql
Path: plugins/sql_loader/__init__.py
Allows to manage sql databases and use them as a source.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/list |
application/json |
✓ |
SVM (@v0.1.1)
processing – ML, QML, classical, classification, quantum, supervised-learning
Path: stable_plugins/quantum_ml/qiskit_ml/svm/__init__.py
Classifies data with a support vector machine. This plugin uses the implementation of scikit-learn 1.1 [0]. The quantum kernels are from Qiskit [1] and the data maps are from Havlíček et al. [2] and Suzuki et al. [3].
The entity points should be saved in the entity/vector format and labels in the entity/label format. A precomputed kernel matrix should be stored in the entity/matrix format. All of them may be stored in either a csv or a json file. A set of entity points and labels can be generated with the data-creator plugin. A precomputed kernel can be computed with a quantum kernel estimation plugin, given the entity points.
Source: [0] https://scikit-learn.org/1.1/modules/svm.html#svm [1] Qiskit’s quantum kernels ZFeatureMap, ZZFeatureMap and PauliFeatureMap [2] Havlíček, V., Córcoles, A.D., Temme, K. et al. Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019). [3] Suzuki, Y., Yano, H., Gao, Q. et al. Analysis and synthesis of feature map for kernel-based quantum classifier. Quantum Mach. Intell. 2, 9 (2020).
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/vector |
text/csv, application/json |
╳ |
entity/label |
text/csv, application/json |
✓ |
entity/vector |
text/csv, application/json |
╳ |
entity/label |
text/csv, application/json |
╳ |
entity/matrix |
text/csv, application/json |
╳ |
entity/matrix |
text/csv, application/json |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/label |
application/json |
✓ |
plot/* |
text/html |
╳ |
plot/* |
text/html |
╳ |
entity/vector |
application/json |
✓ |
Scipy Minimizer Plugin (@v1.0.0)
processing – minimizer, optimization
Path: plugins/scipy_minimizer/__init__.py
This plugin provides an API to minimize a given objective function with scipy.optimize.minimize().
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
txt/* |
text/plain |
✓ |
txt/* |
text/plain |
✓ |
Scipy Minimizer with Gradient Plugin (@v1.0.0)
processing – gradient, minimizer, optimization
Path: plugins/scipy_minimizer_grad/__init__.py
This plugin provides an API to minimize a given objective function with scipy.optimize.minimize() with gradient support.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
txt/* |
text/plain |
✓ |
txt/* |
text/plain |
✓ |
Similarities to distances transformers (@v0.2.1)
processing – distance-calculation, preprocessing, similarity-calculation
Path: stable_plugins/classical_ml/data_preparation/transformers.py
Transforms similarities to distances.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
custom/attribute-similarities |
application/zip |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/attribute-distances |
application/zip |
✓ |
Sym Max Mean attribute comparer (@v0.1.2)
processing – preprocessing, similarity-calculation
Path: stable_plugins/classical_ml/data_preparation/sym_max_mean.py
Compares attributes and returns similarity values.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/list |
application/json, text/csv |
✓ |
custom/element-similarities |
application/zip |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/attribute-similarities |
application/zip |
✓ |
Time tanh similarities (@v0.2.1)
processing – preprocessing, similarity-calculation
Path: stable_plugins/classical_ml/data_preparation/time_tanh.py
Compares elements and returns similarity values.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/list |
application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/element-similarities |
application/zip |
✓ |
Workflow Editor (@v0.1.0)
interaction – camunda, quantme, workflow
Path: stable_plugins/workflow/workflow_editor/plugin.py
Edit BPMN workflows with an online editor.
Workflow Management (@v0.1.1)
interaction – bpmn, camunda-engine, workflow
Path: stable_plugins/workflow/workflows/management.py
Manage workflows deployed in Camunda.
Wu Palmer similarities (@v0.2.1)
processing – preprocessing, similarity-calculation
Path: stable_plugins/classical_ml/data_preparation/wu_palmer.py
Compares elements and returns similarity values.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/list |
application/json |
✓ |
entity/attribute-metadata |
application/json |
✓ |
graph/taxonomy |
application/zip |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/element-similarities |
application/zip |
✓ |
ZX-Calculus Visualization (@v1.0.1)
visualization – circuit, non-default, visualization, zxcalculus
Path: stable_plugins/visualization/complex/zxcalculus/__init__.py
A visualization plugin that visualizes a provided OpenQASM circuit in the ZX-Calculus. When a QASM Circuit URL is provided, a circuit in the ZX-Calculus will be created. When the Optimize Checkbox is checked, an additional circuit is generated. This circuit is optimized using the automatic optimization method provided by the pyzx package, and will be displayed below the original circuit
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
executable/circuit |
text/x-qasm |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
circuit/* |
text/html |
✓ |
Zip merger (@v0.2.0)
processing – utility
Path: stable_plugins/file_utils/zip_merger.py
Merges two zip files into one zip file.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
/ |
application/zip |
✓ |
/ |
application/zip |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
/ |
application/zip |
✓ |
circuit-demo (@v1.0.1)
processing – circuit-demo, demo, quantum
Path: stable_plugins/demo/circuit-demo.py
A demo plugin implementing circuits for the bell states and executing them using a circuit executor.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
executable/circuit |
text/x-qasm |
✓ |
cirq-simulator (@v1.0.0)
processing – circuit-executor, cirq, qasm, qasm-2, qc-simulator
Path: plugins/circuit_executors/cirq_simulator/__init__.py
Allows execution of quantum circuits using a simulator packaged with cirq.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
executable/circuit |
text/x-qasm |
✓ |
provenance/execution-options |
text/csv, application/json, application/X-lines+json |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
entity/vector |
application/json |
╳ |
provenance/trace |
application/json |
✓ |
provenance/execution-options |
application/json |
✓ |
csv-visualization (@v0.1.1)
visualization – csv, visualization
Path: stable_plugins/visualization/file_types/csv_visualization.py
A demo CSV visualization plugin.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
/ |
text/csv |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
/ |
text/html |
✓ |
data-join (@v1.0.0)
processing – join, preprocessing
Path: stable_plugins/classical_ml/data_preparation/data_join/__init__.py
Join data from multiple entity files.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
entity/* |
text/csv, application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/* |
text/csv, application/json |
✓ |
file-upload (@v0.2.0)
dataloader – data-loading
Path: stable_plugins/file_utils/file_upload.py
Uploads files to use in the workflow.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
/ |
/ |
✓ |
hello-world (@v0.2.1)
processing – demo, hello-world
Path: stable_plugins/demo/hello_world.py
Tests the connection of all components by printing some text.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/hello-world-output |
text/plain |
✓ |
hello-world-multi-step (@v0.2.1)
processing – demo, hello-world, multistep
Path: stable_plugins/demo/hello_worl_multi_step/__init__.py
Tests the connection of all components by printing some text. Also tests the ability to execute multi-step plugins.
hinge-loss (@v1.0.0)
processing – objective-function, optimization
Path: plugins/hinge_loss/__init__.py
Hinge Loss objective-function plugin.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
txt/* |
text/plain |
✓ |
json-visualization (@v0.2.1)
visualization – json, visualization
Path: stable_plugins/visualization/file_types/json_visualization.py
Visualizes JSON data.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
/ |
application/json |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/hello-world-output |
text/html |
✓ |
mqt-simulator (@v1.0.1)
processing – circuit-executor, mqt, qasm, qasm-2, qasm-3, qc-simulator
Path: plugins/circuit_executors/mqt_simulator.py
Allows execution of quantum circuits using a simulator packaged with mqt.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
executable/circuit |
text/x-qasm |
✓ |
provenance/execution-options |
text/csv, application/json, application/X-lines+json |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
entity/vector |
application/json |
╳ |
provenance/trace |
application/json |
✓ |
provenance/execution-options |
application/json |
✓ |
neural-network (@v1.0.1)
processing – ML, classical, gradient, neural-network, objective-function, optimization
Path: plugins/neural_network/__init__.py
Neural Network objective-function plugin.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
txt/* |
text/plain |
✓ |
nisq-analyzer (@v0.2.0)
processing – nisq-analyzer
Path: stable_plugins/nisq_analyzer/nisq_analyzer.py
Provides the NISQ Analyzer UI.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
custom/nisq-analyzer-result |
application/json |
✓ |
pennylane-simulator (@v1.0.1)
processing – circuit-executor, pennylane, qasm, qasm-2, qasm-3, qc-simulator
Path: plugins/circuit_executors/pennylane_defaultqubit.py
Allows execution of quantum circuits using a simulator packaged with qiskit.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
executable/circuit |
text/x-qasm |
✓ |
provenance/execution-options |
text/csv, application/json, application/X-lines+json |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
entity/vector |
application/json |
╳ |
provenance/trace |
application/json |
✓ |
provenance/execution-options |
application/json |
✓ |
pytket_qulacsBackend-simulator (@v1.0.0)
processing – circuit-executor, pytket_qulacsBackend, qasm, qasm-2, qc-simulator
Path: plugins/circuit_executors/pytket_Qulacs.py
Allows execution of quantum circuits using a simulator packaged with pytket_qulacsBackend.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
executable/circuit |
text/x-qasm |
✓ |
provenance/execution-options |
text/csv, application/json, application/X-lines+json |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
entity/vector |
application/json |
╳ |
provenance/trace |
application/json |
✓ |
provenance/execution-options |
application/json |
✓ |
qasm-visualization (@v0.3.1)
visualization – qasm, visualization
Path: stable_plugins/visualization/file_types/qasm_visualization.py
Visualizes QASM data.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
/ |
text/x-qasm |
✓ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
/ |
image/svg+xml |
✓ |
qiskit-simulator (@v1.0.1)
processing – circuit-executor, qasm, qasm-2, qasm-3, qc-simulator, qiskit
Path: stable_plugins/quantum_ml/qiskit_ml/qiskit_simulator.py
Allows execution of quantum circuits using a simulator packaged with qiskit.
Inputs:
Data Type |
Content Type |
Required |
|---|---|---|
executable/circuit |
text/x-qasm |
✓ |
provenance/execution-options |
text/csv, application/json, application/X-lines+json |
╳ |
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
entity/vector |
application/json |
✓ |
entity/vector |
application/json |
╳ |
provenance/trace |
application/json |
✓ |
provenance/execution-options |
application/json |
✓ |
ridge-loss (@v1.0.0)
processing – objective-function, optimization
Path: plugins/ridge_loss/__init__.py
Ridge Loss objective-function plugin.
Outputs:
Data Type |
Content Type |
Always |
|---|---|---|
txt/* |
text/plain |
✓ |