# All Plugins :::{list-table} Plugin Overview :header-rows: 1 :width: 100% :widths: 30 10 30 * - Plugin - Type - Tags * - [Aggregators (@v0.2.1)](#distance-aggregator) distance-aggregator@v0-2-1 - processing - distance-calculation preprocessing * - [AmazonBraket_LocalSimulator (@v1.0.0)](#AmazonBraket_LocalSimulator) AmazonBraket_LocalSimulator@v1-0-0 - processing - braket_local circuit-executor qasm qasm-3 qc-simulator * - [Classical k Means (@v0.1.1)](#classical-k-means) classical-k-means@v0-1-1 - processing - ML classical clustering * - [Classical k Medoids (@v0.1.1)](#classical-k-medoids) 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)](#costume-loader) costume-loader@v0-2-1 - dataloader - MUSE data-loading * - [Data Creation (@v0.2.3)](#data-creator) data-creator@v0-2-3 - dataloader - data-loading data-synthesizing * - [Deploy Workflow (@v0.1.1)](#deploy-workflow) deploy-workflow@v0-1-1 - processing - bpmn camunda-engine workflow * - [Entity loader/filter (@v0.2.1)](#entity-filter) 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)](#hybrid-autoencoder) hybrid-autoencoder@v0-2-1 - processing - QML feature-engineering preprocessing quantum * - [LCM (@v0.0.0)](#low-code-modeler) low-code-modeler@v0-0-0 - interaction - low-code-modeler * - [MUSE4Music Loader (@v1.0.0)](#muse-for-music-loader) muse-for-music-loader@v1-0-0 - dataloader - MUSE4Music data-loading * - [Manual Classification (@v0.2.1)](#manual-classification) manual-classification@v0-2-1 - processing - data-annotation manual preprocessing * - [Max Cut (@v0.1.1)](#max_cut) max_cut@v0-1-1 - processing - QML clustering quantum * - [Multidimensional Scaling (MDS) (@v0.2.1)](#mds) mds@v0-2-1 - processing - distance-calculation embedding feature-engineering preprocessing * - [One-Hot Encoding (@v0.2.1)](#one-hot-encoding) one-hot-encoding@v0-2-1 - processing - encoding one-hot-encoding preprocessing * - [Optics (@v0.1.1)](#optics) optics@v0-1-1 - processing - ML classical clustering * - [Optimizer plugin (@v1.0.1)](#optimizer) optimizer@v1-0-1 - processing - optimization * - [Pandas Preprocessing (@v0.1.1)](#pandas-preprocessing) pandas-preprocessing@v0-1-1 - processing - data-cleaning preprocessing * - [Principle Component Analysis (PCA) (@v0.2.2)](#pca) pca@v0-2-2 - processing - feature-engineering preprocessing * - [Qiskit Executor (@v0.1.1)](#qiskit-executor) 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) qiskit-quantum-kernel-estimation@v0-2-1 - processing - QML kernel mapping quantum * - [Quantum CNN (@v0.1.1)](#quantum-cnn@v0-1-1) quantum-cnn@v0-1-1 - processing - QML classification neural-network quantum * - [Quantum Kernel Estimation (@v0.2.1)](#quantum-kernel-estimation) quantum-kernel-estimation@v0-2-1 - processing - QML kernel mapping quantum * - [Quantum Neutral Network (QNN) (@v0.1.1)](#qnn) qnn@v0-1-1 - processing - QML classical classification neural-network quantum * - [Quantum Parzen Window (@v0.2.1)](#quantum-parzen-window) quantum-parzen-window@v0-2-1 - processing - QML classification quantum supervised-learning * - [Quantum Variational Classifier (@v0.1.1)](#vqc) vqc@v0-1-1 - processing - QML classification quantum * - [Quantum k Nearest Neighbours (@v0.2.1)](#quantum-k-nearest-neighbours) quantum-k-nearest-neighbours@v0-2-1 - processing - QML clustering quantum supervised-learning * - [Quantum k-means (@v0.2.1)](#quantum-k-means) quantum-k-means@v0-2-1 - processing - QML clustering quantum * - [REST API Connector (@v0.1.0)](#rest-connector) rest-connector@v0-1-0 - interaction - rest * - [SQL Loader (@v0.1.1)](#sql-loader) sql-loader@v0-1-1 - processing - data-loading mariadb mysql postgresql sql * - [SVM (@v0.1.1)](#svm) svm@v0-1-1 - processing - ML QML classical classification quantum supervised-learning * - [Scipy Minimizer Plugin (@v1.0.0)](#scipy-minimizer) scipy-minimizer@v1-0-0 - processing - minimizer optimization * - [Scipy Minimizer with Gradient Plugin (@v1.0.0)](#scipy-minimizer-grad) scipy-minimizer-grad@v1-0-0 - processing - gradient minimizer optimization * - [Similarities to distances transformers (@v0.2.1)](#sim-to-dist-transformers) sim-to-dist-transformers@v0-2-1 - processing - distance-calculation preprocessing similarity-calculation * - [Sym Max Mean attribute comparer (@v0.1.2)](#sym-max-mean) sym-max-mean@v0-1-2 - processing - preprocessing similarity-calculation * - [Time tanh similarities (@v0.2.1)](#time-tanh) time-tanh@v0-2-1 - processing - preprocessing similarity-calculation * - [Workflow Editor (@v0.1.0)](#workflow-editor) workflow-editor@v0-1-0 - interaction - camunda quantme workflow * - [Workflow Management (@v0.1.1)](#workflow-management) workflow-management@v0-1-1 - interaction - bpmn camunda-engine workflow * - [Wu Palmer similarities (@v0.2.1)](#wu-palmer) 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)](#zip-merger) zip-merger@v0-2-0 - processing - utility * - [circuit-demo (@v1.0.1)](#circuit-demo) circuit-demo@v1-0-1 - processing - circuit-demo demo quantum * - [cirq-simulator (@v1.0.0)](#cirq-simulator) cirq-simulator@v1-0-0 - processing - circuit-executor cirq qasm qasm-2 qc-simulator * - [csv-visualization (@v0.1.1)](#csv-visualization) csv-visualization@v0-1-1 - visualization - csv visualization * - [data-join (@v1.0.0)](#data-join) data-join@v1-0-0 - processing - join preprocessing * - [file-upload (@v0.2.0)](#file-upload) file-upload@v0-2-0 - dataloader - data-loading * - [hello-world (@v0.2.1)](#hello-world) hello-world@v0-2-1 - processing - demo hello-world * - [hello-world-multi-step (@v0.2.1)](#hello-world-multi-step) hello-world-multi-step@v0-2-1 - processing - demo hello-world multistep * - [hinge-loss (@v1.0.0)](#hinge-loss@v1-0-0) hinge-loss@v1-0-0 - processing - objective-function optimization * - [json-visualization (@v0.2.1)](#json-visualization) json-visualization@v0-2-1 - visualization - json visualization * - [mqt-simulator (@v1.0.1)](#mqt-simulator) mqt-simulator@v1-0-1 - processing - circuit-executor mqt qasm qasm-2 qasm-3 qc-simulator * - [neural-network (@v1.0.1)](#neural-network@v1-0-1) neural-network@v1-0-1 - processing - ML classical gradient neural-network objective-function optimization * - [nisq-analyzer (@v0.2.0)](#nisq-analyzer) nisq-analyzer@v0-2-0 - processing - nisq-analyzer * - [pennylane-simulator (@v1.0.1)](#pennylane-simulator) 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) pytket_qulacsBackend-simulator@v1-0-0 - processing - circuit-executor pytket_qulacsBackend qasm qasm-2 qc-simulator * - [qasm-visualization (@v0.3.1)](#qasm-visualization) qasm-visualization@v0-3-1 - visualization - qasm visualization * - [qiskit-simulator (@v1.0.1)](#qiskit-simulator) qiskit-simulator@v1-0-1 - processing - circuit-executor qasm qasm-2 qasm-3 qc-simulator qiskit * - [ridge-loss (@v1.0.0)](#ridge-loss@v1-0-0) 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 (distance-aggregator)= ### Aggregators (@v0.2.1) processing – distance-calculation, preprocessing\ *Path:* {file}`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)= ### AmazonBraket_LocalSimulator (@v1.0.0) processing – braket_local, circuit-executor, qasm, qasm-3, qc-simulator\ *Path:* {file}`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)= ### Classical k Means (@v0.1.1) processing – ML, classical, clustering\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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)= ### Classical k Medoids (@v0.1.1) processing – ML, classical, clustering\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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)= ### Clustered Scatter Plot Visualization (@v1.0.0) visualization – cluster, scatter, visualization\ *Path:* {file}`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)= ### Confusion Matrix Visualization (@v1.0.0) visualization – cluster, confusion-matrix, visualization\ *Path:* {file}`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)= ### Costume loader (@v0.2.1) dataloader – MUSE, data-loading\ *Path:* {file}`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-creator)= ### Data Creation (@v0.2.3) dataloader – data-loading, data-synthesizing\ *Path:* {file}`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)= ### Deploy Workflow (@v0.1.1) processing – bpmn, camunda-engine, workflow\ *Path:* {file}`stable_plugins/workflow/workflows/__init__.py` Deploys a BPMN workflow to Camunda and exposes it as a plugin. (entity-filter)= ### Entity loader/filter (@v0.2.1) processing – filter, preprocessing, sample\ *Path:* {file}`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)= ### Histogram Visualization (@v1.0.1) visualization – histogram, non-default, visualization\ *Path:* {file}`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)= ### Hybrid Autoencoder (@v0.2.1) processing – QML, feature-engineering, preprocessing, quantum\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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|✓| (low-code-modeler)= ### LCM (@v0.0.0) interaction – low-code-modeler\ *Path:* {file}`plugins/low_code_modeler/plugin.py` low code modeler plugin (muse-for-music-loader)= ### MUSE4Music Loader (@v1.0.0) dataloader – MUSE4Music, data-loading\ *Path:* {file}`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)= ### Manual Classification (@v0.2.1) processing – data-annotation, manual, preprocessing\ *Path:* {file}`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)= ### Max Cut (@v0.1.1) processing – QML, clustering, quantum\ *Path:* {file}`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|╳| (mds)= ### Multidimensional Scaling (MDS) (@v0.2.1) processing – distance-calculation, embedding, feature-engineering, preprocessing\ *Path:* {file}`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)= ### One-Hot Encoding (@v0.2.1) processing – encoding, one-hot-encoding, preprocessing\ *Path:* {file}`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)= ### Optics (@v0.1.1) processing – ML, classical, clustering\ *Path:* {file}`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](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.OPTICS.html#sklearn.cluster.OPTICS). The entity points should be saved in the [entity/vector](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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)= ### Optimizer plugin (@v1.0.1) processing – optimization\ *Path:* {file}`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)= ### Pandas Preprocessing (@v0.1.1) processing – data-cleaning, preprocessing\ *Path:* {file}`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|✓| (pca)= ### Principle Component Analysis (PCA) (@v0.2.2) processing – feature-engineering, preprocessing\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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)= ### Qiskit Executor (@v0.1.1) processing – circuit-executor, qasm, qasm-2, qasm-3, qc-executor, qiskit\ *Path:* {file}`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)= ### Qiskit Quantum Kernel Estimation (@v0.2.1) processing – QML, kernel, mapping, quantum\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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).](https://doi.org/10.1038/s41586-019-0980-2) **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)= ### Quantum CNN (@v0.1.1) processing – QML, classification, neural-network, quantum\ *Path:* {file}`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)= ### Quantum Kernel Estimation (@v0.2.1) processing – QML, kernel, mapping, quantum\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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).](https://doi.org/10.1038/s41586-019-0980-2) [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).](https://doi.org/10.1007/s42484-020-00020-y) **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|✓| (qnn)= ### Quantum Neutral Network (QNN) (@v0.1.1) processing – QML, classical, classification, neural-network, quantum\ *Path:* {file}`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)= ### Quantum Parzen Window (@v0.2.1) processing – QML, classification, quantum, supervised-learning\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#entity-vector) format and labels in the [entity/label](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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).](https://doi.org/10.1007/s10773-017-3514-4) **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)= ### Quantum Variational Classifier (@v0.1.1) processing – QML, classification, quantum\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#entity-vector) format and labels in the [entity/label](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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](https://qiskit.org/documentation/machine-learning/stubs/qiskit_machine_learning.algorithms.VQC.html#qiskit_machine_learning.algorithms.VQC) [1] [Havlíček, V., Córcoles, A.D., Temme, K. et al. Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019).](https://doi.org/10.1038/s41586-019-0980-2) **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)= ### Quantum k Nearest Neighbours (@v0.2.1) processing – QML, clustering, quantum, supervised-learning\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#entity-vector) format and labels in the [entity/label](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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.](https://doi.org/10.1007/978-3-319-13560-1_17) [1] [Basheer, Afrad and Afham, A. and Goyal, Sandeep K. (2020). Quantum k-nearest neighbors algorithm. In arXiv.](https://doi.org/10.48550/arXiv.2003.09187) [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).](https://doi.org/10.1007/s10773-017-3514-4) **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)= ### Quantum k-means (@v0.2.1) processing – QML, clustering, quantum\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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.](https://doi.org/10.48550/ARXIV.1909.12183) [1] **Inputs:** | Data Type | Content Type | Required | |-----------|--------------| :------: | |entity/vector|application/json, text/csv|✓| **Outputs:** | Data Type | Content Type | Always | |-----------|--------------| :----: | |custom/clusters|application/json|✓| (rest-connector)= ### REST API Connector (@v0.1.0) interaction – rest\ *Path:* {file}`plugins/rest_connector/plugin.py` Integrate REST APIs as plugins. (sql-loader)= ### SQL Loader (@v0.1.1) processing – data-loading, mariadb, mysql, postgresql, sql\ *Path:* {file}`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)= ### SVM (@v0.1.1) processing – ML, QML, classical, classification, quantum, supervised-learning\ *Path:* {file}`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](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#entity-vector) format and labels in the [entity/label](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#entity-label) format. A precomputed kernel matrix should be stored in the [entity/matrix](https://qhana-plugin-runner.readthedocs.io/en/latest/data-formats/examples/entities.html#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](https://scikit-learn.org/1.1/modules/svm.html#svm) [1] Qiskit's quantum kernels [ZFeatureMap](https://qiskit.org/documentation/stubs/qiskit.circuit.library.ZFeatureMap.html), [ZZFeatureMap](https://qiskit.org/documentation/stubs/qiskit.circuit.library.ZZFeatureMap.html) and [PauliFeatureMap](https://qiskit.org/documentation/stubs/qiskit.circuit.library.PauliFeatureMap.html) [2] [Havlíček, V., Córcoles, A.D., Temme, K. et al. Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019).](https://doi.org/10.1038/s41586-019-0980-2) [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).](https://doi.org/10.1007/s42484-020-00020-y) **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)= ### Scipy Minimizer Plugin (@v1.0.0) processing – minimizer, optimization\ *Path:* {file}`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-grad)= ### Scipy Minimizer with Gradient Plugin (@v1.0.0) processing – gradient, minimizer, optimization\ *Path:* {file}`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|✓| (sim-to-dist-transformers)= ### Similarities to distances transformers (@v0.2.1) processing – distance-calculation, preprocessing, similarity-calculation\ *Path:* {file}`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)= ### Sym Max Mean attribute comparer (@v0.1.2) processing – preprocessing, similarity-calculation\ *Path:* {file}`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)= ### Time tanh similarities (@v0.2.1) processing – preprocessing, similarity-calculation\ *Path:* {file}`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)= ### Workflow Editor (@v0.1.0) interaction – camunda, quantme, workflow\ *Path:* {file}`stable_plugins/workflow/workflow_editor/plugin.py` Edit BPMN workflows with an online editor. (workflow-management)= ### Workflow Management (@v0.1.1) interaction – bpmn, camunda-engine, workflow\ *Path:* {file}`stable_plugins/workflow/workflows/management.py` Manage workflows deployed in Camunda. (wu-palmer)= ### Wu Palmer similarities (@v0.2.1) processing – preprocessing, similarity-calculation\ *Path:* {file}`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)= ### ZX-Calculus Visualization (@v1.0.1) visualization – circuit, non-default, visualization, zxcalculus\ *Path:* {file}`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)= ### Zip merger (@v0.2.0) processing – utility\ *Path:* {file}`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)= ### circuit-demo (@v1.0.1) processing – circuit-demo, demo, quantum\ *Path:* {file}`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)= ### cirq-simulator (@v1.0.0) processing – circuit-executor, cirq, qasm, qasm-2, qc-simulator\ *Path:* {file}`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)= ### csv-visualization (@v0.1.1) visualization – csv, visualization\ *Path:* {file}`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)= ### data-join (@v1.0.0) processing – join, preprocessing\ *Path:* {file}`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)= ### file-upload (@v0.2.0) dataloader – data-loading\ *Path:* {file}`stable_plugins/file_utils/file_upload.py` Uploads files to use in the workflow. **Outputs:** | Data Type | Content Type | Always | |-----------|--------------| :----: | |*/*|*/*|✓| (hello-world)= ### hello-world (@v0.2.1) processing – demo, hello-world\ *Path:* {file}`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)= ### hello-world-multi-step (@v0.2.1) processing – demo, hello-world, multistep\ *Path:* {file}`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)= ### hinge-loss (@v1.0.0) processing – objective-function, optimization\ *Path:* {file}`plugins/hinge_loss/__init__.py` Hinge Loss objective-function plugin. **Outputs:** | Data Type | Content Type | Always | |-----------|--------------| :----: | |txt/*|text/plain|✓| (json-visualization)= ### json-visualization (@v0.2.1) visualization – json, visualization\ *Path:* {file}`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)= ### mqt-simulator (@v1.0.1) processing – circuit-executor, mqt, qasm, qasm-2, qasm-3, qc-simulator\ *Path:* {file}`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)= ### neural-network (@v1.0.1) processing – ML, classical, gradient, neural-network, objective-function, optimization\ *Path:* {file}`plugins/neural_network/__init__.py` Neural Network objective-function plugin. **Outputs:** | Data Type | Content Type | Always | |-----------|--------------| :----: | |txt/*|text/plain|✓| (nisq-analyzer)= ### nisq-analyzer (@v0.2.0) processing – nisq-analyzer\ *Path:* {file}`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)= ### pennylane-simulator (@v1.0.1) processing – circuit-executor, pennylane, qasm, qasm-2, qasm-3, qc-simulator\ *Path:* {file}`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)= ### pytket_qulacsBackend-simulator (@v1.0.0) processing – circuit-executor, pytket_qulacsBackend, qasm, qasm-2, qc-simulator\ *Path:* {file}`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)= ### qasm-visualization (@v0.3.1) visualization – qasm, visualization\ *Path:* {file}`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)= ### qiskit-simulator (@v1.0.1) processing – circuit-executor, qasm, qasm-2, qasm-3, qc-simulator, qiskit\ *Path:* {file}`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)= ### ridge-loss (@v1.0.0) processing – objective-function, optimization\ *Path:* {file}`plugins/ridge_loss/__init__.py` Ridge Loss objective-function plugin. **Outputs:** | Data Type | Content Type | Always | |-----------|--------------| :----: | |txt/*|text/plain|✓|