First end-to-end quantum machine learning Python SDK for classification models. 

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Stafford Computing LLC


Non-quantum coding required

Customized quantum embeddings

End-to-end QML process

The structure of the SDK will allow the data to be executed in different ways for quantum encoding (Qiskit, PennyLane, and proprietary) to extract the best result possible after running the whole process, including algorithms and postprocessing.

The SDK does not require knowledge of any quantum programming language or even expertise in quantum principles or linear algebra. The process of running quantum algorithms will require just a Python environment, a few parameters, and execution.

This Python library will execute an end-to-end process with regards to classification models. Preprocessing, encoding, algorithms, and postprocessing are stages fully embedded in our system.

Fully compatible with well-known quantum SDKs : 

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QUANTUM software development kit

End-to-end quantum machine learning classification model execution

Stafford Computing developed the Falcondale Python SDK with the clear objective of providing a fast and easy path for data scientists, data engineers, analysts, developers, and any quantum enthusiast (with a little Python knowledge) to execute hybrid quantum-classical and quantum-inspired algorithms. This SDK will allow the users to input their classical data (in the same way that is used for classical machine learning models), set up the parameters for the quantum part execution, and later receive the results of the classification model.

The SDK will not require knowledge about quantum programming or physics, but we recommend exploring more deeply some processes in the quantum part to be aware of the potential benefits that this technology can bring to the client's business.

Our tech stack ecosystem 

Features and details:

  • Default parameters can be adjusted by the user (repetitions, shots, qubits, iterations, layers, batches, simulators, among others)
  • Scaling and dimensionality reduction automated
  • Well-known quantum encoding methods included
  • Customized quantum embedding structure to be evaluated against other SDKs methods
  • Hybrid classical-quantum algorithms embedded (Quantum Support Vector Machines, Variational Quantum Algorithms, Quantun Neural Networks)
  • Quantum-inspired algorithms also included (Tensor Networks)
  • Model's evaluation incorporated to select the best results under different encoding methods and algorithms

custom quantum embeddings

Classical Data Space

Classical data usually represents a challenging process if the objective is to classify the datapoints using linear approaches (e.g., SVM). Finding the most accurate way to separate both (or more) classes can be a very difficult process to execute when high accuracy is required.

Quantum Hilbert Space

By the use of quantum embeddings for quantum machine learning algorithms, the data can be represented in a larger dimensional space (Hilbert Space). This technique can be useful in specific cases to extract better separability from non-linearly separable datapoints. Falcondale Python SDK includes customized quantum embedding operations to boost the classification results.

Explore the potential benefits of quantum machine learning in your own projects and classification challenges.

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