Binary Optimization

Get a significant performance boost for D-Wave hardware and use the latest improvements for gate-based approaches.

  • Quantum Annealing

    Bring your own QUBO (BYOQ). Having a hard time getting good solutions? We pick the best parameters for D-Wave’s Quantum Annealer and get better results than the defaults.

    Learn how to use it - read the docs

    Interested in how we do it? - read the paper

  • Quantum Approximate Optimization Algorithm (QAOA)

    Try our off-the-shelf QAOA implementation and see how well it performs.

    Learn how to use it - read the docs

    Interested in how we do it? - read the paper

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Linear Algebra

Execute basic linear algebra functions like matrix multiplication and distance estimation on a quantum computer.

  • Distance Estimation

    Euclidean distance calculations are at the heart of many common engineering problems and algorithms. Our algorithm library can calculate distances (up to some finite epsilon) faster on current NISQ hardware.

    Learn how to use it - read the docs

    Interested in how we do it? - read the paper

  • Matrix Multiplication (Dot Product)

    This is matrix multiplication on steroids.

    Learn how to use it - read the docs

    Interested in how we do it? - read the paper

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Machine Learning

Try out quantum data loaders, as well as algorithms with guaranteed speed-ups on clustering, classification, and regression.

  • Supervised Learning

    Import your data, whether it's enterprise data or a dataset from Kaggle. Execute classification and regression algorithms just like your favorite classical machine learning library. Take a look at our Q Nearest Neighbors classifier and regressor and Q Nearest Centroid classifier.

    Learn how to use it - read the docs

    Interested in how we do it? - read the paper

  • Unsupervised Learning

    Q-means performs better than its classical counterpart, k-means, for higher-dimensional data sets. For a data set with 64 dimensions q-means can be 10 times faster than k-means. With 256 dimensions it is 30 times faster, and for 1,000 dimensions it is 100 times faster.

    Learn how to use it - read the docs

    Interested in how we do it? - read the paper

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