Quantum machine learning made easy.
Your one-stop-shop for quantum machine learning education and development.
Quantum machine learning (QML) combines quantum computing and machine learning by integrating quantum algorithms into the machine learning pipeline.
Benefits of QML:
Encode exponentially larger data samples
Exponential computational speedups
Novel data representations
Improved data augmentation
Savings on training times and resources
Improved explainability
GET STARTED
The Ingenii Quantum Hub
Sign up for early access to our AI-powered QML learning & development platform. Learn more about the curriculum and your journey to QML literacy.
Discover QML
Begin your quantum machine learning journey for FREE with videos, articles, and fundamental QML lessons taught by our lead scientist, Dr. Laia Domingo.
Join our Quantum Community
Ingenii's Quantum Community is live on Discord, bringing together powerful quantum scientists and developers to collaborate on near-term, high impact solutions.
ENHANCE YOUR MACHINE LEARNING PIPELINE
Data Services
Unleash the true power of your data infrastructure with our industry-leading Databricks consulting services and accelerators, tailored to leverage the combined strength of Databricks, Delta Lake, and Azure Data Factory (ADF).
QML Enablement
We can help you identify a relevant use case for quantum machine learning with near-term advantage. With our team’s deep experience in cloud data platforms and quantum machine learning, we know how to effectively select a focused, tangible POC and integrate it tightly into your classical data ecosystem.
EXPLORE OUR RESEARCH
Our research covers world-class quantum solutions for prevalent challenges in oncology.
Medical Imaging
Explore our research on an end-to-end quantum machine learning pipeline that can process high-dimensional data more efficiently. Our pipeline will include: Medical imaging data (MRI or mammograms), a quantum filter to enhance image quality, and the ability to make predictions on tumor segmentation/classification.
Binding Affinity
Explore our research on an end-to-end quantum machine learning pipeline that can better predict the binding affinity between a potential drug and target proteins. Our pipeline will include: PDB-bind data, a hybrid 3DCNN to accelerate the training process, a quantum fusion model to improve the accuracy. Altogether we will aim to improve prediction performance and decrease costs.