A pragmatic, near-term results-driven approach
Our approach to quantum machine learning is to focus scarce quantum resources on hard subtasks in machine learning algorithms. Consequently, our pioneering research centers around hybrid quantum-classical probabilistic methods for unsupervised learning, generative models, and inference.
A common approach to unsupervised learning is to learn a probabilistic model of data. These models can be trained on unlabeled datasets. Generative modeling is one example of unsupervised learning wherein the model automatically learns the patterns of input data in such a way that it can be used to generate new data samples that would seemingly match the statistical distribution of the original dataset.
Quantum circuit Born machines are an example of generative models and are based on parameterized quantum circuits. They can represent probabilistic models and sample complex probability distributions more efficiently than classical computers for certain tasks or datasets. We can train these Born machines for a diverse range of downstream tasks such as reasoning under uncertainty, data augmentation, and anomaly detection. Generative models can also make machine learning more transparent by incorporating domain expertise.
We implement quantum machine learning methods in a pragmatic and hardware-inclusive way. This allows us to implement small-scale models on today’s limited quantum hardware and improve the models as quantum hardware matures.