Other areas of interest in quantum machine learning include:

* Modeling purely classical data on quantum computers.
* Quantum-inspired classical algorithms.
* Supervised learning with quantum classifiers.
* Adaptive layer-wise learning for quantum neural network.
* Quantum dynamics learning.
* Generative modeling of mixed quantum states .
* Classification with quantum neural networks on near term processors.

Other Areas of Interest in Quantum Machine Learning

The quantum data generated by NISQ processors are noisy and typically entangled just before the measurement occurs. Heuristic machine learning techniques can create models that maximize extraction of useful classical information from noisy entangled data. The TensorFlow Quantum (TFQ) library provides primitives to develop models that disentangle and generalize correlations in quantum data—opening up opportunities to improve existing quantum algorithms or discover new quantum algorithms.

University of Michigan - Ann Arbor

TensorFlow Quantum (TFQ) is a quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models. Research in quantum algorithms and applications can leverage Google’s quantum computing frameworks, all from within TensorFlow.

TensorFlow Quantum focuses on quantum data and building hybrid quantum-classical models. It integrates quantum computing algorithms and logic designed in Cirq, and provides quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators.

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