Key research areas in quantum machine learning include modeling purely classical data on quantum computers, developing quantum-inspired classical algorithms, and performing supervised learning with quantum classifiers. Additional areas of focus involve adaptive layer-wise learning for quantum neural networks, quantum dynamics learning, generative modeling of mixed quantum states, and classification using quantum neural networks on near-term processors.

Research Areas in Quantum Machine Learning

Quantum data generated by Noisy Intermediate-Scale Quantum (NISQ) processors are typically noisy and entangled prior to measurement. Heuristic machine learning techniques can be applied to create models that maximize the extraction of useful classical information from this noisy data. Frameworks like the TensorFlow Quantum (TFQ) library provide primitives to develop machine learning models that disentangle and generalize correlations in quantum data, facilitating the improvement or discovery of quantum algorithms.

University of Michigan - Ann Arbor

Google

TensorFlow Quantum (TFQ) is an open-source Python library for rapid prototyping of hybrid quantum-classical machine learning models (Broughton et al., 2020). By integrating quantum circuit design in Cirq with TensorFlow, TFQ provides quantum computing primitives and high-performance simulators to process quantum data and build hybrid algorithms (Broughton et al., 2020; TensorFlow Quantum Tutorial).

TensorFlow Quantum

Quantum data is any data source that occurs in a natural or artificial quantum system. This can be data generated by a quantum computer, like the samples gathered from the Sycamore processor for Google’s demonstration of quantum supremacy. Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. The quantum supremacy experiment showed it is possible to sample from an extremely complex joint probability distribution of 2^53 Hilbert space.

Quantum Data

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

https://www.tensorflow.org/quantum/overview

Tutorial of TensorFlow Quantum

Machine Learning for Noisy Quantum Data

Quantum data generation and simulation have several key applications:

* **Chemical simulation**: Extracting information about chemical structures and dynamics with potential applications to materials science, computational chemistry, computational biology, and drug discovery.
* **Quantum matter simulation**: Modeling and designing high-temperature superconductivity or other exotic states of matter which exhibit many-body quantum effects.
* **Quantum control**: Training hybrid quantum-classical models to perform optimal open- or closed-loop control, calibration, error mitigation, and error correction strategies for quantum devices.
* **Quantum communication networks**: Using machine learning to discriminate among non-orthogonal quantum states to design and construct structured quantum repeaters, receivers, and purification units.
* **Quantum metrology**: Improving high-precision measurements (such as quantum sensing and imaging) inherently done on small-scale quantum probes using variational quantum models.

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