Quantum machine learning (QML) is a rapidly growing field at the intersection of quantum technology and artificial intelligence. It aims to use quantum computers and quantum algorithms to enhance the performance and capabilities of machine learning models.
One of the main challenges of QML is to design and implement QML models that are compatible with the current and near-term quantum devices, which are limited by noise, errors, and scalability issues. These devices are also known as noisy intermediate-scale quantum (NISQ) devices.
To address this challenge, Google has developed TensorFlow Quantum (TFQ), a software framework for hybrid quantum-classical machine learning. TFQ is an extension of the popular TensorFlow library for classical machine learning, and it integrates with Cirq, a framework for creating and manipulating quantum circuits.
TFQ allows users to build QML models using familiar TensorFlow APIs, such as Keras and tf.data. It also provides quantum computing primitives, such as quantum circuits, operators, measurements, and simulators, that can be used in conjunction with classical neural networks and data pipelines.
TFQ enables users to explore various QML applications, such as quantum data classification, quantum state reconstruction, quantum neural networks, variational quantum algorithms, and quantum error correction. TFQ also supports distributed training and execution of QML models on both simulators and real quantum hardware.
TFQ is a powerful tool for researchers and developers who want to experiment with QML on NISQ devices. It offers a flexible and user-friendly platform for creating and testing hybrid quantum-classical models that can leverage the advantages of both quantum and classical computing.
Read more:
- Quantum Machine Learning: Introduction to TensorFlow Quantum (mlq.ai)
- TensorFlow Quantum
- QML. Learning with Tensorflow Quantum | by Nicholas Teague | From the Diaries of John Henry | Medium
- [2307.00908] Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications (arxiv.org)
- [2301.01851] Quantum Machine Learning: from physics to software engineering (arxiv.org)
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