Quantum Machine Learning
How may one apply quantum computing to practical tasks? One area of research that has attracted considerable interest is the design of machine learning algorithms that inherently rely on quantum properties to accelerate their performance. One key observation that has led to the application of quantum computers to machine learning is their ability to perform fast linear algebra on a state space that grows exponentially with the number of qubits. These quantum accelerated linear-algebra based techniques for machine learning can be considered the first generation of quantum machine learning (QML) algorithms tackling a wide range of applications in both supervised and unsupervised learning, including principal component analysis, support vector machines, kmeans clustering, and recommendation systems. These algorithms often admit exponentially faster solutions compared to their classical counterparts on certain types of quantum data. This has led to a significant surge of interest in the subject. However, to apply these algorithms to classical data, the data must first be embedded into quantum states, a process whose scalability is under debate. Additionally, many common approaches for applying these algorithms to classical data rely on specific structure in the data that can also be exploited by classical algorithms, sometimes precluding the possibility of a quantum speedup. Tests based on the structure of a classical dataset have recently been developed that can sometimes determine if a quantum speedup is possible on that data. Continuing debates around speedups and assumptions make it prudent to look beyond classical data for applications of quantum computation to machine learning.
My research in QML are divided into two directions:
(1) infrastructure development
(2) QML algorithm innovations
QML infrastructure: Tensorflow Quantum
Fig. 1, A high-level abstract overview of the computational steps involved in the end-to-end pipeline for inference and training of a hybrid quantum-classical discriminative model for quantum data in TFQ. To see the code for an end-to-end example, please check the “Hello Many-Worlds” example, the quantum convolutional neural networks tutorial, and our guide.