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.
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:
My research in QML are divided into two directions:
(1) infrastructure development
(1) infrastructure development
(2) QML algorithm innovations
(2) QML algorithm innovations
QML infrastructure: Tensorflow Quantum
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.
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators.
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators.
QML Algorithm Design
QML Algorithm Design
Entangling Quantum Generative Adversarial Networks
Entangling Quantum Generative Adversarial Networks
Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) that overcomes some limitations of previously proposed quantum GANs. Leveraging the entangling power of quantum circuits, EQ-GAN guarantees the convergence to a Nash equilibrium under minimax optimization of the discriminator and generator circuits by performing entangling operations between both the generator output and true quantum data. We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor. By adversarially learning efficient representations of quantum states, we prepare an approximate quantum random access memory (QRAM) and demonstrate its use in applications including the training of quantum neural networks.
Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) that overcomes some limitations of previously proposed quantum GANs. Leveraging the entangling power of quantum circuits, EQ-GAN guarantees the convergence to a Nash equilibrium under minimax optimization of the discriminator and generator circuits by performing entangling operations between both the generator output and true quantum data. We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor. By adversarially learning efficient representations of quantum states, we prepare an approximate quantum random access memory (QRAM) and demonstrate its use in applications including the training of quantum neural networks.
