NSF funded the project, “Quantum Machine Learning with Photonics,” as part of an initiative known as the Quantum Idea Incubator for Transformational Advances in Quantum Systems (QII - TAQS). QII-TAQS is designed to support interdisciplinary teams that will explore highly innovative and potentially transformative ideas for developing and applying quantum science, quantum computing, and quantum engineering.
“Our team is exploring a completely new approach to quantum computing that takes machine learning into the quantum domain,” said Electrical and Computer Engineering Professor Edo Waks, who is a fellow of the Joint Quantum Institute and the Quantum Technology Center, and the principal investigator of the grant. Co-principal investigators include Andrew Childs, UMD Computer Science and Institute for Advanced Computer Studies Professor, and Co-director of the Joint Center for Quantum Information and Computer Science, and Professors Seth Lloyd and Dirk Englund of the Massachusetts Institute of Technology.
In contrast to conventional approaches where computation is decomposed into logic gates, the investigators will focus on quantum computing architectures inspired by machine learning and deep learning. These architectures are naturally efficient and robust to noise, and are ideally suited to maximize the computational capabilities of currently available quantum processors which are composed of many noisy quantum bits. The project represents a highly multi-disciplinary effort that combines quantum hardware based on integrated and nonlinear optics, with algorithms and computer architecture and design. Success of the project could enable currently available quantum hardware to efficiently solve problems in a broad range of fields, such as medicine, biology, nuclear physics, and fundamental quantum science.
The UMD award is one of 19 QII-TAQS projects intended to deliver new concepts, platforms, and approaches that will accelerate the science, computing, and engineering of quantum technologies, resulting in breakthroughs in quantum sensing, quantum communication, quantum simulation, and quantum computing systems.
“Our team is exploring a completely new approach to quantum computing that takes machine learning into the quantum domain,” said Electrical and Computer Engineering Professor Edo Waks, who is a fellow of the Joint Quantum Institute and the Quantum Technology Center, and the principal investigator of the grant. Co-principal investigators include Andrew Childs, UMD Computer Science and Institute for Advanced Computer Studies Professor, and Co-director of the Joint Center for Quantum Information and Computer Science, and Professors Seth Lloyd and Dirk Englund of the Massachusetts Institute of Technology.
In contrast to conventional approaches where computation is decomposed into logic gates, the investigators will focus on quantum computing architectures inspired by machine learning and deep learning. These architectures are naturally efficient and robust to noise, and are ideally suited to maximize the computational capabilities of currently available quantum processors which are composed of many noisy quantum bits. The project represents a highly multi-disciplinary effort that combines quantum hardware based on integrated and nonlinear optics, with algorithms and computer architecture and design. Success of the project could enable currently available quantum hardware to efficiently solve problems in a broad range of fields, such as medicine, biology, nuclear physics, and fundamental quantum science.
The UMD award is one of 19 QII-TAQS projects intended to deliver new concepts, platforms, and approaches that will accelerate the science, computing, and engineering of quantum technologies, resulting in breakthroughs in quantum sensing, quantum communication, quantum simulation, and quantum computing systems.
IonQ Achieves Industry Leading Performance on Next Generation Barium Qubits