Xanadu Awarded Darpa Grant to Further Advance Quantum Machine Learning
Pennylane, the Company’s Cross-Platform Software for Quantum Machine Learning, Will Be Central to the Team’s Work.
Xanadu, a full-stack quantum computing and advanced AI company developing quantum hardware and software solutions, has been awarded a Defense Advanced Research Projects Agency (DARPA) grant. The grant will enable Xanadu to undertake a comprehensive investigation of the performance of quantum machine learning (QML) algorithms on currently available quantum computing hardware.
“There is a strong crossover happening right now between quantum computing and AI,” said Nathan Killoran, who heads up Xanadu’s Quantum Machine Learning team. “Many signature ideas and concepts from AI can be ported to be quantum-aware, and run on quantum computing hardware. But it’s a bit of a wild west at the moment. We don’t really have a good sense yet which of these ported machine learning methods are best suited to quantum computing, especially with today’s noisy and imperfect quantum hardware devices.”
Xanadu has been developing an open-source software platform for QML, known as PennyLane over the past two years. PennyLane allows users to connect quantum computing hardware and software from multiple vendors—Xanadu, IBM, Google, Rigetti, and Microsoft—with popular machine learning libraries like TensorFlow and PyTorch. This seamless integration will enable Xanadu researchers to systematically explore the performance of a variety of near-term QML algorithms on multiple hardware devices.
Xanadu will leverage the expertise of its in-house team of dedicated scientists, whose work in QML is globally recognized, to carry out the DARPA-funded research project over a twelve-month period. “Xanadu’s team has been at the forefront of cutting-edge QML research, implementing several world firsts in quantum software and delivering several signature papers in the field over the past two years,” said Christian Weedbrook, the company’s founder and CEO. “This award recognizes that strength and enables us to continue pushing the area of QML forward, towards eventual applications in a wide range of industries.”