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SUPERCOMPUTING SCIENCE MISSION DIRECTORATE In partnership with Google and USRA, NASA houses a D-Wave quantum annealer at Ames Research Center. The agency is the Test & Evaluation lead for the Intelligence Advanced Research Projects Activity’s Quantum Enhanced Optimization Program, and has worked with other partners to explore prototype gate-model processors. Left: the D-Wave quantum annealer at NASA Ames. Right: quantum processors being developed at Google (top) and Rigetti Computing (bottom). Images courtesy of John Hardman, NASA; Erik Lucero, Google; Rigetti Computing Eleanor Rieffel, NASA Ames Research Center Rupak Biswas, NASA Ames Research Center To explore the potential of quantum computing to solve mission challenges and enable more ambitious missions, NASA established the Quantum Artificial Intelligence Laboratory (QuAIL). The power of quantum computing comes from encoding data in qubits—units of information that enable non-classical computations—which take advantage of quantum tunneling, interference, and entanglement. The QuAIL team evaluates emerging quantum systems, both special-purpose hardware, such as quantum annealers, and general-purpose gate-model quantum processors. The team develops programming principles and compilation strategies, characterizes hardware capabilities, and explores small-scale versions of quantum algorithms to determine the potential of quantum computing and the breadth and range of its applications to NASA missions. Examples of NASA research into quantum computing applications. Top: general planning problems, encompassing navigation, scheduling, and asset allocation, including job shop scheduling. Middle: fault diagnosis in simple electrical power networks and more complex digital circuits. Bottom: Boltzmann sampling, commonly used in machine learning. We successfully demonstrated that machine learning occurs when using a quantum annealer as a Boltzmann sampler, overcoming technical challenges. Eleanor Rieffel, NASA/Ames Quantum Computing Research at NASA

Quantum Computing Research at NASA › ... › 19_Rieffel_E_Quantum_SC17LR.pdfD-Wave quantum annealer at NASA Ames. Right: quantum processors being developed at Google (top) and Rigetti

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Page 1: Quantum Computing Research at NASA › ... › 19_Rieffel_E_Quantum_SC17LR.pdfD-Wave quantum annealer at NASA Ames. Right: quantum processors being developed at Google (top) and Rigetti

S U P E R C O M P U T I N GSCIENCE MISSION DIRECTORATE

In partnership with Google and USRA, NASA houses a D-Wave quantum annealer at Ames Research Center. The agency is the Test & Evaluation lead for the Intelligence Advanced Research Projects Activity’s Quantum Enhanced Optimization Program, and has worked with other partners to explore prototype gate-model processors. Left: the D-Wave quantum annealer at NASA Ames. Right: quantum processors being developed at Google (top) and Rigetti Computing (bottom). Images courtesy of John Hardman, NASA; Erik Lucero, Google; Rigetti Computing

Eleanor Rieffel, NASA Ames Research CenterRupak Biswas, NASA Ames Research Center

To explore the potential of quantum computing to solve mission challenges and enable more ambitious missions, NASA established the Quantum Artificial Intelligence Laboratory (QuAIL). The power of quantum computing comes from encoding data in qubits—units of information that enable non-classical computations—which take advantage of quantum tunneling, interference, and entanglement. The QuAIL team evaluates emerging quantum systems, both special-purpose hardware, such as quantum annealers, and general-purpose gate-model quantum processors. The team develops programming principles and compilation strategies, characterizes hardware capabilities, and explores small-scale versions of quantum algorithms to determine the potential of quantum computing and the breadth and range of its applications to NASA missions.

Examples of NASA research into quantum computing applications. Top: general planning problems, encompassing navigation, scheduling, and asset allocation, including job shop scheduling. Middle: fault diagnosis in simple electrical power networks and more complex digital circuits. Bottom: Boltzmann sampling, commonly used in machine learning. We successfully demonstrated that machine learning occurs when using a quantum annealer as a Boltzmann sampler, overcoming technical challenges. Eleanor Rieffel, NASA/Ames

Quantum Computing Research at NASA