An Exciting Year in Quantum Computing Research at NASA

Overview

Successful NASA missions require the solution of many challenging computational problems. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA’s Ames Research Center is the hub for assessing the potential impact of quantum computers on computational problems that will be faced by NASA missions in the coming decades. The power of quantum computing comes from encoding information in qubits—non-classical units of information which enable unconventional computation that takes advantage of quantum tunneling, interference, and entanglement.

While large-scale universal quantum computers are likely decades away, the next few years will be exciting with the emergence of prototype quantum computational hardware, enabling exploration of a broad array of quantum heuristic algorithms for optimization, machine learning, simulation of quantum chemistry and material science, and beyond. The QuAIL team develops novel quantum and hybrid quantum-classical algorithms; maps specific problems to quantum algorithms; devises quantum programming principles and compilation strategies; characterizes quantum hardware capabilities; and explores small-scale versions of quantum algorithms on prototype hardware and in simulation to assess the breadth and range of quantum computing’s impact on NASA missions.

Project Details

QuAIL utilizes supercomputing resources, analytical techniques, and access to emerging quantum hardware—both special-purpose hardware, such as quantum annealers, and general-purpose hardware such as superconducting gate-mode quantum processors, available to the team through partnerships. QuAIL researchers have extensive expertise in making effective use of near-term quantum hardware to evaluate the potential impact of quantum computing. Initially, the focus of the team was on quantum annealing, because D-Wave quantum annealers were the only quantum processors available when QuAIL was formed. (Quantum annealers are special-purpose hardware that can run only one type of quantum algorithm—quantum annealing, an optimization metaheuristic.) More recently, as general-purpose quantum gate-model processors have emerged, the team has broadened its work to include significant contributions to gate-model quantum computing, particularly for exact and approximate optimization; and for sampling, with a focus on techniques that can be implemented on near-term hardware.

Results and Impact

The QuAIL team has pioneered novel techniques for programming and compiling code for near-term quantum processors, including hybrid quantum-classical approaches, enabling maximum utilization of emerging quantum hardware. QuAIL research has also advanced purely classical algorithms, inspired by physics and computational insights; the researchers have also pushed the boundary of classical simulation of gate-model quantum circuits through novel simulation techniques. The team is broadly engaged in elucidating the mechanisms underlying quantum computing, including analytical and experimental insights into tunneling, entanglement, resource states for measurement-based quantum computation, thermalization and freeze-out, fair sampling, and quantum many-body delocalization. They have developed state-of-the-art quantum approaches to computational challenges in planning and scheduling, fault diagnosis, robust network design, and machine learning.

Why HPC Matters

QuAIL makes extensive use of NASA's conventional supercomputers for simulations of quantum algorithms and systems, and to compare quantum and classical approaches to challenging computational problems. The team also uses the D-Wave quantum annealer housed in the NASA Advanced Supercomputing facility at Ames Research Center, in collaboration with Google and Universities Space Research Association. Our research helps advance the time when quantum procesors will be a standard part of the high-performance computing environment.

More Information

• NASA Quantum Artificial Intelligence Laboratory
• A NASA Perspective on Quantum Computing: Opportunities and Challenges
• See the poster for this demonstration (PDF, 1.9MB). As shown in the NASA booth at SC18.