Quantum Computing

Since its inception in June 1983, RIACS has conducted basic and applied research in computer science for the nation's aeronautics and space-related missions and programs. In 2012, USRA partnered with NASA and Google to found the Quantum Artificial Intelligence Laboratory (QuAIL): the space agency’s hub to evaluate the near term impact of quantum technologies.

The mission of RIACS quantum computing team is to advance the industry and the body of knowledge in quantum information related sciences, and to continue to provide to its partners the most qualified technical support to address hard challenges in applied computer science.




Collaborative Research Partnership between USRA and Standard Chartered Bank

USRA and Standard Chartered Bank have partnered to collaboratively advance the state of art of computing technologies. In the collaboration, USRA will support fundamental academic research in quantum physics and artificial intelligence and Standard Chartered will focus on future commercial applications in the field of Finance. The two institutions have already worked together in the past on the application of the Reverse Quantum Annealing technique for combinatorial optimization problems (see paper) and plan to continue to investigate advanced quantum annealing techniques as well as other quantum-assisted heuristics relevant for machine learning.

July 14th, 2020 Collaboration, Quantum Optimization, Quantum Machine Learning, Quantum Annealing

New ArXiv paper from QuAIL: Multifractal Dynamics of the QREM

This work by Parolini and Mossi studies energy matching in the Quantum Random Energy Model, a problem whose goal is to find multiple approximate solutions to an optimization problem once a given approximate solution is provided as input. A quantum process known as Population Transfer (PT) -- which uses resonant tunnelling in order to efficiently move through classically forbidden regions of configuration space -- is used in order to search for the target states. Through numerical simulations they find that PT optimality is achieved inside of the "bad metal" phase of the QREM close to the critical line of the Anderson transition. Comparison with classical heuristics for energy matching show a probabilistic oracle avantage in favour of PT, but the timescale necessary for the quantum dynamics to achieve such an optimal performance indicate that larger system sizes need to be explored in order to witness a genuine quantum advantage in this setting.

July 9th, 2020

QuAIL ArXiv paper: Quantum annealing speedup via suppression of singularities

In this paper by Knysh, Plamadeala and Venturelli, it is shown that a smart choice of embedding parameters reduces annealing times for random Ising chain from exponentially long to polynomially long for large problems. Dramatic reduction in time-to-solution for QA is confirmed by numerics, for which we developed a custom integrator to overcome convergence issues. The optimal parameter setting for applications problems embedded into hardware graphs is key to practical quantum annealers (QA).

July 2nd, 2020 quantum annealing, paper

New QuAIL ArXiv paper: Ferromagnetically shifting the power of pausing

In this paper by Izquierdo, Grabbe, Hadfield, Marshall, Wang and Rieffel, it is studied the interplay between quantum annealing parameters in embedded problems, providing both deeper insights into the physics of these devices and pragmatic recommendations to improve performance on optimization problems. Through runs on a D-Wave quantum annealer, it is demonstrated that pausing in a specific time window in the anneal provides improvement in the probability of success and in the time-to-solution for these problems. It is also confirmed that the optimal pause location exhibits a shift with the magnitude of the ferromagnetic coupling between physical qubits representing the same logical one. The work generally provides both deeper insights into the physics of quantum annealers and pragmatic recommendations to improve performance on optimization and sampling problems.

June 24th, 2020 quantum annealing, paper

New QuAIL paper posted on the ArXiv: Augmented fidelities for single qubit gates

In the paper the USRA-NASA-Google team present a novel method to characterize noise and imperfections in single qubit gates. The introduced metric provides an augmentation to a popular benchmarking tool of the average gate fidelity. We demonstrate how an average over different distributions of initial states can tell us more information about the underlying noise process and improve our understanding of a quantum hardware. In particular, using this newly developed techniques we can differentiate errors that are otherwise indistinguishable. We believe that this another step in providing more reliable methods to assess performance of quantum computers. For more information, contact Filip Wudarski fwudarski@usra.edu.

June 15th, 2020 Quantum Computing, Paper