Quantum Computing

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.

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.

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).

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.

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.