Year: 2015
Type: dissertation
Abstract: Quantum information processing offers potential improvements to a wide range \nof computing endevaors, including cryptography, chemistry simulations and \nmachine learning. The development of practical quantum information processing \ndevices is impeded, however, by challenges arising from the apparent exponential \ndimension of the space one must consider in characterizing quantum \nsystems, verifying their correct operation, and in designing useful control \nsequences. In this work, we address each in turn by providing useful \nalgorithms that can be readily applied in experimental practice. \n \nIn order to characterize the dynamics of quantum systems, we apply statistical \nmethods based on Bayes' rule, thus enabling the use of strong prior \ninformation and parameter reduction. We first discuss an \nanalytically-tractable special case, and then employ a numerical algorithm, \nsequential Monte Carlo, that uses simulation as a resource for characterization. We \ndiscuss several examples of SMC and show its application in nitrogen vacancy \ncenters and neutron interferometry. \nWe then discuss how characterization techniques such as SMC can be used to \nverify quantum systems by using credible region estimation, model selection, \nstate-space modeling and hyperparameterization. Together, these techniques \nallow us to reason about the validity of assumptions used in analyzing quantum \ndevices, and to bound the credible range of quantum dynamics. \n \nNext, we discuss the use of optimal control theory to design robust control \nfor quantum systems. We show extensions to existing OCT algorithms that allow \nfor including models of classical electronics as well as quantum dynamics, \nenabling higher-fidelity control to be designed for cutting-edge experimental \ndevices. Moreover, we show how control can be implemented in parallel across \nnode-based architectures, providing a valuable tool for implementing \nproposed fault-tolerant protocols. \n \nWe close by showing how these algorithms can be augmented using quantum \nsimulation resources to enable addressing characterization and control design \nchallenges in even large quantum devices. In particular, we will introduce a \nnovel genetic algorithm for quantum control design, MOQCA, that utilizes \nquantum coprocessors to design robust control sequences. Importantly, MOQCA is \nalso memetic, in that improvement is performed between genetic steps. We then \nextend sequential Monte Carlo with quantum simulation resources to enable \ncharacterizing and verifying the dynamics of large quantum devices. By using \nnovel insights in epistemic information locality, we are able to learn \ndynamics using strictly smaller simulators, leading to an algorithm we call \nquantum bootstrapping. We demonstrate by using a numerical example of learning \nthe dynamics of a 50-qubit device using an 8-qubit simulator.
Source: UWSpace (University of Waterloo)
Author: Christopher Granade
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W826023566