Springer Towards a Scalable Quantum Computing Platform in the Ultrastrong Coupling Regime (Springer Theses)

Product Description This thesis devotes three introductory chapters to outlining basic recipes for constructing the quantum Hamiltonian of an arbitrary superconducting circuit, starting from classical circuit design. Since a superconducting circuit is one of the most promising platforms for realizing a practical quantum computer, anyone who is starting out in the field will benefit greatly from this introduction. The second focus of the introduction is the ultrastrong light-matter interaction (USC), where the latest developments are described. This is followed by three main research works comprising quantum memory in USC; scaling up the 1D circuit to a 2D lattice configuration; creation of Noisy Intermediate-Scale Quantum era quantum error correction codes and polariton-mediated qubit-qubit interaction. The research work detailed in this thesis will make a major contribution to the development of quantum random access memory, a prerequisite for various quantum machine learning algorithms and applications. From the Back Cover The thesis devotes three introductory chapters to outline basic recipes to construct quantum Hamiltonian of an arbitrary superconducting circuit, starting from classical circuit design. Since superconducting circuit is one of the most promising platforms towards a practical quantum computer, anyone who is starting the field would be profoundly benefited from this thesis, and should be able to pick it up timely. The second focus of the introduction is the ultrastrong light-matter interaction (USC), summarizing latest developments in the community. It is then followed by the three main research work comprising- quantum memory in USC, scaling up the 1D circuit to 2D lattice configuration, creation of Noisy Intermediate-Scale Quantum era quantum error correction codes and polariton-mediated qubit-qubit interaction. We believe that the research work detailed in this thesis would eventually lead to development of quantum random access memory which is needed for various quantum machine learning algorithms and applications.