A simple all-microwave entangling gate for fixed-frequency superconducting qubits

We demonstrate an all-microwave two-qubit gate on superconducting qubits which are fixed in frequency at optimal bias points. The gate requires no additional subcircuitry and is tunable via the amplitude of microwave irradiation on one qubit at the transition frequency of the other. We use the gate to generate entangled states with a maximal extracted concurrence of 0.88 and quantum process tomography reveals a gate fidelity of 81%

Visualizing Poiseuille flow of hydrodynamic electrons

Hydrodynamics is a general description for the flow of a fluid, and is expected to hold even for fundamental particles such as electrons when inter-particle interactions dominate. While various aspects of electron hydrodynamics were revealed in recent experiments, the fundamental spatial structure of hydrodynamic electrons, the Poiseuille flow profile, has remained elusive. In this work, we provide the first real-space imaging of Poiseuille flow of an electronic fluid, as well as visualization of its evolution from ballistic flow. Utilizing a scanning nanotube single electron transistor, we image the Hall voltage of electronic flow through channels of high-mobility graphene. We find that the profile of the Hall field across the channel is a key physical quantity for distinguishing ballistic from hydrodynamic flow. We image the transition from flat, ballistic field profiles at low temperature into parabolic field profiles at elevated temperatures, which is the hallmark of Poiseuille flow. The curvature of the imaged profiles is qualitatively reproduced by Boltzmann calculations, which allow us to create a 'phase diagram' that characterizes the electron flow regimes. Our results provide long-sought, direct confirmation of Poiseuille flow in the solid state, and enable a new approach for exploring the rich physics of interacting electrons in real space

The evolutionary dynamics of biochemical networks in fluctuating environments

Typically, systems biology focuses on the form and function of networks of biochemical interactions. Questions inevitably arise as to the evolutionary origin of those networks' properties. Such questions are of interest to a growing number of systems biologists, and several groups have published studies shown how varying environments can affect network topology and lead to increased evolvability. For decades, evolutionary biologists have also investigated the evolution of evolvability and its relationship to the interactions between genotype and phenotype. While the perspectives of systems and evolutionary biologists sometimes differ, their interests in patterns of interactions and evolvability have much in common. This thesis attempts to bring together the perspectives of systems and evolutionary theory to investigate the evolutionary effects of fluctuating environments. Chapter 1 introduces the necessary themes, terminology and literature from these fields. Chapter 2 explores how rapid environmental fluctuations, or "noise", affects network size and robustness. In Chapter 3, we use the Avida platform to investigate the relationship between genetic architecture, fluctuating environments and population biology. Chapter 4 examines contingency loci as a physical basis for evolvability, while chapter 5 presents a 500-generation laboratory evolution experiment which exposes E. coli to varying environments. The final discussion, concludes that the evolution of generalism can lead to genetic architectures which confer evolvability, which may arise in rapidly fluctuating environments as a by-product of generalism rather than as a selected trait.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Evolutionary dynamics of tumor progression with random fitness values

Most human tumors result from the accumulation of multiple genetic and epigenetic alterations in a single cell. Mutations that confer a fitness advantage to the cell are known as driver mutations and are causally related to tumorigenesis. Other mutations, however, do not change the phenotype of the cell or even decrease cellular fitness. While much experimental effort is being devoted to the identification of the different functional effects of individual mutations, mathematical modeling of tumor progression generally considers constant fitness increments as mutations are accumulated. In this paper we study a mathematical model of tumor progression with random fitness increments. We analyze a multi-type branching process in which cells accumulate mutations whose fitness effects are chosen from a distribution. We determine the effect of the fitness distribution on the growth kinetics of the tumor. This work contributes to a quantitative understanding of the accumulation of mutations leading to cancer phenotypes.Comment: 33 pages, 2 Figure