Spin Qubits in Multi-Electron Quantum Dots

We study the effect of mesoscopic fluctuations on the magnitude of errors that can occur in exchange operations on quantum dot spin-qubits. Mid-size double quantum dots, with an odd number of electrons in the range of a few tens in each dot, are investigated through the constant interaction model using realistic parameters. It is found that the constraint of having short pulses and small errors implies keeping accurate control, at the few percent level, of several electrode voltages. In practice, the number of independent parameters per dot that one should tune depends on the configuration and ranges from one to four.Comment: RevTex, 6 pages, 5 figures. v3: two figures added, more details provided. Accepted for publication in PR

Universal Quantum Degeneracy Point for Superconducting Qubits

The quantum degeneracy point approach [D. Vion et al., Science 296, 886 (2002)] effectively protects superconducting qubits from low-frequency noise that couples with the qubits as transverse noise. However, low-frequency noise in superconducting qubits can originate from various mechanisms and can couple with the qubits either as transverse or as longitudinal noise. Here, we present a quantum circuit containing a universal quantum degeneracy point that protects an encoded qubit from arbitrary low-frequency noise. We further show that universal quantum logic gates can be performed on the encoded qubit with high gate fidelity. The proposed scheme is robust against small parameter spreads due to fabrication errors in the superconducting qubits.Comment: 7 pages, 4 figure

Analytical Solution of Electron Spin Decoherence Through Hyperfine Interaction in a Quantum Dot

We analytically solve the {\it Non-Markovian} single electron spin dynamics due to hyperfine interaction with surrounding nuclei in a quantum dot. We use the equation-of-motion method assisted with a large field expansion, and find that virtual nuclear spin flip-flops mediated by the electron contribute significantly to a complete decoherence of transverse electron spin correlation function. Our results show that a 90% nuclear polarization can enhance the electron spin $T_2$ time by almost two orders of magnitude. In the long time limit, the electron spin correlation function has a non-exponential $1/t^2$ decay in the presence of both polarized and unpolarized nuclei.Comment: 4 pages, 3 figure

Spin swap gate in the presence of qubit inhomogeneity in a double quantum dot

We study theoretically the effects of qubit inhomogeneity on the quantum logic gate of qubit swap, which is an integral part of the operations of a quantum computer. Our focus here is to construct a robust pulse sequence for swap operation in the simultaneous presence of Zeeman inhomogeneity for quantum dot trapped electron spins and the finite-time ramp-up of exchange coupling in a double dot. We first present a geometric explanation of spin swap operation, mapping the two-qubit operation onto a single-qubit rotation. We then show that in this geometric picture a square-pulse-sequence can be easily designed to perform swap in the presence of Zeeman inhomogeneity. Finally, we investigate how finite ramp-up times for the exchange coupling $J$ negatively affect the performance of the swap gate sequence, and show how to correct the problems numerically.Comment: published versio

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction