We apply the full power of modern electronic band structure engineering and epitaxial hetero-structures to design a transistor that can sense and control a single donor electron spin. Spin resonance transistors may form the technological basis for quantum information processing. One and two qubit operations are performed by applying a gate bias. The bias electric field pulls the electron wave function away from the dopant ion into layers of different alloy composition. Owing to the variation of the g-factor (Si:g=1.998, Ge:g=1.563), this displacement changes the spin Zeeman energy, allowing single-qubit operations. By displacing the electron even further, the overlap with neighboring qubits is affected, which allows two-qubit operations. Certain Silicon-Germanium alloys allow a qubit spacing as large as 200 nm, which is well within the capabilities of current lithographic techniques. We discuss manufacturing limitations and issues regarding scaling up to a large size computer.

High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks.H igh-throughput techniques in biology, such as DNA microarray (1), have generated a large amount of data that can potentially provide systems-level information regarding the underlying dynamics and mechanisms. These high-dimensional output data are typically the end products of low-dimensional regulatory signals driven through an interacting network. As illustrated in Fig. 1, the relationship between the lower dimensional regulatory signals (or states) and output data can be modeled by a bipartite networked system, where the output signals (e.g., gene expression levels) are generated by weighted functions of the intracellular states (e.g., the activity of the transcription factors). A major challenge in systems biology is to derive methodologies for simultaneous reconstructions of the hidden dynamics of the regulatory signals.In recent years, statistical techniques for determining lowdimensional representations of high-dimensional data sets, e.g., principal component analysis (PCA) (2) or singular value decomposition (3-5) and independent component analysis (ICA) (6), have been applied successfully to deduce biologically significant information from high-throughput data sets. It is important to recognize that such dimensionality reduction techniques are not designed to address the hidden dynamics reconstruction problem addressed in this article. For example, PCA and ICA both would generate linear networks for interpreting the observed data set, where t...

To exploit a heterogeneous computing (HC) environment, an application task may be decomposed into subtasks that have data dependencies. Subtask matching and scheduling consists of assigning subtasks to machines, ordering subtask execution for each machine, and ordering intermachine data transfers. The goal is to achieve the minimal completion time for the task. A heuristic approach based on a genetic algorithm is developed to do matching and scheduling in HC environments. It is assumed that the matcher/scheduler is in control of a dedicated HC suite of machines. The characteristics of this genetic-algorithm-based approach include: separation of the matching and the scheduling representations, independence of the chromosome structure from the details of the communication subsystem, and consideration of overlap among all computations and communications that obey subtask precedence constraints. It is applicable to the static scheduling of production jobs and can be readily used to collectively schedule a set of tasks that are decomposed into subtasks. Some parameters and the selection scheme of the genetic algorithm were chosen experimentally to achieve the best performance. Extensive simulation tests were conducted. For small-sized problems (e.g., a small number of subtasks and a small number of machines), exhaustive searches were used to verify that this genetic-algorithm-based approach found the optimal solutions. Simulation results for larger-sized problems showed that this genetic-algorithm-based approach outperformed two nonevolutionary heuristics and a random Search.

We present here algorithmic cooling (via polarization heat bath)-a powerful method for obtaining a large number of highly polarized spins in liquid nuclear-spin systems at finite temperature. Given that spin-half states represent (quantum) bits, algorithmic cooling cleans dirty bits beyond the Shannon's bound on data compression, by using a set of rapidly thermal-relaxing bits. Such auxiliary bits could be implemented by using spins that rapidly get into thermal equilibrium with the environment, e.g., electron spins. Interestingly, the interaction with the environment, usually a most undesired interaction, is used here to our benefit, allowing a cooling mechanism. Cooling spins to a very low temperature without cooling the environment could lead to a breakthrough in NMR experiments, and our ''spin-refrigerating'' method suggests that this is possible. The scaling of NMR ensemble computers is currently one of the main obstacles to building larger-scale quantum computing devices, and our spin-refrigerating method suggests that this problem can be resolved.

We study entanglement in Valence-Bond-Solid state, which describes the ground state of an AKLT quantum spin chain, consisting of bulk spin-1's and two spin-1/2's at the ends. We characterize entanglement between various subsystems of the ground state by mostly calculating the entropy of one of the subsystems; when appropriate, we evaluate concurrences as well. We show that the reduced density matrix of a continuous block of bulk spins is independent of the size of the chain and the location of the block relative to the ends. Moreover, we show that the entanglement of the block with the rest of the sites approaches a constant value exponentially fast, as the size of the block increases. We also calculate the entanglement of (i) any two bulk spins with the rest, and (ii) the end spin-1/2's (together and separately) with the rest of the ground state. For example, we show that (i) any two bulk spins become maximally entangled with the rest of the ground state exponentially fast in their separation distance, (ii) the two end spin-1/2's share no entanglement, and (iii) each end spin-1/2 is maximally entangled with the rest. There is considerable current interest in quantifying entanglement in various quantum systems. Entanglement in spin chains, correlated electrons, interacting bosons and other models was studied [3,4,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]. Entanglement is a fundamental measure of how much quantum effects we can observe and use, and it is the primary resource in quantum computation and quantum information processing [1,2]. Also entanglement plays a role in the quantum phase transitions [3,4], and it has been experimentally demonstrated that the entanglement may affect macroscopic properties of solids [5,6].In this Letter, we will study a spin chain introduced by Affleck, Kennedy, Lieb, and Tasaki (AKLT model) [28,29]. The ground state of the model is a unique pure state. It is known as Valence-Bond Solid (VBS), and plays a central role in condensed matter physics. Haldane [31] conjectured that an anti-ferromagnetic Hamiltonian describing half-odd-integer spins is gap-less, but for integer spins it has a gap. AKLT model describing interaction of spin-1's in the bulk agrees with the conjecture. An implementation of AKLT in optical lattices was proposed recently [32], and the use of AKLT model for universal quantum computation was discussed in [30]. VBS is also closely related to Laughlin ansatz [33] and to fractional quantum Hall effect [34].We investigate the seminal AKLT model from the new perspective of quantum information, and evaluate the entanglement (in terms of entropy) of various subsystems of the VBS. The results and the methodologies adopted herein have several implications from the perspective of both quantum information and condensed matter. For example, while the entanglement in spin chains with periodic boundary conditions has been studied extensively, our results provide entanglement calculations for spin chains with open boundary conditions. For critical gap-les...