Combined Error Correction Techniques for Quantum Computing Architectures

Proposals for quantum computing devices are many and varied. They each have unique noise processes that make none of them fully reliable at this time. There are several error correction/avoidance techniques which are valuable for reducing or eliminating errors, but not one, alone, will serve as a panacea. One must therefore take advantage of the strength of each of these techniques so that we may extend the coherence times of the quantum systems and create more reliable computing devices. To this end we give a general strategy for using dynamical decoupling operations on encoded subspaces. These encodings may be of any form; of particular importance are decoherence-free subspaces and quantum error correction codes. We then give means for empirically determining an appropriate set of dynamical decoupling operations for a given experiment. Using these techniques, we then propose a comprehensive encoding solution to many of the problems of quantum computing proposals which use exchange-type interactions. This uses a decoherence-free subspace and an efficient set of dynamical decoupling operations. It also addresses the problems of controllability in solid state quantum dot devices.Comment: Contribution to Proceedings of the 2002 Physics of Quantum Electronics Conference", to be published in J. Mod. Optics. This paper provides a summary and review of quant-ph/0205156 and quant-ph/0112054, and some new result

Estimating Single-Channel Source Separation Masks: Relevance Vector Machine Classifiers vs. Pitch-Based Masking

Audio sources frequently concentrate much of their energy into a relatively small proportion of the available time-frequency cells in a short-time Fourier transform (STFT). This sparsity makes it possible to separate sources, to some degree, simply by selecting STFT cells dominated by the desired source, setting all others to zero (or to an estimate of the obscured target value), and inverting the STFT to a waveform. The problem of source separation then becomes identifying the cells containing good target information. We treat this as a classification problem, and train a Relevance Vector Machine (a probabilistic relative of the Support Vector Machine) to perform this task. We compare the performance of this classifier both against SVMs (it has similar accuracy but is not as efficient as RVMs), and against a traditional Computational Auditory Scene Analysis (CASA) technique based on a noise-robust pitch tracker, which the RVM outperforms significantly. Differences between the RVM- and pitch-tracker-based mask estimation suggest benefits to be obtained by combining both

A variational EM algorithm for learning eigenvoice parameters in mixed signals

We derive an efficient learning algorithm for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a priori. The sources are modeled using factor-analyzed hidden Markov models (HMM) where source specific characteristics are captured by an "eigenvoice" speaker subspace model. The proposed algorithm is able to learn adaptation parameters for two speech sources when only a mixture of signals is observed. We evaluate the algorithm on the 2006 speech separation challenge data set and show that it is significantly faster than our earlier system at a small cost in terms of performance

Response to Nature commentary “Clear up this stem-cell mess”

n/

Learning, Using, and Adapting Models in Scene Analysis

Discusses models of source behavior as the way to conquer uncertainty in mixtures

Monaural speech separation using source-adapted models

We propose a model-based source separation system for use on single channel speech mixtures where the precise source characteristics are not known a priori. We do this by representing the space of source variation with a parametric signal model based on the eigenvoice technique for rapid speaker adaptation. We present an algorithm to infer the characteristics of the sources present in a mixture, allowing for significantly improved separation performance over that obtained using unadapted source models. The algorithm is evaluated on the task defined in the 2006 Speech Separation Challenge [1] and compared with separation using source-dependent models

Multi-Pattern Visual Statistical Learning in Monolinguals and Bilinguals

To date, the impact of bilingualism on statistical learning remains unclear. Here we test a novel visual statistical learning task that affords simultaneous learning of two types of regularities: co-occurrence regularities between pairs of elements and the co-occurrence of visual features that could define categories. We compared performance by English monolinguals, Spanish-Catalan bilinguals and Spanish-English bilinguals, as previous studies have suggested that bilinguals might be more open than monolinguals to the presence of multiple regularities, though no previous studies have tested the learning of multiple patterns within a single task. We demonstrated that both monolingual and bilingual participants could learn the co-occurrence probabilities and the features that define categories. To the best of our knowledge, this study is the first to demonstrate that learners can extract co-occurrence regularities along two dimensions in the visual modality. However, we did not detect significant differences in performance across groups. We close by discussing the implications for the growing literature on bilingualism and statistical learning

Realtime Optimization of MPC Setpoints using Time-Varying Extremum Seeking Control for Vapor Compression Machines

Recently, model predictive control (MPC) has received increased attention in the HVAC community, largely due to its ability to systematically manage constraints while optimally regulating signals of interest to setpoints. For example, in a common formulation of an MPC control problem for variable compressor speed vapor compression machines, the setpoints often include the zone temperature and the evaporator superheat temperature. However, the energy consumption of vapor compression systems has been shown to be sensitive to these setpoints. Further, while superheat temperature is often preferred because it can be easily correlated to heat exchanger efficiency (and therefore cycle efficiency), direct measurement of superheat is not always available. Therefore, identifying alternate signals in the control of vapor compression machines that correlate to efficiency is desired. Conventionally, methods for maximizing the energy efficiency rely on the use of mathematical models of the physics of vapor compression systems. These model-based approaches attempt to describe the influence of commanded inputs on the thermodynamic behavior of the system and the consumed electrical energy, and they are used to predict the combination of inputs that both meet the heat load requirements and minimize energy consumption. However, these models of vapor compression systems rely on simplifying assumptions in order to produce a mathematically tractable representation. Further, they are difficult to derive and calibrate, and often do not describe variations over long time scales, such as those due to refrigerant losses or accumulation of debris on the heat exchangers. In this paper, we consider a model-free extremum seeking algorithm that adjusts setpoints provided to a model predictive controller. While perturbation-based extremum seeking methods have been known for some time, they suffer from slow convergence rates---a problem emphasized by the long time constants associated with thermal systems. Our method uses a new algorithm (time-varying extremum seeking), which has dramatically faster and more reliable convergence properties. In particular, we regulate the compressor discharge temperature using an MPC controller with setpoints selected from a model-free time-varying extremum seeking algorithm. We show that the relationship between compressor discharge temperature and power consumption is convex (a requirement for this class of realtime optimization), and use time-varying extremum seeking to drive these setpoints to values that minimize power. The results are compared to the traditional perturbation-based extremum seeking approach. Further, because the required cooling capacity (and therefore compressor speed) is a function of measured and unmeasured disturbances, the optimal compressor discharge temperature setpoint must vary according to these conditions. We show that the energy optimal discharge temperature is tracked with the time-varying extremum seeking algorithm in the presence of disturbances