# Browsing by Subject "Information theory"

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Item Open Access An information-theoretic analysis of spike processing in a neuroprosthetic model(2007-05-03T18:53:57Z) Won, Deborah S.Neural prostheses are being developed to provide motor capabilities to patients who suffer from motor-debilitating diseases and conditions. These brain-computer interfaces (BCI) will be controlled by activity from the brain and bypass damaged parts of the spinal cord or peripheral nervous system to re-establish volitional control of motor output. Spike sorting is a technologically expensive component of the signal processing chain required to interpret population spike activity acquired in a BCI. No systematic analysis of the need for spike sorting has been carried out and little is known about the effects of spike sorting error on the ability of a BCI to decode intended motor commands. We developed a theoretical framework and a modelling environment to examine the effects of spike processing on the information available to a BCI decoder. Shannon information theory was applied to simulated neural data. Results demonstrated that reported amounts of spike sorting error reduce mutual information (MI) significantly in single-unit spike trains. These results prompted investigation into how much information is available in a cluster of pooled signals. Indirect information analysis revealed the conditions under which pooled multi-unit signals can maintain the MI that is available in the corresponding sorted signals and how the information loss grows with dissimilarity of MI among the pooled responses. To reveal the differences in non-sorted spike activity within the context of a BCI, we simulated responses of 4 neurons with the commonly observed and exploited cosine-tuning property and with varying levels of sorting error. Tolerances of angular tuning differences and spike sorting error were given for MI loss due to pooling under various conditions, such as cases of inter- and/or intra-electrode differences and combinations of various mean firing rates and tuning depths. These analyses revealed the degree to which mutual information loss due to pooling spike activity depended upon differences in tuning between pooled neurons and the amount of spike error introduced by sorting. The theoretical framework and computational tools presented in this dissertation will BCI system designers to make decisions with an understanding of the tradeoffs between a system with and without spike sorting.Item Open Access Locally Adaptive Protocols for Quantum State Discrimination(2021) Brandsen, SarahThis dissertation makes contributions to two rapidly developing fields: quantum information theory and machine learning. It has recently been demonstrated that reinforcement learning is an effective tool for a wide variety of tasks in quantum information theory, ranging from quantum error correction to quantum control to preparation of entangled states. In this work, we demonstrate that reinforcement learning is additionally highly effective for the task of multiple quantum hypothesis testing.

Quantum hypothesis testing consists of finding the quantum measurement which allows one to discriminate with minimal error between $m$ possible states $\{\rho_{k}\}|_{k=1}^{m}$ of a quantum system with corresponding prior probabilities $p_{k} = \text{Pr}[\rho = \rho_{k}]$. In the general case, although semi-definite programming offers a way to numerically approximate the optimal solution~\cite{Eldar_Semidefinite2}, a closed-form analytical solution for the optimal measurement is not known.

Additionally, when the quantum system is large and consists of many subsystems, the optimal measurement may be experimentally difficult to implement. In this work, we provide a comprehensive study of locally adaptive approaches to quantum hypothesis testing where only a single subsystem is measured at a time and the order and types of measurements implemented may depend on previous measurement results. Thus, these locally adaptive protocols present an experimentally feasible approach to quantum state discrimination.

We begin with the case of binary hypothesis testing (where $m=2$), and generalize previous work by Acin et al. (Phys. Rev. A 71, 032338) to show that a simple Bayesian-updating scheme can optimally distinguish between any pair of arbitrary pure, tensor product quantum states. We then demonstrate that this same Bayesian-updating scheme has poor asymptotic behaviour when the candidate states are not pure, and based on this we introduce a modified scheme with strictly better performance. Finally, a dynamic programming (DP) approach is used to find the optimal local protocol for binary state discrimination and numerical simulations are run for both qubit and qutrit subsystems.

Based on these results, we then turn to the more general case of multiple hypothesis testing where there may be several candidate states. Given that the dynamic-programming approach has a high complexity when there are a large number of subsystems, we turn to reinforcement learning methods to learn adaptive protocols for even larger systems. Our numerical results support the claim that reinforcement learning with neural networks (RLNN) is able to successfully find the optimal locally adaptive approach for up to 20 subsystems. We additionally find the optimal collective measurement through semidefinite programming techniques, and demonstrate that the RLNN approach meets or comes close to the optimal collective measurement in every random trial.

Next, we focus on quantum information theory and provide an operational interpretation for the entropy of a channel. This task is motivated by the central role of entropy across several areas of physics and science. We use games of chance as a more systematic and unifying approach to define entropy, as a system's performance in any game of chance depends solely on the uncertainty of the output. We construct families of games which result in a pre-order on channels and provide an operational interpretation for all pre-orders (corresponding to majorization, conditional majorization, and channel majorization respectively), and this defines the unique asymptotically continuous entropy function for classical channels.