Post-variational quantum neural networks

Quantum computing has the potential to provide substantial computational advantages over current state-of-the-art classical supercomputers. However, current hardware is not advanced enough to execute fault-tolerant quantum algorithms. An alternative of using hybrid quantum-classical computing with variational algorithms can exhibit barren plateau issues, causing slow convergence of gradient-based optimization techniques. In this paper, we discuss "post-variational strategies", which shift tunable parameters from the quantum computer to the classical computer, opting for ensemble strategies when optimizing quantum models. We discuss various strategies and design principles for constructing individual quantum circuits, where the resulting ensembles can be optimized with convex programming. Further, we discuss architectural designs of post-variational quantum neural networks and analyze the propagation of estimation errors throughout such neural networks. Lastly, we show that our algorithm can be applied to real-world applications such as image classification on handwritten digits, producing a 96% classification accuracy.Comment: 17 pages, 9 figure

Dynamic Power Index Adjustment Based On Battery Level

This disclosure describes techniques for dynamic adjustment of output power index of a wireless remote controller device based on a detected battery level of the device. The battery voltage level of the device is periodically measured. When the level falls below a predetermined threshold, the output power index is adjusted to ensure that the total transmit power from the controller device lies within a specified range. Dynamic adjustment of transmit power via the power index adjustment enables the controller device to have a transmit power that lies between the power spectral distribution (PSD) target and the PSD limit (maximum) over a range of battery voltage values

Hybrid quantum-classical and quantum-inspired classical algorithms for solving banded circulant linear systems

Solving linear systems is of great importance in numerous fields. In particular, circulant systems are especially valuable for efficiently finding numerical solutions to physics-related differential equations. Current quantum algorithms like HHL or variational methods are either resource-intensive or may fail to find a solution. We present an efficient algorithm based on convex optimization of combinations of quantum states to solve for banded circulant linear systems whose non-zero terms are within distance $K$ of the main diagonal. By decomposing banded circulant matrices into cyclic permutations, our approach produces approximate solutions to such systems with a combination of quantum states linear to $K$, significantly improving over previous convergence guarantees, which require quantum states exponential to $K$. We propose a hybrid quantum-classical algorithm using the Hadamard test and the quantum Fourier transform as subroutines and show its PromiseBQP-hardness. Additionally, we introduce a quantum-inspired algorithm with similar performance given sample and query access. We validate our methods with classical simulations and actual IBM quantum computer implementation, showcasing their applicability for solving physical problems such as heat transfer.Comment: 21 pages, 12 figure

DEXON: A Highly Scalable, Decentralized DAG-Based Consensus Algorithm

A blockchain system is a replicated state machine that must be fault tolerant. When designing a blockchain system, there is usually a trade-off between decentralization, scalability, and security. In this paper, we propose a novel blockchain system, DEXON, which achieves high scalability while remaining decentralized and robust in the real-world environment. We have two main contributions. First, we present a highly scalable sharding framework for blockchain. This framework takes an arbitrary number of single chains and transforms them into the \textit{blocklattice} data structure, enabling \textit{high scalability} and \textit{low transaction confirmation latency} with asymptotically optimal communication overhead. Second, we propose a single-chain protocol based on our novel verifiable random function and a new Byzantine agreement that achieves high decentralization and low latency

Discovering Fuzzy Association Rules from Patient's Daily Text Messages to Diagnose Melancholia

With the constant stress from work load and daily life people may show symptoms of melancholia. However, most people are reluctant to describe it or may not know that they already have it. In this paper a novel system is proposed to discover clues from patient’s interaction with psychologist or from self-recorded voice or text messages. A user friendly interface is provided for patients to input text messages or record a voice file by mobile phones or other input devices. A speech-totext conversion software is used to convert voice mails to simple text files in advance. Based on the text files, a data mining model is used to discover frequent keywords mentioned in the text or speech files. The association rules can be used to help psychologists diagnose patients’ degree of melancholia. Experimental results show that the proposed system can effectively discover melancholia keywords