Fundamental thresholds of realistic quantum error correction circuits from classical spin models

Mapping the decoding of quantum error correcting (QEC) codes to classical disordered statistical mechanics models allows one to determine critical error thresholds of QEC codes under phenomenological noise models. Here, we extend this mapping to admit realistic, multi-parameter noise models of faulty QEC circuits, derive the associated strongly correlated classical spin models, and illustrate this approach for a quantum repetition code with faulty stabilizer readout circuits. We use Monte-Carlo simulations to study the resulting phase diagram and benchmark our results against a minimum-weight perfect matching decoder. The presented method provides an avenue to assess fundamental thresholds of QEC circuits, independent of specific decoding strategies, and can thereby help guiding the development of near-term QEC hardware.

Graph coloring using the reduced quantum genetic algorithm

Genetic algorithms (GA) are computational methods for solving optimization problems inspired by natural selection. Because we can simulate the quantum circuits that implement GA in different highly configurable noise models and even run GA on actual quantum computers, we can analyze this class of heuristic methods in the quantum context for NP-hard problems. This paper proposes an instantiation of the Reduced Quantum Genetic Algorithm (RQGA) that solves the NP-hard graph coloring problem in O(N1/2). The proposed implementation solves both vertex and edge coloring and can also determine the chromatic number (i.e., the minimum number of colors required to color the graph). We examine the results, analyze the algorithm convergence, and measure the algorithm's performance using the Qiskit simulation environment. Our Reduced Quantum Genetic Algorithm (RQGA) circuit implementation and the graph coloring results show that quantum heuristics can tackle complex computational problems more efficiently than their conventional counterparts.

Semi-empiric noise modeling of a Cargo eVTOL UAV by means of system identification from flight noise measurement data

This publication shows the semi-empiric noise modeling of an electric-powered vertical takeoff and landing (eVTOL) unmanned aerial vehicle (UAV) by means of system identification from flight noise measurement data. This work aims to provide noise models with a compact analytical ansatz for horizontal and vertical flight which are suited for integration into a geographical information system. Therefore, flight noise measurement campaigns were conducted and evaluated. An existing noise model ansatz is adapted to the eVTOL UAV under consideration and noise models are computed from the measurement data using the output error method. The resulting models are checked for plausibility by comparing them to technical literature. The horizontal flight noise model is subjected to a correlation analysis and the influence of meteorological effects are examined. To achieve a higher level of accuracy in future noise modelings, an optimization of the microphone positions as well as the flight trajectory is carried out.

A quantum Hopfield associative memory implemented on an actual quantum processor

In this work, we present a Quantum Hopfield Associative Memory (QHAM) and demonstrate its capabilities in simulation and hardware using IBM Quantum Experience.. The QHAM is based on a quantum neuron design which can be utilized for many different machine learning applications and can be implemented on real quantum hardware without requiring mid-circuit measurement or reset operations. We analyze the accuracy of the neuron and the full QHAM considering hardware errors via simulation with hardware noise models as well as with implementation on the 15-qubit ibmq_16_melbourne device. The quantum neuron and the QHAM are shown to be resilient to noise and require low qubit overhead and gate complexity. We benchmark the QHAM by testing its effective memory capacity and demonstrate its capabilities in the NISQ-era of quantum hardware. This demonstration of the first functional QHAM to be implemented in NISQ-era quantum hardware is a significant step in machine learning at the leading edge of quantum computing.

Traffic noise models and noise guidelines: A review

Traffic noise is a major cause of noise pollution, and impacts of noise pollution on humans have been studied extensively. Previous studies have helped in the development of traffic noise prediction analysis using robust analytical and computational models, parameters connected with traffic, road characteristics, and environment. This study reviewed 11 traffic noise modeling strategies and parameters used by various agencies in different parts of the world. Seven out of the 11 models reviewed in this paper were based on outdoor measurements while the remaining four were based on outdoor and indoor measurements. Considering the cost and time involved in developing these models, there is need to understand existing traffic noise models, differences, and assumptions before adopting or recalibrating them for local demands. This study contributes to the larger body of knowledge and intends to serve as a reference material for future researchers in the area of traffic noise modeling and techniques.

Spatial Variations of Stochastic Noise Properties in GPS Time Series

The noise in position time series of 568 GPS (Global Position System) stations across North America with an observation span of ten years has been investigated using solutions from two processing centers, namely, the Pacific Northwest Geodetic Array (PANGA) and New Mexico Tech (NMT). It is well known that in the frequency domain, the noise exhibits a power-law behavior with a spectral index of around −1. By fitting various noise models to the observations and selecting the most likely one, we demonstrate that the spectral index in some regions flattens to zero at long periods while in other regions it is closer to −2. This has a significant impact on the estimated linear rate since flattening of the power spectral density roughly halves the uncertainty of the estimated tectonic rate while random walk doubles it. Our noise model selection is based on the highest log-likelihood value, and the Akaike and Bayesian Information Criteria to reduce the probability of over selecting noise models with many parameters. Finally, the noise in position time series also depends on the stability of the monument on which the GPS antenna is installed. We corroborate previous results that deep-drilled brace monuments produce smaller uncertainties than concrete piers. However, if at each site the optimal noise model is used, the differences become smaller due to the fact that many concrete piers are located in tectonic/seismic quiet areas. Thus, for the predicted performance of a new GPS network, not only the type of monument but also the noise properties of the region need to be taken into account.