A surrogate accelerated multicanonical Monte Carlo method for uncertainty quantification

In this work we consider a class of uncertainty quantification problems where the system performance or reliability is characterized by a scalar parameter $y$. The performance parameter $y$ is random due to the presence of various sources of uncertainty in the system, and our goal is to estimate the probability density function (PDF) of $y$. We propose to use the multicanonical Monte Carlo (MMC) method, a special type of adaptive importance sampling algorithm, to compute the PDF of interest. Moreover, we develop an adaptive algorithm to construct local Gaussian process surrogates to further accelerate the MMC iterations. With numerical examples we demonstrate that the proposed method can achieve several orders of magnitudes of speedup over the standard Monte Carlo method

Enabling Full-Stack Quantum Computing with Changeable Error-Corrected Qubits

Executing quantum applications with quantum error correction (QEC) faces the gate non-universality problem imposed by the Eastin-Knill theorem. As one resource-time-efficient solution, code switching changes the encoding of logical qubits to implement universal logical gates. Unfortunately, it is still unclear how to perform full-stack fault-tolerant quantum computing (FTQC) based on the changeable logical qubit. Specifically, three critical problems remain unsolved: a) how to implement the dynamic logical qubit on hardware; b) how to determine the appropriate timing for logical qubit varying; c) how to improve the overall system performance for programs of different features. To overcome those design problems, We propose CECQ, to explore the large design space for FTQC based on changeable logical qubits. Experiments on various quantum programs demonstrate the effectiveness of CECQ

Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data

Identifying parameters of computational models from experimental data, or model calibration, is fundamental for assessing and improving the predictability and reliability of computer simulations. In this work, we propose a method for Bayesian calibration of models that predict morphological patterns of diblock copolymer (Di-BCP) thin film self-assembly while accounting for various sources of uncertainties in pattern formation and data acquisition. This method extracts the azimuthally-averaged power spectrum (AAPS) of the top-down microscopy characterization of Di-BCP thin film patterns as summary statistics for Bayesian inference of model parameters via the pseudo-marginal method. We derive the analytical and approximate form of a conditional likelihood for the AAPS of image data. We demonstrate that AAPS-based image data reduction retains the mutual information, particularly on important length scales, between image data and model parameters while being relatively agnostic to the aleatoric uncertainties associated with the random long-range disorder of Di-BCP patterns. Additionally, we propose a phase-informed prior distribution for Bayesian model calibration. Furthermore, reducing image data to AAPS enables us to efficiently build surrogate models to accelerate the proposed Bayesian model calibration procedure. We present the formulation and training of two multi-layer perceptrons for approximating the parameter-to-spectrum map, which enables fast integrated likelihood evaluations. We validate the proposed Bayesian model calibration method through numerical examples, for which the neural network surrogate delivers a fivefold reduction of the number of model simulations performed for a single calibration task

Compilation for Quantum Computing on Chiplets

Chiplet architecture is an emerging architecture for quantum computing that could significantly increase qubit resources with its great scalability and modularity. However, as the computing scale increases, communication between qubits would become a more severe bottleneck due to the long routing distances. In this paper, we trade ancillary qubits for program concurrency by proposing a multi-entry communication highway mechanism, and building a compilation framework to efficiently manage and utilize the highway resources. Our evaluation shows that this framework significantly outperforms the baseline approach in both the circuit depth and the number of operations on some typical quantum benchmarks, leading to a more efficient and less error-prone compilation of quantum programs

Ti₃C₂ MXene-based Schottky Photocathode for Enhanced Photoelectrochemical Sensing

Nanomaterials are vital to the realization of photoelectrochemical (PEC) sensing platfrom that provides the sensitive detection and quantification of low-abundance biological samples. Here, this work reports a Schottky junction-based BiOI/Ti₃C₂ heterostructure, used as a photocathode for PEC bioanalysis. Specially, we realize in situ growth of flower-like BiOI on 2D intrinsically negatively charged Ti₃C₂ MXene nanosheet that endows BiOI/Ti₃C₂ heterostructure with admirably combined merits, noting in particular the generation of built-in electric field and the decrease of contact resistance between BiOI and Ti₃C₂. Under the visible light irradiation, the BiOI/Ti₃C₂ heterostructure-modified PEC platform displays superior cathodic photocurrent signal, while PEC response cuts down with the presence of L-Cysteine (L-Cys) as a representative analyte owing to the metal-S bond formation. The “signal-off” PEC sensing strategy shows good performance in terms of sensitivity, limit of detection (LOD, 0.005 nM) and stability. This research reveals the great potentials of MXene-based heterostructure in the application field of PEC sensor establishment

Nitrogen rather than streamflow regulates the growth of riparian trees

In arid and semiarid regions, riparian forests are crucial for maintaining ecological biodiversity and sustainability, and supporting social and economic development. For the typical arid and semiarid ecosystem, streamflow variability is thought to be the dominant factor influencing the vulnerability and evolution of the riparian forests, which often leads to the neglect of other potentially important factors such as nutrient availability and transport. Here, we measured annual stable nitrogen isotopes (δ15N) and nitrogen concentrations (N%) in the tree rings of Populus euphratica Oliv. (Euphrates poplar) over a 90 year period (1920–2012), collected from the lower researches of the inland Heihe River, northwestern China. Coupling with our previous dual-isotope (δ13C and δ18O) chronologies and estimated intrinsic water-use efficiency (iWUE), we examined the linkages between tree-ring δ15N and δ18O, iWUE, streamflow, and then explored the contributions of each to tree growth during the study period. Our results show that after 1975, a statistically significant correlation between tree-ring δ15N and river streamflow appears, indicating the river as a potential carrier of nitrogen from the upper and middle reaches to the lower research trees. In addition, the linkage between tree-ring δ15N and iWUE suggests substantial influence of carbon and nitrogen together on photosynthesis and transpiration of trees, although this connection become decoupled since AD 1986. The commonality analysis revealed that the nitrogen impacts indicated by tree-ring δ15N on tree growth cannot be ignored when evaluating riparian forest development. The fertilization effects caused by rising CO2 concentration complicate the nitrogen constraints on tree growth during the later part of the past century. Our results have potentially broad implications for identifying the limited factors for dryland forest ecosystems that are susceptible to natural water resource variations and human activities