Current quantum devices face challenges when dealing with large circuits due to error rates as circuit size and the number of qubits increase. The circuit wire-cutting technique addresses this issue by breaking down a large circuit into smaller, more manageable subcircuits. However, the exponential increase in the number of subcircuits and the complexity of reconstruction as more cuts are made poses a great practical challenge. Inspired by ancilla-assisted quantum process tomography and the MUBs-based grouping technique for simultaneous measurement, we propose a new approach that can reduce subcircuit running overhead. The approach first uses ancillary qubits to transform all quantum input initializations into quantum output measurements. These output measurements are then organized into commuting groups for the purpose of simultaneous measurement, based on MUBs-based grouping. This approach significantly reduces the number of necessary subcircuits as well as the total number of shots. Lastly, we provide numerical experiments to demonstrate the complexity reduction.

Classical shadow tomography is a sample-efficient technique for characterizing quantum systems and predicting many of their properties. Circuit cutting is a technique for dividing large quantum circuits into smaller fragments that can be executed more robustly using fewer quantum resources. We introduce a divide-and-conquer circuit cutting method for estimating the expectation values of observables using classical shadows. We derive a general formula for making predictions using the classical shadows of circuit fragments from arbitrarily cut circuits, and provide the sample complexity analysis for the case when observables factorize across fragments. Then, we numerically show that our divide-and-conquer method outperforms traditional uncut shadow tomography when estimating high-weight observables that act non-trivially on many qubits, and discuss the mechanisms for this advantage.

Quantum circuit cutting has emerged as a promising method for simulating large quantum circuits using a collection of small quantum machines. Running low-qubit circuit “fragments” not only overcomes the size limitation of near-term hardware, but it also increases the fidelity of the simulation. However, reconstructing measurement statistics requires computational resources-both classical and quantum-that grow exponentially with the number of cuts. In this manuscript, we introduce the concept of a golden cutting point, which identifies unnecessary basis components during reconstruction and avoids related downstream computation. We propose a hypothesis-testing scheme for identifying golden cutting points, and provide robustness results in the case of the test failing with low probability. Lastly, we demonstrate the applicability of our method on Qiskit's Aer simulator and observe a reduced wall time from identifying and avoiding obsolete measurements.

Many natural and engineered systems can be modeled as discrete state Markov processes. Often, only a subset of states are directly observable. Inferring the conditional probability that a system occupies a particular hidden state, given the partial observation, is a problem with broad application. In this paper, we introduce a continuous-time formulation of the sum-product algorithm, which is a well-known discrete-time method for finding the hidden states' conditional probabilities, given a set of finite, discrete-time observations. From our new formulation, we can explicitly solve for the conditional probability of occupying any state, given the transition rates and observations within a finite time window. We apply our algorithm to a realistic model of the cystic fibrosis transmembrane conductance regulator (CFTR) protein for exact inference of the conditional occupancy probability, given a finite time series of partial observations.

Statistical correlations that can be generated across the nodes in a quantum network depend crucially on its topology. However, this topological information might not be known a priori, or it may need to be verified. In this paper, we propose an efficient protocol for distinguishing and inferring the topology of a quantum network. We leverage entropic quantities -- namely, the von Neumann entropy and the measured mutual information -- as well as measurement covariance to uniquely characterize the topology. We show that the entropic quantities are sufficient to distinguish two networks that prepare GHZ states. Moreover, if qubit measurements are available, both entropic quantities and covariance can be used to infer the network topology without state-preparation assumptions. We show that the protocol can be entirely robust to noise and can be implemented via quantum variational optimization. Numerical experiments on both classical simulators and quantum hardware show that covariance is generally more reliable for accurately and efficiently inferring the topology, whereas entropy-based methods are often better at identifying the absence of entanglement in the low-shot regime.

At present, quantum computing is in the noisy intermediate-scale quantum (NISQ) era, marked by small qubit counts and high levels of noise and errors. Building a quantum computer with sufficient size and low error rates remains a challenge. In many promising quantum hardware architectures, the state of the physical qubits is controlled by pulse signals. In this paper, we will explore pulse-level control of quantum gates. Unlike the usual gate-level control, the pulse-level control provides increased flexibility and reduced latency. One direct application of pulse-level control is Variational Quantum Algorithms (VQA). The inherent properties of VQA allow us to disregard the gate-based evolution process and concentrate on the final target loss function. From the perspective of pulse-level control, we can generate a sequence of pulse-based gates to rotate the quantum state directly to the desired destination. In this study, we demonstrate an application of pulse-level VQA in estimating the ground state energy of molecular hydrogen. Our experiment is conducted using Quanlse which specializes in pulse-level control of quantum gates. The experimental results reveal a rapid convergence rate of optimization iterations, and the control pulses for each pulse-based gate is also displayed. These results highlight the considerable potential of pulse-level control techniques in practical applications.

Quantum circuit cutting has been proposed to help execute large quantum circuits using only small and noisy machines. Intuitively, cutting a qubit wire can be thought of as classically passing information of a quantum state along each element in a basis set. As the number of cuts increase, the number of quantum degrees of freedom needed to be passed through scales exponentially. We propose a simple reduction scheme to lower the classical and quantum resources required to perform a cut. Particularly, we recognize that for some cuts, certain basis element might pass "no information" through the qubit wire and can effectively be neglected. We empirically demonstrate our method on circuit simulators as well as IBM quantum hardware, and we observed up to 33 percent reduction in wall time without loss of accuracy.

Despite the promise for performance and accuracy improvements of quantum inspired (QI) algorithms over classical machine learning (ML) algorithms, such gains have not been realized in practice. The quantum inspired algorithms can theoretically achieve significant speed up based on sampling assumptions and have thus far failed to outperform the existing classical ML models in practical applications. The speedup of quantum machine learning (QML) algorithms assume the access to data in quantum random access memory (QRAM) which is a strong assumption with current quantum architectures. QI algorithms assume sample and query (SQ) access to input vector and norms of matrices using a dynamic data structure. We explore the components of these models and the assumptions in this paper by surveying the recent works in QML and QI Machine learning (QIML) algorithms. We limit our study to QML and QIML models on achieving a speed up over classical ML techniques rather than individual proofs of these algorithms. This study highlights the assumptions being made that are currently not practical for QML and QIML algorithms in achieving performance advantage over classical ML algorithms.

Current and imminent quantum hardware lacks reliability and applicability due to noise and limited qubit counts. Quantum circuit cutting — a technique dividing large quantum circuits into smaller subcircuits with sizes appropriate for the limited quantum resource at hand — is used to mitigate these problems. However, classical postprocessing involved in circuit cutting generally grows exponentially with the number of cuts and quantum counts. This article introduces the notion of approximate circuit reconstruction. Using a sampling-based method like Markov Chain Monte Carlo (MCMC), we probabilistically select bit strings of high probability upon reconstruction. This avoids excessive calculations when reconstructing the full probability distribution. Our results show that such a sampling-based postprocessing method holds great potential for fast and reliable circuit reconstruction in the NISQ era and beyond.

In the past decade, remarkable progress has been achieved in deep learning related systems and applications. In the post Moore's Law era, however, the limit of semiconductor fabrication technology along with the increasing data size have slowed down the development of learning algorithms. In parallel, the fast development of quantum computing has pushed it to the new ear. Google illustrates quantum supremacy by completing a specific task (random sampling problem), in 200 seconds, which is impracticable for the largest classical computers. Due to the limitless potential, quantum based learning is an area of interest, in hopes that certain systems might offer a quantum speedup. In this work, we propose a novel architecture QuClassi, a quantum neural network for both binary and multi-class classification. Powered by a quantum differentiation function along with a hybrid quantum-classic design, QuClassi encodes the data with a reduced number of qubits and generates the quantum circuit, pushing it to the quantum platform for the best states, iteratively. We conduct intensive experiments on both the simulator and IBM-Q quantum platform. The evaluation results demonstrate that QuClassi is able to outperform the state-of-the-art quantum-based solutions, Tensorflow-Quantum and QuantumFlow by up to 53.75% and 203.00% for binary and multi-class classifications. When comparing to traditional deep neural networks, QuClassi achieves a comparable performance with 97.37% fewer parameters.

Huanglongbing (HLB or Citrus Greening Disease) is a destructive citrus disease that has impacted the U.S. citrus industry as well as global citrus industry for many years. There is yet to be a practical cure for HLB. Currently, there are only temporary and costly solutions of applying insecticides to target the psyllid vector and burning down the infected trees. Here we investigated the protein and phosphoprotein differences between control (healthy) and diseased citrus leaves to identify potential biomarkers essential towards fighting HLB within the trees themselves. Proteins were extracted from control and diseased leaves, followed by trypsin digestion, phosphopeptide enrichment and label-free quantitative proteomics. In total, we identified 1539 proteins and 278 phosphoproteins, among which 63 proteins and 23 phosphoproteins exhibited significant changes between control and diseased samples. Proteins in response to stimulus were increased at both total protein level and phosphorylation level. Interestingly, proteins related to membranes and complexes were decreased, but increased at phosphorylation level. Several differential phosphoproteins (e.g., a nuclear pore complex protein and a glutathione-S-transferase) could play important roles in battling the devastating disease HLB, one that we can hopefully conquer in the near future.

Natamycin is a new postharvest biofungicide for citrus and some other fruit crops in the United States that can be effectively used in recycling drench or flooder treatments. These applications necessitate sanitation of the fungicide solution to ensure that it remains free from contamination by bacteria that are potentially human pathogens. During in vitro experiments, heated (48°C) citric acid (1,100 or 2,200 μg/ml) amended with sodium dodecylbenzenesulfonate (SDBS) (60 or 120 μg/ml, respectively) significantly reduced the viability of a nonpathogenic strain of Escherichia coli in natamycin solutions by >5 log10 compared with the control. During laboratory studies with Penicillium digitatum-inoculated lemon fruit, 1,000 μg/ml of natamycin mixed with 1,000 μg/ml of lactic acid or citric acid and with or without SDBS (55 μg/ml) effectively and significantly reduced green mold. Natamycin mixed with lactic acid at ≥2,000 μg/ml, however, caused fruit injury, resulting in browning and rind pitting. Natamycin was incompatible with peroxyacetic acid, resulting in reduced efficacy against green mold. Sodium hypochlorite mixed with natamycin lost its toxicity to E. coli; however, the performance of natamycin was not affected. With heated (average 49°C) drench treatments on an experimental packing line, natamycin (1,000 μg/ml), fludioxonil (300 μg/ml), or azoxystrobin (300 μg/ml) mixed with citric acid (1,000 μg/ml) and SDBS (55 μg/ml) were effective against green mold without fruit injury. At a pH between 3.6 and 3.8, citric acid-SDBS significantly reduced the viability of E. coli by approximately 4 log10 in mixtures with fludioxonil or azoxystrobin, but not with natamycin. However, natamycin at 1,000 μg/ml mixed with 2,000 μg/ml of citric acid and SDBS (55 μg/ml) significantly reduced E. coli counts by >4 log10 within 4 min when the pH was maintained between 3.0 and 3.3, and the efficacy of the fungicide was retained. The use of citric acid with a surfactant can be a viable alternative sanitation method for natamycin in citrus packinghouses utilizing heated recirculating fungicide systems.

The growth of need of quantum computers in many domains such as machine learning, numerical scientific simulation and finance has urged the quantum computers to produce more stable and less error-prone results. However, to mitigate the impact of the noise inside each quantum device remains a present challenge. In this paper, we investigate the temporal behavior of noisy intermediate-scale quantum (NISQ) computer errors based on calibration data and the characteristics of individual devices. In particular, we collect calibration data of IBM-Q machines over 90 days and compare the quantum error robustness across processor types, quantum topology and quantum volumes. We analyze the aging effect by comparing the quantum error data from four IBM quantum computers during 2019-2021 showing that only one computer experienced significant error growth overtime. In this study, we simply analyze the collected data and run temporal test analysis to observe the behaviour of IBM-Q computers.

Fleas are carriers for many largely understudied zoonotic, endemic, emerging, and re-emerging infectious disease agents, but little is known about their prevalence and role as a vector in Africa. The aim of this study was to determine the diversity of fleas and the prevalence of infectious agents in them collected from human dwellings in western Kenya. A total of 306 fleas were collected using light traps from 33 human dwellings; 170 (55.56%) were identified as Ctenocephalides spp., 121 (39.54%) as Echidnophaga gallinacea, 13 (4.25%) as Pulex irritans, and 2 (0.65%) as Xenopsylla cheopis. Of the 306 individual fleas tested, 168 (54.9%) tested positive for rickettsial DNA by a genus-specific quantitative real-time PCR (qPCR) assay based on the 17-kDa antigen gene. Species-specific qPCR assays and sequencing revealed presence of Rickettsia asembonensis in 166 (54.2%) and Rickettsia felis in 2 (0.7%) fleas. Borrelia burgdorferi, normally known to be carried by ticks, was detected in four (1.3%) flea DNA preparations. We found no evidence of Yersinia pestis, Bartonella spp., or Orientia spp. Not only were Ctenocephalides spp. the most predominant flea species in the human dwellings, but also almost all of them were harboring R. asembonensis.

Tremendous progress has been witnessed in artificial intelligence where neural network backed deep learning systems have been used, with applications in almost every domain. As a representative deep learning framework, Generative Adversarial Network (GAN) has been widely used for generating artificial images, text-to-image or image augmentation across areas of science, arts and video games. However, GANs are computationally expensive, sometimes computationally prohibitive. Furthermore, training GANs may suffer from convergence failure and modal collapse. Aiming at the acceleration of use cases for practical quantum computers, we propose QuGAN, a quantum GAN architecture that provides stable convergence, quantum-states based gradients and significantly reduced parameter sets. The QuGAN architecture runs both the discriminator and the generator purely on quantum state fidelity and utilizes the swap test on qubits to calculate the values of quantum-based loss functions. Built on quantum layers, QuGAN achieves similar performance with a 94.98% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms state-of-the-art quantum based GANs in the literature providing a 48.33% improvement in system performance compared to others attaining less than 0.5% in terms of similarity between generated distributions and original data sets. QuGAN code is released at https://github.com/yingmao/Quantum-Generative-Adversarial-Network

We probe non-equilibrium properties of an active bacterial bath through measurements of correlations of passive tracer particles and the response function of a driven, optically trapped tracer. These measurements demonstrate violation of the fluctuation-dissipation theorem and enable us to extract the power spectrum of the active stress fluctuations. In some cases, we observe 1/ √ ω scaling in the noise spectrum which we show can be derived from a theoretical model incorporating coupled stress, orientation, and concentration fluctuations of the bacteria.

Natamycin is a biofungicide that was registered in the United States in 2016 and approved in California in 2017 for postharvest use on citrus and stone fruits. It has been used as a food preservative for many decades, with no resistance ever observed to date. The objective of this study was to determine baseline sensitivities for mycelial growth of 43 to 72 isolates of seven postharvest pathogens to natamycin and the resistance potential of Penicillium digitatum. Mean effective concentrations to inhibit mycelial growth by 50% (EC50 values), as determined by the spiral gradient method, were 0.90 μg/ml for Alternaria alternata, 0.76 μg/ml for Botrytis cinerea, 3.20 μg/ml for Geotrichum citri-aurantii, 0.17 μg/ml for Monilinia fructicola, 1.54 μg/ml for P. digitatum, 1.14 μg/ml for P. expansum, and 0.48 μg/ml for Rhizopus stolonifer. Distributions of EC50 values for each pathogen were unimodal and mostly normal with no outliers detected. Natamycin was also inhibitory to spore germination with values for five of the species similar to those for mycelial growth. Microscopically, natamycin generally arrested spores at the pregermination swelling stage. Mass platings of a conidial mixture of 10 isolates of P. digitatum were inoculated on agar media with 2.5-log radial concentration gradients of natamycin or fludioxonil, and a conidial mixture of 10 isolates of G. citri-aurantii were plated on media amended with natamycin or propiconazole. No resistant isolates were observed for both species to natamycin or for G. citri-aurantii to propiconazole, whereas a resistance frequency of 4.5 × 10-6 to 3.1 × 10-6 was calculated for P. digitatum to fludioxonil. The wide spectrum of activity against different fungal pathogens and a low resistance potential support the registration of natamycin as a postharvest treatment and its integration into an integrated pest management program with other practices including sanitation and rotation of other fungicides with different modes of action.

Background Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expression between individual genes. Methods We proposed a method to transform RNA sequencing data into artificial image objects (AIOs) and applied convolutional neural network (CNN) algorithms to classify these AIOs. With the AIO technique, we considered each gene as a pixel in an image and its expression level as pixel intensity. Using the GSE96058 ( n = 2976), GSE81538 ( n = 405), and GSE163882 ( n = 222) datasets, we created AIOs for the subjects and designed CNN models to classify biomarker Ki67 and Nottingham histologic grade (NHG). Results With fivefold cross-validation, we accomplished a classification accuracy and AUC of 0.821 ± 0.023 and 0.891 ± 0.021 for Ki67 status. For NHG, the weighted average of categorical accuracy was 0.820 ± 0.012, and the weighted average of AUC was 0.931 ± 0.006. With GSE96058 as training data and GSE81538 as testing data, the accuracy and AUC for Ki67 were 0.826 ± 0.037 and 0.883 ± 0.016, and that for NHG were 0.764 ± 0.052 and 0.882 ± 0.012, respectively. These results were 10% better than the results reported in the original studies. For Ki67, the calls generated from our models had a better power for prediction of survival as compared to the calls from trained pathologists in survival analyses. Conclusions We demonstrated that RNA sequencing data could be transformed into AIOs and be used to classify Ki67 status and NHG with CNN algorithms. The AIO method could handle high-dimensional data with highly correlated variables, and there was no need for variable selection. With the AIO technique, a data-driven, consistent, and automation-ready model could be developed to classify biomarkers with RNA sequencing data and provide more efficient care for cancer patients.

Tremendous progress has been witnessed in artificial intelligence, where neural network backed deep learning systems have been used, with applications in almost every domain. As a representative deep learning framework, Generative Adversarial Network (GAN) has been widely used for generating artificial images, text-to-image or image augmentation across areas of science, arts and video games. However, GANs are computationally expensive, sometimes computationally prohibitive. Furthermore, training GANs may suffer from convergence failure and modal collapse. Aiming at the acceleration of use cases for practical quantum computers, we propose QuGAN, a quantum GAN architecture that provides stable convergence, quantum-states based gradients and significantly reduced parameter sets. The QuGANarchitecture runs both the discriminator and the generator purely on quantum state fidelity and utilizes the swap test on qubits to calculate the values of quantum-based loss functions. Built on quantum layers, QuGAN achieves similar performance with a 94.98% reduction on the parameter set when compared to classical GANs. With the same number of parameters, addition-ally, QuGAN outperforms state-of-the-art quantum based GANsin the literature providing a 48.33% improvement in system performance compared to others attaining less than 0.5% in terms of similarity between generated distributions and original data sets.

An enticing feature of active materials is the possibility of controlling macroscale rheological properties through the activity of the microscopic constituents. Using a unique combination of microscopy and rheology we study three dimensional microtubule-based active materials whose autonomous flows are powered by a continually rearranging connected network. We quantify the relationship between the microscopic dynamics and the bulk mechanical properties of these nonequilibrium networks. Experiments reveal a surprising nonmonotonic viscosity that strongly depends on the relative magnitude of the rate of internally generated activity and the externally applied shear. A simple two-state mechanical model that accounts for both the solidlike and yielded fluidlike elements of the network accurately describes the rheological measurements.