Dynamics of magnetization at infinite temperature in a Heisenberg spin chain

Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain's center, [Formula: see text]. The first two moments of [Formula: see text] show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.

Stable quantum-correlated many-body states through engineered dissipation

Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.

Measurement-induced entanglement and teleportation on a noisy quantum processor

Measurement has a special role in quantum theory1: by collapsing the wavefunction, it can enable phenomena such as teleportation2 and thereby alter the 'arrow of time' that constrains unitary evolution. When integrated in many-body dynamics, measurements can lead to emergent patterns of quantum information in space-time3-10 that go beyond the established paradigms for characterizing phases, either in or out of equilibrium11-13. For present-day noisy intermediate-scale quantum (NISQ) processors14, the experimental realization of such physics can be problematic because of hardware limitations and the stochastic nature of quantum measurement. Here we address these experimental challenges and study measurement-induced quantum information phases on up to 70 superconducting qubits. By leveraging the interchangeability of space and time, we use a duality mapping9,15-17 to avoid mid-circuit measurement and access different manifestations of the underlying phases, from entanglement scaling3,4 to measurement-induced teleportation18. We obtain finite-sized signatures of a phase transition with a decoding protocol that correlates the experimental measurement with classical simulation data. The phases display remarkably different sensitivity to noise, and we use this disparity to turn an inherent hardware limitation into a useful diagnostic. Our work demonstrates an approach to realizing measurement-induced physics at scales that are at the limits of current NISQ processors.

Non-Abelian braiding of graph vertices in a superconducting processor

Indistinguishability of particles is a fundamental principle of quantum mechanics¹. For all elementary and quasiparticles observed to date-including fermions, bosons and Abelian anyons-this principle guarantees that the braiding of identical particles leaves the system unchanged^2,3. However, in two spatial dimensions, an intriguing possibility exists: braiding of non-Abelian anyons causes rotations in a space of topologically degenerate wavefunctions^4-8. Hence, it can change the observables of the system without violating the principle of indistinguishability. Despite the well-developed mathematical description of non-Abelian anyons and numerous theoretical proposals^9-22, the experimental observation of their exchange statistics has remained elusive for decades. Controllable many-body quantum states generated on quantum processors offer another path for exploring these fundamental phenomena. Whereas efforts on conventional solid-state platforms typically involve Hamiltonian dynamics of quasiparticles, superconducting quantum processors allow for directly manipulating the many-body wavefunction by means of unitary gates. Building on predictions that stabilizer codes can host projective non-Abelian Ising anyons^9,10, we implement a generalized stabilizer code and unitary protocol²³ to create and braid them. This allows us to experimentally verify the fusion rules of the anyons and braid them to realize their statistics. We then study the prospect of using the anyons for quantum computation and use braiding to create an entangled state of anyons encoding three logical qubits. Our work provides new insights about non-Abelian braiding and, through the future inclusion of error correction to achieve topological protection, could open a path towards fault-tolerant quantum computing.

Formation of robust bound states of interacting microwave photons

Systems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2-9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.

Prevalence of HIV, hepatitis B virus, and hepatitis C virus among incarcerated people in Iran: a systematic review and meta-analysis

OBJECTIVES

Incarcerated people are at higher risk for HIV, Hepatitis B Virus (HBV), and Hepatitis C Virus (HCV) infections. This review systematically summarized the evidence on the prevalence of these infections among incarcerated people in Iran.

STUDY DESIGN

A systematic review and meta-analysis.

METHODS

We searched Embase, PubMed, Web of Science, Scopus, PsychInfo, Iranian databases, including IranMedex, Magiran, Scientific Information Database (SID), and IranDoc. A grey literature review was conducted to find unpublished reports from the Ministry of Health and experts throughout the country. Included studies reported data on the prevalence of HIV, HBV, or HCV infections. A random-effects meta-analysis was performed to estimate the pooled prevalence. A meta-regression analysis was also conducted.

RESULTS

Of 1461 screened records, 23 records were eligible (total participants = 199,855). The pooled prevalence of HIV (17 studies), HBV (6 studies), and HCV (10 studies) was 2.77% (95% CI: 1.96, 3.70), 2.89% (95% CI: 2.28, 3.56), and 21.57% (95% CI: 13.62, 30.76), respectively. Meta-regression analyses showed that HIV (P-value = 0.05) and HCV (P-value = 0.02) were reduced over time using survey year as the interested variable in the model. Also, lifetime history of drug injection had a significant association with the HIV infection (P-value = 0.03).

CONCLUSION

The findings suggest that the prevalences of these infections are relatively considerable among Iranian incarcerated people. These findings support developing interventions to reduce the risk of the acquisition and circulation of these infections among incarcerated people, and continued harm reduction programs among most at-risk incarcerated people, as well as HCV treatment.

Sex out of marriage and condom use among homeless youth in Iran

OBJECTIVES

Sexual behaviors of homeless youth in Iran have not been well studied. This study aimed to measure the frequency and associated factors of sex out of marriage and condom use among homeless youth in Kerman, Iran.

STUDY DESIGN

In this cross-sectional study, we recruited 202 homeless youth (age: 15-29 years who experienced 30 or more days of homelessness in the last 12 months) from 11 street locations between September to December 2017.

METHODS

Of 202 participants, 169 (83.7%) reported sex in the last 12 months and were include in this analysis. We assessed the prevalence of sex out of marriage in the last 12 months, condom use in last sex, and then evaluated their covariates in multivariable logistic regression analysis.

RESULTS

The prevalence of sex out of marriage was 19.6% (95% confidence intervals [CIs]: 13.8%, 26.3%) and the prevalence of condom use was 43.8% (95% CI: 36.2%, 51.6%). Sex out of marriage was significantly correlated with male gender (adjusted odds ratio [AOR]: 24.38; 95% CI: 3.1-192.1) and being unmarried (AOR: 5.94; 95% CI: 2.3-15.5). Condom use was significantly correlated with male gender (AOR: 2.16; 95% CI: 1.1-4.2) and higher educational status (AOR: 4.30; 95% CI: 2.1-8.8).

CONCLUSION

Our findings indicate that one in five homeless youth had sex out of marriage, and less than half did no use condom. These should be addressed by adapting education and harm reduction programs targeting this specific population in Iran.

Color Doppler ultrasonography diagnostic value in detection of malignant nodules in cysts with pathologically proven thyroid malignancy: a systematic review and meta-analysis

AIMS

Today, the color Doppler ultrasonography is used to further evaluate suspected malignant tumors. This study investigates the malignant thyroid nodules using color Doppler.

METHODS

After extracting true positive, false positive, false negative, and true negative among included studies, a quality was evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (with 95% confidence interval) were found using a random effect model. Summary receiver operating characteristic curves (SROC) were used to assess relationship between sensitivity and specificity. The area under the curve of the SROC was calculated to estimate the performance of color Doppler ultrasound to distinguish malignant thyroid nodules. Our registration code in PROSPERO is CRD42018111198.

RESULTS

Of 1125 articles, 288 articles were selected for the further investigation. After excluding irrelevant and poor articles, 20 studies were included for the meta-analysis. According to a random effect model, the pooled sensitivity and specificity of color Doppler ultrasound to distinguish malignant thyroid nodules were estimated as 0.74 (95% CI 0.62-0.83; [Formula: see text]) and 0.70 (95% CI 0.56-0.81; [Formula: see text]), respectively. The SROC curve consists of representing the paired results for sensitivity and specificity. According to SROC, AUC = 0.78 (95% CI 0.74-0.81) is between 0.75 and 0.92, so that color Doppler ultrasound has a good accuracy.

CONCLUSION

Color Doppler is a valuable non-invasive method for evaluating thyroid nodules, and it is a high-sensitivity diagnostic tool for assessing thyroid nodules. Resistive index > 0.75 and a pattern III or more in color Doppler predicts malignant with the confidence. Due to its precision, cost-efficiency, easy access, and non-invasive nature, color Doppler should be included in the standard clinical protocol for the decision-making period and the treatment evaluation.

Prevalence of American foulbrood in asymptomatic apiaries of Kurdistan, Iran

AIM

Paenibacillus larvae subsp. larvae is the etiological agent of American foulbrood (AFB), the most virulent bacterial disease of honey bee brood worldwide. In many countries, AFB is a notifiable disease since it is highly contagious, in most cases incurable, and able to kill affected colonies. The aim of this study was to determine the prevalence of P. larvae subsp. larvae in Kurdistan province apiaries by polymerase chain reaction (PCR) technique.

MATERIALS AND METHODS

A total of 100 samples were randomly purchased from apiaries in Kurdistan, Iran. Apiaries were randomly sampled in accordance with the instructions of the veterinary organization from different provinces and were tested using PCR method and an exclusive primer of 16S rRNA for the presence of P. larvae subsp. larvae.

RESULTS

The results of this study indicated a low level of contamination with P. larvae subsp. larvae in the Kurdistan province. The number of positive samples obtained by PCR was 2%.

CONCLUSION

Therefore, monitoring programs for this honeybee disease in Kurdistan should be developed and implemented to ensure that it is detected early and managed.

Reliable emotion recognition system based on dynamic adaptive fusion of forehead biopotentials and physiological signals

In this study, we proposed a new adaptive method for fusing multiple emotional modalities to improve the performance of the emotion recognition system. Three-channel forehead biosignals along with peripheral physiological measurements (blood volume pressure, skin conductance, and interbeat intervals) were utilized as emotional modalities. Six basic emotions, i.e., anger, sadness, fear, disgust, happiness, and surprise were elicited by displaying preselected video clips for each of the 25 participants in the experiment; the physiological signals were collected simultaneously. In our multimodal emotion recognition system, recorded signals with the formation of several classification units identified the emotions independently. Then the results were fused using the adaptive weighted linear model to produce the final result. Each classification unit is assigned a weight that is determined dynamically by considering the performance of the units during the testing phase and the training phase results. This dynamic weighting scheme enables the emotion recognition system to adapt itself to each new user. The results showed that the suggested method outperformed conventional fusion of the features and classification units using the majority voting method. In addition, a considerable improvement, compared to the systems that used the static weighting schemes for fusing classification units, was also shown. Using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers, the overall classification accuracies of 84.7% and 80% were obtained in identifying the emotions, respectively. In addition, applying the forehead or physiological signals in the proposed scheme indicates that designing a reliable emotion recognition system is feasible without the need for additional emotional modalities.

Surface electromyogram signal estimation based on wavelet thresholding technique

Surface Electromyogram signal collected from the surface of skin is a biopotential signal that may be influenced by different types of noise. This is a considerable drawback in the processing of the sEMG signals. To acquire the clean sEMG that contains useful information, we need to detect and eliminate these unwanted parts of signal. In this work, a new method based on wavelet thresholding technique is presented which provides an acceptable purified sEMG signal. sEMG signals for this study are extracted for various hand movements. We use three hand movements to calculate the near optimal estimation parameters. In this work two types of thresholding techniques, namely Stein unbiased risk (SURE) estimator and adaptive Bayes estimator are utilized coupled with selected types of mother wavelets with different levels of decomposition. After designing the estimation technique, for evaluating the efficacy of method, the formed signals are sent to a pattern recognition system in order to discriminate among eight hand movements. The acquired results indicate that the wavelet based estimation technique using SURE thresholding approach is an appropriate method for producing sEMG signals without noise that may result in considerable improvement in the application of hand movement recognition.

Real-time intelligent pattern recognition algorithm for surface EMG signals

Background

Electromyography (EMG) is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided a user assessment routine to evaluate the correctness of executed movements.

Methods

We propose to use an intelligent approach based on adaptive neuro-fuzzy inference system (ANFIS) integrated with a real-time learning scheme to identify hand motion commands. For this purpose and to consider the effect of user evaluation on recognizing hand movements, vision feedback is applied to increase the capability of our system. By using this scheme the user may assess the correctness of the performed hand movement. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP) and least mean square (LMS) is utilized. Also in order to optimize the number of fuzzy rules, a subtractive clustering algorithm has been developed. To design an effective system, we consider a conventional scheme of EMG pattern recognition system. To design this system we propose to use two different sets of EMG features, namely time domain (TD) and time-frequency representation (TFR). Also in order to decrease the undesirable effects of the dimension of these feature sets, principle component analysis (PCA) is utilized.

Results

In this study, the myoelectric signals considered for classification consists of six unique hand movements. Features chosen for EMG signal are time and time-frequency domain. In this work we demonstrate the capability of an EMG pattern recognition system using ANFIS as classifier with a real-time learning method. Our results reveal that the utilized real-time ANFIS approach along with the user evaluation provides a 96.7% average accuracy. This rate is superior to the previously reported result utilizing artificial neural networks (ANN) real-time method [1].

Conclusion

This study shows that ANFIS real-time learning method coupled with mixed time and time-frequency features as EMG features can provide acceptable results for designing sEMG pattern recognition system suitable for hand prosthesis control.

Introducing a new multi-wavelet function suitable for sEMG signal to identify hand motion commands

In recent years, electromyogram signal (EMG) feature selection, based on wavelet transform, has received considerable attention. This study introduces a new multi-wavelet function for surface EMG (sEMG) signal intended for tasks that involve hand movement recognition. To create the new wavelet function, several types of well known mother wavelet were exploited and through their integration the proposed mother wavelet was generated. The proposed wavelet function closely reproduced the characteristics of the EMG signal, while increasing the recognition accuracy of hand movements. We used eight unique classes of hand motions and considered the ability of various mother wavelets and the proposed multi-wavelet to recognize these movements. Furthermore, we used local extrema and zero crossing (ZC) as DWT features. The results demonstrate that the proposed multi-wavelet function provides 87% recognition mark compared to 78%, the best performance that any other mother wavelet was able to achieve.