arXiv subjects

Peng Zhao

Publications and source records attributed to Peng Zhao.

At least 163 records · Page 9Linked to original sources

arXiv2023

An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems

With the increasing amount of distributed energy resources (DERs) integration, there is a significant need to model and analyze hosting capacity (HC) for future electric distribution grids. Hosting capacity analysis (HCA) examines the amount of DERs that can be safely integrated into the grid and is a challenging task in full generality because there are many possible integration of DERs in foresight. That is, there are numerous extreme points between feasible and infeasible sets. Moreover, HC depends on multiple factors such as (a) adoption patterns of DERs that depend on socio-economic behaviors and (b) how DERs are controlled and managed. These two factors are intrinsic to the problem space because not all integration of DERs may be centrally planned, and could largely change our understanding about HC. This paper addresses the research gap by capturing the two factors (a) and (b) in HCA and by identifying a few most insightful HC scenarios at the cost of domain knowledge. We propose a data-driven HCA framework and introduce active learning in HCA to effectively explore scenarios. Active learning in HCA and characteristics of HC with respect to the two factors (a) and (b) are illustrated in a 3-bus example. Next, detailed large-scale studies are proposed to understand the significance of (a) and (b). Our findings suggest that HC and its interpretations significantly change subject to the two factors (a) and (b).

eess.SY↗

arXiv2023

Gradient Aligned Attacks via a Few Queries

Black-box query attacks, which rely only on the output of the victim model, have proven to be effective in attacking deep learning models. However, existing black-box query attacks show low performance in a novel scenario where only a few queries are allowed. To address this issue, we propose gradient aligned attacks (GAA), which use the gradient aligned losses (GAL) we designed on the surrogate model to estimate the accurate gradient to improve the attack performance on the victim model. Specifically, we propose a gradient aligned mechanism to ensure that the derivatives of the loss function with respect to the logit vector have the same weight coefficients between the surrogate and victim models. Using this mechanism, we transform the cross-entropy (CE) loss and margin loss into gradient aligned forms, i.e. the gradient aligned CE or margin losses. These losses not only improve the attack performance of our gradient aligned attacks in the novel scenario but also increase the query efficiency of existing black-box query attacks. Through theoretical and empirical analysis on the ImageNet database, we demonstrate that our gradient aligned mechanism is effective, and that our gradient aligned attacks can improve the attack performance in the novel scenario by 16.1\% and 31.3\% on the $l_2$ and $l_{\infty}$ norms of the box constraint, respectively, compared to four latest transferable prior-based query attacks. Additionally, the gradient aligned losses also significantly reduce the number of queries required in these transferable prior-based query attacks by a maximum factor of 2.9 times. Overall, our proposed gradient aligned attacks and losses show significant improvements in the attack performance and query efficiency of black-box query attacks, particularly in scenarios where only a few queries are allowed.

cs.CV↗

arXiv2023

Trapped surface formation for the Einstein-Scalar system

We consider the formation of trapped surfaces in the evolution of the Einstein-scalar field system without symmetries. To this end, we follow An's strategy to analyse the formation of trapped surfaces in vacuum and for the Einstein-Maxwell system. Accordingly, we set up a characteristic initial value problem (CIVP) for the Einstein-Scalar system with initial data given on two intersecting null hypersurfaces such that on the incoming slince the data is Minkowskian whereas on the outgoing side no symmetries are assumed. We obtain a scale-critical semi-global existence result by assigning a signature for decay rates to both the geometric quantities and the scalar field. The analysis makes use of a gauge due to J. Stewart and an adjustment of the Geroch-Held-Penrose (GHP) formalism, the T-weight formalism, which renders the connection between the Newman-Penrose (NP) quantities and the PDE analysis systematic and transparent.

gr-qc↗

arXiv2023

FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples

In strong adversarial attacks against deep neural networks (DNN), the generated adversarial example will mislead the DNN-implemented classifier by destroying the output features of the last layer. To enhance the robustness of the classifier, in our paper, a \textbf{F}eature \textbf{A}nalysis and \textbf{C}onditional \textbf{M}atching prediction distribution (FACM) model is proposed to utilize the features of intermediate layers to correct the classification. Specifically, we first prove that the intermediate layers of the classifier can still retain effective features for the original category, which is defined as the correction property in our paper. According to this, we propose the FACM model consisting of \textbf{F}eature \textbf{A}nalysis (FA) correction module, \textbf{C}onditional \textbf{M}atching \textbf{P}rediction \textbf{D}istribution (CMPD) correction module and decision module. The FA correction module is the fully connected layers constructed with the output of the intermediate layers as the input to correct the classification of the classifier. The CMPD correction module is a conditional auto-encoder, which can not only use the output of intermediate layers as the condition to accelerate convergence but also mitigate the negative effect of adversarial example training with the Kullback-Leibler loss to match prediction distribution. Through the empirically verified diversity property, the correction modules can be implemented synergistically to reduce the adversarial subspace. Hence, the decision module is proposed to integrate the correction modules to enhance the DNN classifier's robustness. Specially, our model can be achieved by fine-tuning and can be combined with other model-specific defenses.

cs.CV↗

arXiv2023

Fuzziness-tuned: Improving the Transferability of Adversarial Examples

With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considerable efforts of adversarial attacks on improving the transferability of adversarial examples have been developed, the attack success rate of the transfer-based attacks on the surrogate model is much higher than that on victim model under the low attack strength (e.g., the attack strength $ε=8/255$). In this paper, we first systematically investigated this issue and found that the enormous difference of attack success rates between the surrogate model and victim model is caused by the existence of a special area (known as fuzzy domain in our paper), in which the adversarial examples in the area are classified wrongly by the surrogate model while correctly by the victim model. Then, to eliminate such enormous difference of attack success rates for improving the transferability of generated adversarial examples, a fuzziness-tuned method consisting of confidence scaling mechanism and temperature scaling mechanism is proposed to ensure the generated adversarial examples can effectively skip out of the fuzzy domain. The confidence scaling mechanism and the temperature scaling mechanism can collaboratively tune the fuzziness of the generated adversarial examples through adjusting the gradient descent weight of fuzziness and stabilizing the update direction, respectively. Specifically, the proposed fuzziness-tuned method can be effectively integrated with existing adversarial attacks to further improve the transferability of adverarial examples without changing the time complexity. Extensive experiments demonstrated that fuzziness-tuned method can effectively enhance the transferability of adversarial examples in the latest transfer-based attacks.

cs.LG↗

arXiv2023

An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling

Gaussian graphical models typically assume a homogeneous structure across all subjects, which is often restrictive in applications. In this article, we propose a weighted pseudo-likelihood approach for graphical modeling which allows different subjects to have different graphical structures depending on extraneous covariates. The pseudo-likelihood approach replaces the joint distribution by a product of the conditional distributions of each variable. We cast the conditional distribution as a heteroscedastic regression problem, with covariate-dependent variance terms, to enable information borrowing directly from the data instead of a hierarchical framework. This allows independent graphical modeling for each subject, while retaining the benefits of a hierarchical Bayes model and being computationally tractable. An efficient embarrassingly parallel variational algorithm is developed to approximate the posterior and obtain estimates of the graphs. Using a fractional variational framework, we derive asymptotic risk bounds for the estimate in terms of a novel variant of the $α$-Rényi divergence. We theoretically demonstrate the advantages of information borrowing across covariates over independent modeling. We show the practical advantages of the approach through simulation studies and illustrate the dependence structure in protein expression levels on breast cancer patients using CNV information as covariates.

stat.ME↗

arXiv2023

Error per single-qubit gate below $10^{-4}$ in a superconducting qubit

Implementing arbitrary single-qubit gates with near perfect fidelity is among the most fundamental requirements in gate-based quantum information processing. In this work, we fabric a transmon qubit with long coherence times and demonstrate single-qubit gates with the average gate error below $10^{-4}$, i.e. $(7.42\pm0.04)\times10^{-5}$ by randomized benchmarking (RB). To understand the error sources, we experimentally obtain an error budget, consisting of the decoherence errors lower bounded by $(4.62\pm0.04)\times10^{-5}$ and the leakage rate per gate of $(1.16\pm0.04)\times10^{-5}$. Moreover, we reconstruct the process matrices for the single-qubit gates by the gate set tomography (GST), with which we simulate RB sequences and obtain single-qubit fedlities consistent with experimental results. We also observe non-Markovian behavior in the experiment of long-sequence GST, which may provide guidance for further calibration. The demonstration extends the upper limit that the average fidelity of single-qubit gates can reach in a transmon-qubit system, and thus can be an essential step towards practical and reliable quantum computation in the near future.

quant-ph↗

arXiv2023

Adapting to Online Label Shift with Provable Guarantees

The standard supervised learning paradigm works effectively when training data shares the same distribution as the upcoming testing samples. However, this stationary assumption is often violated in real-world applications, especially when testing data appear in an online fashion. In this paper, we formulate and investigate the problem of \emph{online label shift} (OLaS): the learner trains an initial model from the labeled offline data and then deploys it to an unlabeled online environment where the underlying label distribution changes over time but the label-conditional density does not. The non-stationarity nature and the lack of supervision make the problem challenging to be tackled. To address the difficulty, we construct a new unbiased risk estimator that utilizes the unlabeled data, which exhibits many benign properties albeit with potential non-convexity. Building upon that, we propose novel online ensemble algorithms to deal with the non-stationarity of the environments. Our approach enjoys optimal \emph{dynamic regret}, indicating that the performance is competitive with a clairvoyant who knows the online environments in hindsight and then chooses the best decision for each round. The obtained dynamic regret bound scales with the intensity and pattern of label distribution shift, hence exhibiting the adaptivity in the OLaS problem. Extensive experiments are conducted to validate the effectiveness and support our theoretical findings.

cs.LG↗

arXiv2022

Parity-dependent unidirectional and chiral photon transfer in reversed-dissipation cavity optomechanics

Nonreciprocal elements, such as isolators and circulators, play an important role in classical and quantum information processing. Recently, strong nonreciprocal effects have been experimentally demonstrated in cavity optomechanical systems. In these approaches, the bandwidth of the nonreciprocal photon transmission is limited by the mechanical resonator linewidth, which is arguably much smaller than the linewidths of the cavity modes in most electromechanical or optomechanical devices. In this work, we demonstrate broadband nonreciprocal photon transmission in the \emph{reversed-dissipation} regime, where the mechanical mode with a large decay rate can be adiabatically eliminated while mediating anti-$\mathcal{PT}$-symmetric dissipative coupling with two kinds of phase factors. Adjusting the relative phases allows the observation of \emph{periodic} Riemann-sheet structures with distributed exceptional points (Eps). At the Eps, destructive quantum interference breaks both the $\mathcal{T}$- and $\mathcal{P}$-inversion symmetry, resulting in unidirectional and chiral photon transmissions. In the reversed-dissipation regime, the nonreciprocal bandwidth is no longer limited by the mechanical mode linewidth but is improved to the linewidth of the cavity resonance. Furthermore, we find that the direction of the unidirectional and chiral energy transfer could be reversed by changing the \emph{parity} of the Eps. Extending non-Hermitian couplings to a three-cavity model, the broken anti-$\mathcal{PT}$-symmetry allows us to observe high-order Eps, at which a parity-dependent chiral circulator is demonstrated. The driving-phase controlled periodical Riemann sheets allow observation of the parity-dependent unidirectional and chiral energy transfer and thus provide a useful cell for building up nonreciprocal array and realizing topological, e.g., isolators, circulators, or amplifiers.

quant-ph↗

arXiv2022

Universality of universal single-qubit-gate decomposition with coherent errors

To generate arbitrary one- and two-qubit gates, the universal decompositions are usually used in quantum computing, and the universality of these decompositions has been demonstrated. However, in realistic experiments, gate errors may affect the universality of the universal decompositions. Here, we focus on the single-qubit-gate decomposition scheme and study the coherent-error effects on universality. We prove that, in the parameter space which we studied, some kinds of coherent errors will not affect the original universality, but some others will destroy it. We provide the definition and analytical solutions for universality with coherent errors and propose methods to resume the accuracy of the operations with coherent errors based on our analysis. We also give the analytical results for three kinds of fidelities, which provide another metric for universality and comprehensively depict the resilience of the decomposition scheme with various kinds of coherent errors. Our work introduces a different way of thinking for quantum compilation than existing methods.

quant-ph↗

arXiv2022

Control and mitigation of microwave crosstalk effect with superconducting qubits

Improving gate performance is vital for scalable quantum computing. The universal quantum computing also requires the gate fidelity to reach a high level. For superconducting quantum processor, which operates in the microwave band, the single-qubit gates are usually realized with microwave driving. The crosstalk between microwave pulses is a non-negligible error source. In this article, we propose an error mitigation scheme to address this crosstalk issue for single-qubit gates. There are three steps in our method. First, by controlling the detuning between qubits, the microwave induced classical crosstalk error can be constrained within the computational subspace. Second, by applying the general decomposition procedure, arbitrary single-qubit gate can be decomposed as a sequence of $\sqrt{X}$ and virtual Z gates. Finally, by optimizing the parameters in virtual Z gates, the error constrained in the computational space can be corrected. Using our method, no additional compensation signals are needed, arbitrary single-qubit gate time will not be prolonged, and the circuit depth containing simultaneous single-qubit gates will also not increase. The simulation results show that, in specific regime of qubit-qubit detuning, the infidelities of simultaneous single-qubit gates can be as low as which without microwave crosstalk.

quant-ph↗

arXiv2022

Bethe-State Counting and the Witten Index

We count the Bethe states of quantum integrable models with twisted boundary conditions using the Witten index of 2d supersymmetric gauge theories. For multi-component models solvable by the nested Bethe ansatz, the result is a novel restricted occupancy problem. For the SU(3) spin chain and the t-J model, we propose formulae for the solution count on singular loci in the space of twist parameters.

hep-th↗

arXiv2022

Dynamic Regret of Online Markov Decision Processes

We investigate online Markov Decision Processes (MDPs) with adversarially changing loss functions and known transitions. We choose dynamic regret as the performance measure, defined as the performance difference between the learner and any sequence of feasible changing policies. The measure is strictly stronger than the standard static regret that benchmarks the learner's performance with a fixed compared policy. We consider three foundational models of online MDPs, including episodic loop-free Stochastic Shortest Path (SSP), episodic SSP, and infinite-horizon MDPs. For these three models, we propose novel online ensemble algorithms and establish their dynamic regret guarantees respectively, in which the results for episodic (loop-free) SSP are provably minimax optimal in terms of time horizon and certain non-stationarity measure. Furthermore, when the online environments encountered by the learner are predictable, we design improved algorithms and achieve better dynamic regret bounds for the episodic (loop-free) SSP; and moreover, we demonstrate impossibility results for the infinite-horizon MDPs.

cs.LG↗

arXiv2022

Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction

Deep neural networks are easily attacked by imperceptible perturbation. Presently, adversarial training (AT) is the most effective method to enhance the robustness of the model against adversarial examples. However, because adversarial training solved a min-max value problem, in comparison with natural training, the robustness and generalization are contradictory, i.e., the robustness improvement of the model will decrease the generalization of the model. To address this issue, in this paper, a new concept, namely confidence threshold (CT), is introduced and the reducing of the confidence threshold, known as confidence threshold reduction (CTR), is proven to improve both the generalization and robustness of the model. Specifically, to reduce the CT for natural training (i.e., for natural training with CTR), we propose a mask-guided divergence loss function (MDL) consisting of a cross-entropy loss term and an orthogonal term. The empirical and theoretical analysis demonstrates that the MDL loss improves the robustness and generalization of the model simultaneously for natural training. However, the model robustness improvement of natural training with CTR is not comparable to that of adversarial training. Therefore, for adversarial training, we propose a standard deviation loss function (STD), which minimizes the difference in the probabilities of the wrong categories, to reduce the CT by being integrated into the loss function of adversarial training. The empirical and theoretical analysis demonstrates that the STD based loss function can further improve the robustness of the adversarially trained model on basis of guaranteeing the changeless or slight improvement of the natural accuracy.

cs.LG↗

arXiv2022

Remarks on 2d Unframed Quiver Gauge Theories

We study 2d $\mathcal{N}=(2,2)$ quiver gauge theories without flavor nodes. There is a special class of quivers whose gauge group ranks stay positive in any duality frame. We illustrate this with the Abelian Kronecker quiver and the Abelian Markov quiver as the simplest examples. In the geometric phase, they engineer an infinite sequence of projective spaces and hypersurfaces in Calabi-Yau spaces, respectively. We show that the Markov quiver provides an Abelianization of SU(3) SQCD. Turning on the FI parameters and the $θ$ angles for the Abelian quiver effectively deform SQCD by such parameters. For an Abelian necklace quiver corresponding to SU($k$) SQCD, we find evidence for singular loci supporting non-compact Coulomb branches in the Kähler moduli space.

hep-th↗

arXiv2022

Spurious microwave crosstalk in floating superconducting circuits

Crosstalk is a major concern in the implementation of large-scale quantum computation since it can degrade the performance of qubit addressing and cause gate errors. Finding the origin of crosstalk and separating contributions from different channels are essential prerequisites for figuring out crosstalk mitigation schemes. Here, by performing circuit analysis of two coupled floating transmon qubits, we demonstrate that, even if the stray coupling, e.g., between a qubit and the drive line of its nearby qubit, is absent, microwave crosstalk between qubits can still exist due to the presence of a spurious crosstalk channel. This channel arises from free modes, which are supported by the floating structure of transmon qubits, i.e., the two superconducting islands of each qubit with no galvanic connection to the ground. For various geometric layouts of floating transmon qubits, we give the contributions of microwave crosstalk from the spurious channel and show that this channel can become a performance-limiting factor in qubit addressing. This research could provide guidance for suppressing microwave crosstalk between floating superconducting qubits through the design of qubit circuits.

quant-ph↗

arXiv2022

Combating fluctuations in relaxation times of fixed-frequency transmon qubits with microwave-dressed states

With the long coherence time, the fixed-frequency transmon qubit is a promising qubit modality for quantum computing. Currently, diverse qubit architectures that utilize fixed-frequency transmon qubits have been demonstrated with high-fidelity gate performance. Nevertheless, the relaxation times of transmon qubits can have large temporal fluctuations, causing instabilities in gate performance. The fluctuations are often believed to be caused by nearly on-resonance couplings with sparse two-level-system (TLS) defects. To mitigate their impact on qubit coherence and gate performance, one direct approach is to tune the qubits away from these TLSs. In this work, to combat the potential TLS-induced performance fluctuations in a tunable-bus architecture unitizing fixed-frequency transmon qubits, we explore the possibility of using an off-resonance microwave drive to effectively tuning the qubit frequency through the ac-Stark shift while implementing universal gate operations on the microwave-dressed qubit. We show that the qubit frequency can be tuned up to 20 MHz through the ac-stark shift while keeping minimal impacts on the qubit control. Besides passive approaches that aim to remove these TLSs through more careful treatments of device fabrications, this work may offer an active approach towards mitigating the TLS-induced performance fluctuations in fixed-frequency transmon qubit devices.

quant-ph↗

arXiv2022

Tunable coupling of widely separated superconducting qubits: A possible application towards a modular quantum device

Besides striving to assemble more and more qubits in a single monolithic quantum device, taking a modular design strategy may mitigate numerous engineering challenges for achieving large-scalable quantum processors with superconducting qubits. Nevertheless, a major challenge in the modular quantum device is how to realize high-fidelity entanglement operations on qubits housed in different modules while preserving the desired isolation between modules. In this work, we propose a conceptual design of a modular quantum device, where nearby modules are spatially separated by centimeters. In principle, each module can contain tens of superconducting qubits, and can be separately fabricated, characterized, packaged, and replaced. By introducing a bridge module between nearby qubit modules and taking the coupling scheme utilizing a tunable bus, tunable coupling of qubits that are housed in nearby qubit modules, could be realized. Given physically reasonable assumptions, we expect that sub-100-ns two-qubit gates for qubits housed in nearby modules which are spatially separated by more than two centimeters could be obtained. In this way, the inter-module gate operations are promising to be implemented with gate performance comparable with that of intra-module gate operations. Moreover, with help of through-silicon vias technologies, this long-range coupling scheme may also allow one to implement inter-module couplers in a multi-chip stacked processor. Thus, the tunable longer-range coupling scheme and the proposed modular architecture may provide a promising foundation for solving challenges toward large-scale quantum information processing with superconducting qubits.

quant-ph↗