arXiv subjects

Sai Li

Publications and source records attributed to Sai Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

QUEST: A Quantized Energy-Aware SNN Training Framework for Multi-State Neuromorphic Devices

Neuromorphic devices, leveraging novel physical phenomena, offer a promising path toward energy-efficient hardware beyond CMOS technology by emulating brain-inspired computation. However, their progress is often limited to proof-of-concept studies due to the lack of flexible spiking neural network (SNN) algorithm frameworks tailored to device-specific characteristics, posing a significant challenge to scalability and practical deployment. To address this, we propose QUEST, a unified co-design framework that directly trains SNN for emerging devices featuring multilevel resistances. With Skyrmionic Magnetic Tunnel Junction (Sk-MTJ) as a case study, experimental results on the CIFAR-10 dataset demonstrate the framework's ability to enable scalable on-device SNN training with minimal energy consumption during both feedforward and backpropagation. By introducing device mapping pattern and activation operation sparsity, QUEST achieves effective trade-offs among high accuracy (89.6%), low bit precision (2-bit), and energy efficiency (93 times improvement over the ANNs). QUEST offers practical design guidelines for both the device and algorithm communities, providing insights to build energy-efficient and large-scale neuromorphic systems.

physics.app-ph↗

arXiv2024

MHRC: Closed-loop Decentralized Multi-Heterogeneous Robot Collaboration with Large Language Models

The integration of large language models (LLMs) with robotics has significantly advanced robots' abilities in perception, cognition, and task planning. The use of natural language interfaces offers a unified approach for expressing the capability differences of heterogeneous robots, facilitating communication between them, and enabling seamless task allocation and collaboration. Currently, the utilization of LLMs to achieve decentralized multi-heterogeneous robot collaborative tasks remains an under-explored area of research. In this paper, we introduce a novel framework that utilizes LLMs to achieve decentralized collaboration among multiple heterogeneous robots. Our framework supports three robot categories, mobile robots, manipulation robots, and mobile manipulation robots, working together to complete tasks such as exploration, transportation, and organization. We developed a rich set of textual feedback mechanisms and chain-of-thought (CoT) prompts to enhance task planning efficiency and overall system performance. The mobile manipulation robot can adjust its base position flexibly, ensuring optimal conditions for grasping tasks. The manipulation robot can comprehend task requirements, seek assistance when necessary, and handle objects appropriately. Meanwhile, the mobile robot can explore the environment extensively, map object locations, and communicate this information to the mobile manipulation robot, thus improving task execution efficiency. We evaluated the framework using PyBullet, creating scenarios with three different room layouts and three distinct operational tasks. We tested various LLM models and conducted ablation studies to assess the contributions of different modules. The experimental results confirm the effectiveness and necessity of our proposed framework.

cs.RO↗

arXiv2024

Discovery and inference of possibly bi-directional causal relationships with invalid instrumental variables

Learning causal relationships between pairs of complex traits from observational studies is of great interest across various scientific domains. However, most existing methods assume the absence of unmeasured confounding and restrict causal relationships between two traits to be uni-directional, which may be violated in real-world systems. In this paper, we address the challenge of causal discovery and effect inference for two traits while accounting for unmeasured confounding and potential feedback loops. By leveraging possibly invalid instrumental variables, we provide identification conditions for causal parameters in a model that allows for bi-directional relationships, and we also establish identifiability of the causal direction under the introduced conditions. Then we propose a data-driven procedure to detect the causal direction and provide inference results about causal effects along the identified direction. We show that our method consistently recovers the true direction and produces valid confidence intervals for the causal effect. We conduct extensive simulation studies to show that our proposal outperforms existing methods. We finally apply our method to analyze real data sets from UK Biobank.

stat.ME↗

arXiv2024

Autonomous Stabilization of Fock States in an Oscillator against Multiphoton Losses

Fock states with a well-defined number of photons in an oscillator have shown a wide range of applications in quantum information science. Nonetheless, their usefulness has been marred by single and multiple photon losses due to unavoidable environment-induced dissipation. Though several dissipation engineering methods have been developed to counteract the leading single-photon loss error, averting multiple photon losses remains elusive. Here, we experimentally demonstrate a dissipation engineering method that autonomously stabilizes multi-photon Fock states against losses of multiple photons using a cascaded selective photon-addition operation in a superconducting quantum circuit. Through measuring the photon-number populations and Wigner tomography of the oscillator states, we observe a prolonged preservation of nonclassical Wigner negativities for the stabilized Fock states $\vert N\rangle$ with $N=1,2,3$ for a duration of about 10 ms. Furthermore, the dissipation engineering method demonstrated here also facilitates the implementation of a non-unitary operation for resetting a binomially-encoded logical qubit. These results highlight potential applications in error-correctable quantum information processing against multi-photon-loss errors.

quant-ph↗

arXiv2024

FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality

Machine learning methods often assume that the test data have the same distribution as the training data. However, this assumption may not hold due to multiple levels of heterogeneity in applications, raising issues in algorithmic fairness and domain generalization. In this work, we address the problem of fair and generalizable machine learning by invariant principles. We propose a training environment-based oracle, FAIRM, which has desirable fairness and domain generalization properties under a diversity-type condition. We then provide an empirical FAIRM with finite-sample theoretical guarantees under weak distributional assumptions. We then develop efficient algorithms to realize FAIRM in linear models and demonstrate the nonasymptotic performance with minimax optimality. We evaluate our method in numerical experiments with synthetic data and MNIST data and show that it outperforms its counterparts.

stat.ML↗

arXiv2024

Scalable protocol to mitigate $ZZ$ crosstalk in universal quantum gates

High-fidelity universal quantum gates are widely acknowledged as essential for scalable quantum computation. However, in solid-state quantum systems, which hold promise as physical implementation platforms for quantum computation, the inevitable $ZZ$ crosstalk resulting from interqubit interactions significantly impairs quantum operation performance. Here we propose a scalable protocol to achieve $ZZ$-crosstalk mitigation in universal quantum gates. This method converts the noisy Hamiltonian with $ZZ$ crosstalk into a framework that efficiently suppresses all $ZZ$-crosstalk effects, leading to ideal target quantum operations. Specifically, we first analytically derive the $ZZ$-crosstalk mitigation conditions and then apply them to enhance the performance of target universal quantum gates. Moreover, numerical simulations validate the effectiveness of $ZZ$-crosstalk mitigation when multiple qubit gates operate concurrently. As a result, our protocol presents a promising approach for implementing practical parallel quantum gates in large-scale quantum computation scenarios.

quant-ph↗

arXiv2024

Leveraging Local Distributions in Mendelian Randomization: Uncertain Opinions are Invalid

Mendelian randomization (MR) considers using genetic variants as instrumental variables (IVs) to infer causal effects in observational studies. However, the validity of causal inference in MR can be compromised when the IVs are potentially invalid. In this work, we propose a new method, MR-Local, to infer the causal effect in the existence of possibly invalid IVs. By leveraging the distribution of ratio estimates around the true causal effect, MR-Local selects the cluster of ratio estimates with the least uncertainty and performs causal inference within it. We establish the asymptotic normality of our estimator in the two-sample summary-data setting under either the plurality rule or the balanced pleiotropy assumption. Extensive simulations and analyses of real datasets demonstrate the reliability of our approach.

stat.ME↗

arXiv2023

A Regression-based Approach to Robust Estimation and Inference for Genetic Covariance

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits, and some variants are shown to be associated with multiple complex traits. Genetic covariance between two traits is defined as the underlying covariance of genetic effects and can be used to measure the shared genetic architecture. The data used to estimate such a genetic covariance can be from the same group or different groups of individuals, and the traits can be of different types or collected based on different study designs. This paper proposes a unified regression-based approach to robust estimation and inference for genetic covariance of general traits that may be associated with genetic variants nonlinearly. The asymptotic properties of the proposed estimator are provided and are shown to be robust under certain model mis-specification. Our method under linear working models provides a robust inference for the narrow-sense genetic covariance, even when both linear models are mis-specified. Numerical experiments are performed to support the theoretical results. Our method is applied to an outbred mice GWAS data set to study the overlapping genetic effects between the behavioral and physiological phenotypes. The real data results reveal interesting genetic covariance among different mice developmental traits.

stat.ME↗

arXiv2023

Multi-dimensional domain generalization with low-rank structures

In conventional statistical and machine learning methods, it is typically assumed that the test data are identically distributed with the training data. However, this assumption does not always hold, especially in applications where the target population are not well-represented in the training data. This is a notable issue in health-related studies, where specific ethnic populations may be underrepresented, posing a significant challenge for researchers aiming to make statistical inferences about these minority groups. In this work, we present a novel approach to addressing this challenge in linear regression models. We organize the model parameters for all the sub-populations into a tensor. By studying a structured tensor completion problem, we can achieve robust domain generalization, i.e., learning about sub-populations with limited or no available data. Our method novelly leverages the structure of group labels and it can produce more reliable and interpretable generalization results. We establish rigorous theoretical guarantees for the proposed method and demonstrate its minimax optimality. To validate the effectiveness of our approach, we conduct extensive numerical experiments and a real data study focused on education level prediction for multiple ethnic groups, comparing our results with those obtained using other existing methods.

stat.ME↗

arXiv2023

Beating the break-even point with a discrete-variable-encoded logical qubit

Quantum error correction (QEC) aims to protect logical qubits from noises by utilizing the redundancy of a large Hilbert space, where an error, once it occurs, can be detected and corrected in real time. In most QEC codes, a logical qubit is encoded in some discrete variables, e.g., photon numbers. Such encoding schemes make the codewords orthogonal, so that the encoded quantum information can be unambiguously extracted after processing. Based on such discrete-variable encodings, repetitive QEC demonstrations have been reported on various platforms, but there the lifetime of the encoded logical qubit is still shorter than that of the best available physical qubit in the entire system, which represents a break-even point that needs to be surpassed for any QEC code to be of practical use. Here we demonstrate a QEC procedure with a logical qubit encoded in photon-number states of a microwave cavity, dispersively coupled to an ancilla superconducting qubit. By applying a pulse featuring a tailored frequency comb to the ancilla, we can repetitively extract the error syndrome with high fidelity and perform error correction with feedback control accordingly, thereby exceeding the break-even point by about 16% lifetime enhancement. Our work illustrates the potential of the hardware-efficient discrete-variable QEC codes towards a reliable quantum information processor.

quant-ph↗

arXiv2022

Scalable Method for Eliminating Residual $ZZ$ Interaction between Superconducting Qubits

Unwanted $ZZ$ interaction is a quantum-mechanical crosstalk phenomenon which correlates qubit dynamics and is ubiquitous in superconducting qubit systems. It adversely affects the quality of quantum operations and can be detrimental in scalable quantum information processing. Here we propose and experimentally demonstrate a practically extensible approach for complete cancellation of residual $ZZ$ interaction between fixed-frequency transmon qubits, which are known for long coherence and simple control. We apply to the intermediate coupler that connects the qubits a weak microwave drive at a properly chosen frequency in order to noninvasively induce an ac Stark shift for $ZZ$ cancellation. We verify the cancellation performance by measuring vanishing two-qubit entangling phases and $ZZ$ correlations. In addition, we implement a randomized benchmarking experiment to extract the idling gate fidelity which shows good agreement with the coherence limit, demonstrating the effectiveness of $ZZ$ cancellation. Our method allows independent addressability of each qubit-qubit connection, and is applicable to both nontunable and tunable couplers, promising better compatibility with future large-scale quantum processors.

quant-ph↗

arXiv2022

Causal Inference for Nonlinear Outcome Models with Possibly Invalid Instrumental Variables

Instrumental variable methods are widely used for inferring the causal effect in the presence of unmeasured confounders. Existing instrumental variable methods for nonlinear outcome models require stringent identifiability conditions. This paper considers a flexible semi-parametric potential outcome model that allows for possibly invalid instruments. We propose new identifiability conditions to identify the causal parameters when the majority of the instrumental variables are valid. We devise a novel inference procedure for a new average structural function and the conditional average treatment effect. We establish the asymptotic normality of the proposed estimators and construct confidence intervals for the causal estimands by bootstrap. The proposed method is demonstrated in large-scale simulation studies and is applied to infer the effect of income on house ownership.

stat.ME↗

arXiv2022

Improving Out-of-Distribution Robustness via Selective Augmentation

Machine learning algorithms typically assume that training and test examples are drawn from the same distribution. However, distribution shift is a common problem in real-world applications and can cause models to perform dramatically worse at test time. In this paper, we specifically consider the problems of subpopulation shifts (e.g., imbalanced data) and domain shifts. While prior works often seek to explicitly regularize internal representations or predictors of the model to be domain invariant, we instead aim to learn invariant predictors without restricting the model's internal representations or predictors. This leads to a simple mixup-based technique which learns invariant predictors via selective augmentation called LISA. LISA selectively interpolates samples either with the same labels but different domains or with the same domain but different labels. Empirically, we study the effectiveness of LISA on nine benchmarks ranging from subpopulation shifts to domain shifts, and we find that LISA consistently outperforms other state-of-the-art methods and leads to more invariant predictors. We further analyze a linear setting and theoretically show how LISA leads to a smaller worst-group error.

cs.LG↗

arXiv2022

A Focusing Framework for Testing Bi-Directional Causal Effects with GWAS Summary Data

Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on an outcome. Although recent years have seen many extensions of basic MR methods to be robust to certain violations of assumptions, few methods were proposed to infer bi-directional causal relationships, especially for phenotypes with limited biological understandings. The presence of horizontal pleiotropy adds another layer of complexity. In this article, we show that assumptions for common MR methods are often impossible or too stringent in the existence of bi-directional relationships. We then propose a new focusing framework for testing bi-directional causal effects between two traits with possibly pleiotropic genetic variants. Our proposal can be coupled with many state-of-art MR methods. We provide theoretical guarantees on the Type I error and power of the proposed methods. We demonstrate the robustness of the proposed methods using several simulated and real datasets.

stat.ME↗

arXiv2022

Fast quantum state transfer and entanglement for cavity-coupled many qubits via dark pathways

Quantum state transfer (QST) and entangled state generation (ESG) are important building blocks for modern quantum information processing. To achieve these tasks, convention wisdom is to consult the quantum adiabatic evolution, which is time-consuming, and thus is of low fidelity. Here, using the shortcut to adiabaticity technique, we propose a general method to realize high-fidelity fast QST and ESG in a cavity-coupled many qubits system via its dark pathways, which can be further designed for high-fidelity quantum tasks with different optimization purpose. Specifically, with a proper dark pathway, QST and ESG between any two qubits can be achieved without decoupling the others, which simplifies experimental demonstrations. Meanwhile, ESG among all qubits can also be realized in a single step. In addition, our scheme can be implemented in many quantum systems, and we illustrate its implementation on superconducting quantum circuits. Therefore, we propose a powerful strategy for selective quantum manipulation, which is promising in cavity coupled quantum systems and could find many convenient applications in quantum information processing.

quant-ph↗

arXiv2022

Estimation and Inference with Proxy Data and its Genetic Applications

Existing high-dimensional statistical methods are largely established for analyzing individual-level data. In this work, we study estimation and inference for high-dimensional linear models where we only observe "proxy data", which include the marginal statistics and sample covariance matrix that are computed based on different sets of individuals. We develop a rate optimal method for estimation and inference for the regression coefficient vector and its linear functionals based on the proxy data. Moreover, we show the intrinsic limitations in the proxy-data based inference: the minimax optimal rate for estimation is slower than that in the conventional case where individual data are observed; the power for testing and multiple testing does not go to one as the signal strength goes to infinity. These interesting findings are illustrated through simulation studies and an analysis of a dataset concerning the genetic associations of hindlimb muscle weight in a mouse population.

stat.ME↗

arXiv2021

Superrobust Geometric Control of a Superconducting Circuit

Geometric phases accompanying adiabatic quantum evolutions can be used to construct robust quantum control for quantum information processing due to their noise-resilient feature. A significant development along this line is to construct geometric gates using nonadiabatic quantum evolutions to reduce errors due to decoherence. However, it has been shown that nonadiabatic geometric gates are not necessarily more robust than dynamical ones, in contrast to an intuitive expectation. Here we experimentally investigate this issue for the case of nonadiabatic holonomic quantum computation~(NHQC) and show that conventional NHQC schemes cannot guarantee the expected robustness due to a cross coupling to the states outside the computational space. We implement a different set of constraints for gate construction in order to suppress such cross coupling to achieve an enhanced robustness. Using a superconducting quantum circuit, we demonstrate high-fidelity holonomic gates whose infidelity against quasi-static transverse errors can be suppressed up to the fourth order, instead of the second order in conventional NHQC and dynamical gates. In addition, we explicitly measure the accumulated dynamical phase due to the above mentioned cross coupling and verify that it is indeed much reduced in our NHQC scheme. We further demonstrate a protocol for constructing two-qubit NHQC gates also with an enhanced robustness.

quant-ph↗

arXiv2021

Dynamically Corrected Nonadiabatic Holonomic Quantum Gates

The key for realizing fault-tolerant quantum computation lies in maintaining the coherence of all qubits so that high-fidelity and robust quantum manipulations on them can be achieved. One of the promising approaches is to use geometric phases in the construction of universal quantum gates, due to their intrinsic robustness against certain types of local noises. However, due to limitations in previous implementations, the noise-resilience feature of nonadiabatic holonomic quantum computation (NHQC) still needs to be improved. Here, combining with the dynamical correction technique, we propose a general protocol of universal NHQC with simplified control, which can greatly suppress the effect of the accompanied X errors, retaining the main merit of geometric quantum operations. Numerical simulation shows that the performance of our gate can be much better than previous protocols. Remarkably, when incorporating a decoherence-free subspace encoding for the collective dephasing noise, our scheme can also be robust against the involved Z errors. In addition, we also outline the physical implementation of the protocol that is insensitive to both X and Z errors. Therefore, our protocol provides a promising strategy for scalable fault-tolerant quantum computation.

quant-ph↗