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Tingting Li

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11 papers
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11

AAAI Conference 2026 Conference Paper

Adaptive Fidelity Estimation for Quantum Programs with Graph-Guided Noise Awareness

  • Tingting Li
  • Ziming Zhao
  • Jianwei Yin

Fidelity estimation is a critical yet resource-intensive step in testing quantum programs on noisy intermediate-scale quantum (NISQ) devices, where the required number of measurements is difficult to predefine due to hardware noise, device heterogeneity, and transpilation-induced circuit transformations. We present QuFid, an adaptive and noise-aware framework that determines measurement budgets online by leveraging circuit structure and runtime statistical feedback. QuFid models a quantum program as a directed acyclic graph (DAG) and employs a control-flow-aware random walk to characterize noise propagation along gate dependencies. Backend-specific effects are captured via transpilation-induced structural deformation metrics, which are integrated into the random-walk formulation to induce a noise-propagation operator. Circuit complexity is then quantified through the spectral characteristics of this operator, providing a principled and lightweight basis for adaptive measurement planning. Experiments on 18 quantum benchmarks executed on IBM Quantum backends show that QuFid significantly reduces measurement cost compared to fixed-shot and learning-based baselines, while consistently maintaining acceptable fidelity bias.

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AAAI Conference 2026 Conference Paper

Relational Verification for Cost-Aware Quantum Program Optimization

  • Ziming Zhao
  • Tingting Li
  • Zhaoxuan Li
  • Jianwei Yin

Optimizing quantum programs is key to mitigating noise, reducing error-correction overhead, and improving performance on both near-term and fault-tolerant devices. Existing heuristic and learning-based optimizers, however, lack formal guarantees and risk semantic errors in the presence of entanglement and measurement. We present RelOpt, a semantics-preserving optimizer that enforces relational correctness between original and optimized programs. RelOpt is built on a lightweight intermediate language (QCore) with a relational operational semantics supporting partial-trace equivalence, measurement-distribution preservation, and approximate correctness. Optimization is guided by a multi-objective cost model that considers gate count, circuit depth, and error-correction cost. Only rewrite rules that are formally verified against user-specified contracts are applied. The engine combines symbolic simulation, SMT reasoning, and cost analysis to achieve safe and effective optimizations. On standard benchmarks such as QFT, Grover, and QAOA, RelOpt consistently outperforms Qiskit, t|ket>, and learning-based optimizers across multiple cost metrics while maintaining formal guarantees. By integrating formal verification with cost-aware compilation, RelOpt establishes a foundation for trustworthy and hardware-adaptive quantum toolchains.

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ICLR Conference 2025 Conference Paper

Edge-aware Image Smoothing with Relative Wavelet Domain Representation

  • Huiqing Qi
  • Xiaoliu Luo
  • Tingting Li
  • Fang Li 0004

Image smoothing is a fundamental technique in image processing, designed to eliminate perturbations and textures while preserving dominant structures. It plays a pivotal role in numerous high-level computer vision tasks. More recently, both traditional and deep learning-based smoothing methods have been developed. However, existing algorithms frequently encounter issues such as gradient reversals and halo artifacts. Furthermore, the smoothing strength of deep learning-based models, once trained, cannot be adjusted for adapting different complexity levels of textures. These limitations stem from the inability of previous approaches to achieve an optimal balance between smoothing intensity and edge preservation. Consequently, image smoothing while maintaining edge integrity remains a significant challenge. To address these challenges, we propose a novel edge-aware smoothing model that leverages a relative wavelet domain representation. Specifically, by employing wavelet transformation, we introduce a new measure, termed Relative Wavelet Domain Representation (RWDR), which effectively distinguishes between textures and structures. Additionally, we present an innovative edge-aware scale map that is incorporated into the adaptive bilateral filter, facilitating mutual guidance in the smoothing process. This paper provides complete theoretical derivations for solving the proposed non-convex optimization model. Extensive experiments substantiate that our method has a competitive superiority with previous algorithms in edge-preserving and artifact removal. Visual and numerical comparisons further validate the effectiveness and efficiency of our approach in several applications of image smoothing.

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IJCAI Conference 2025 Conference Paper

Empowering Quantum Serverless Circuit Deployment Optimization via Graph Contrastive Learning and Learning-to-Rank Co-designed Approaches

  • Tingting Li
  • Ziming Zhao
  • Jianwei Yin

With the rapid advancements in quantum computing, cloud-based quantum services have gained increasing prominence. However, due to quantum noise, optimizing the deployment of quantum circuits remains an NP-hard problem with an expansive search space. Existing methods usually use heuristic algorithms to approximate the solution, such as the representative IBM Qiskit. On the one hand, they often find suboptimal deployment solutions. On the other hand, prior technologies do not consider user-specific requirements and can only provide a single deployment strategy. In this paper, we propose QCDeploy that can provide a ranked list of effective deployment strategies to optimize quantum serverless circuit deployment. Specifically, we model quantum circuits as Directed Acyclic Graph (DAG) representations and utilize graph contrastive learning for vector embedding. Then, a tailored list-aware learning-to-rank architecture is employed to generate a list of candidate strategies (prioritizing better strategies). We conduct extensive evaluations involving 45 prevalent quantum algorithm circuits across 3~5 qubits, utilizing 3 IBM quantum physical devices with three types of chip topologies. The results demonstrate that our proposed framework significantly outperforms IBMQ's default deployment scheme, e. g. , achieving 17. 95% overhead reduction and increasing the execution success rate by 20%~40%.

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IJCAI Conference 2025 Conference Paper

Enhancing Automated Grading in Science Education through LLM-Driven Causal Reasoning and Multimodal Analysis

  • Haohao Zhu
  • Tingting Li
  • Peng He
  • Jiayu Zhou

Automated assessment of open responses in K–12 science education poses significant challenges due to the multimodal nature of student work, which often integrates textual explanations, drawings, and handwritten elements. Traditional evaluation methods that focus solely on textual analysis fail to capture the full breadth of student reasoning and are susceptible to biases such as handwriting neatness or answer length. In this paper, we propose a novel LLM-augmented multimodal evaluation framework that addresses these limitations through a comprehensive, bias-corrected grading system. Our approach leverages LLMs to generate causal knowledge graphs that encapsulate the essential conceptual relationships in student responses, comparing these graphs with those derived automatically from the rubrics and submissions. Experimental results demonstrate that our framework improves grading accuracy and consistency over deep supervised learning and few-shot LLM baselines.

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JBHI Journal 2019 Journal Article

Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph

  • Xuhua Ren
  • Tingting Li
  • Xiujun Yang
  • Shuai Wang
  • Sahar Ahmad
  • Lei Xiang
  • Shaun Richard Stone
  • Lihong Li

Skeletal bone age assessment is a common clinical practice to investigate endocrinology, and genetic and growth disorders of children. However, clinical interpretation and bone age analyses are time-consuming, labor intensive, and often subject to inter-observer variability. This advocates the need of a fully automated method for bone age assessment. We propose a regression convolutional neural network (CNN) to automatically assess the pediatric bone age from hand radiograph. Our network is specifically trained to place more attention to those bone age related regions in the X-ray images. Specifically, we first adopt the attention module to process all images and generate the coarse/fine attention maps as inputs for the regression network. Then, the regression CNN follows the supervision of the dynamic attention loss during training; thus, it can estimate the bone age of the hard (or “outlier”) images more accurately. The experimental results show that our method achieves an average discrepancy of 5. 2–5. 3 months between clinical and automatic bone age evaluations on two large datasets. In conclusion, we propose a fully automated deep learning solution to process X-ray images of the hand for bone age assessment, with the accuracy comparable to human experts but with much better efficiency.

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AAMAS Conference 2013 Conference Paper

Governing Intelligent Virtual Agent Behaviour with Norms

  • Jeehang Lee
  • Tingting Li
  • Marina De Vos
  • Julian Padget

One requirement by which virtual environments (VEs) are judged, is the believability of the virtual agents (VAs). One aspect of believability, is that agent responses to situations should not create cognitive dissonance and thereby distract the observer. One approach to this problem is the use of institutional models providing social reasoning, in conjunction with classical AI techniques providing individual reasoning, to achieve the appropriate recognition of complex situations and provide guidance on the subsequent choice of action(s). We present a distributed approach that offers governance – rather than regimentation – of intelligent virtual agents (IVAs) situated in a VE. We aim to show that the combination of an institution providing social reasoning and BDI agents providing individual reasoning, establishes a framework for enhancing believability through the interplay between: (i) the institution and IVAs in VEs, and (ii) norms maintained by the institution and the mental states of IVAs. From an engineering point of view, the framework provides a separation of concerns because the BDI agent is augmented with the capacity to process social obligations, while the specification and verification of social structure resides in the institutional models.

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IJCAI Conference 2013 Conference Paper

Normative Conflict Detection and Resolution in Cooperating Institutions

  • Tingting Li

Institutions (also called normative frameworks) provide an effective mechanism to govern agents in open distributed systems. An institution specifies a set of norms, with respect to the achievement of a goal or goals, that regulate agents’ behaviours in terms of permissions, empowerments and obligations. However, in most real circumstances, several institutions probably have to cooperate to govern the same entities simultaneously, which is very likely to give rise to norm conflicts simply if institutions will be designed independently and typically with different goals. In this thesis, we aim: (i) to identify the different ways to combine institutions, (ii) to model those ways formally and computationally by extending an existing model for single institutions, (iii) to detect conflicts in different types of combined institutions automatically, and (iv) to resolve those conflicts via automatic norm revision using an approach based on inductive learning.

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