Ziming Zhao

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.

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.

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%.

IJCAI Conference 2025 Conference Paper

Harnessing Vision Models for Time Series Analysis: A Survey

• Jingchao Ni
• Ziming Zhao
• ChengAo Shen
• Hanghang Tong
• Dongjin Song
• Wei Cheng
• Dongsheng Luo
• Haifeng Chen

Time series analysis has evolved from traditional autoregressive models to deep learning, Transformers, and Large Language Models (LLMs). While vision models have also been explored along the way, their contributions are less recognized due to the predominance of sequence modeling. However, challenges such as the mismatch between continuous time series and LLMs’ discrete token space, and the difficulty in capturing multivariate correlations, have led to growing interest in Large Vision Models (LVMs) and Vision-Language Models (VLMs). This survey highlights the advantages of vision models over LLMs in time series analysis, offering a comprehensive dual-view taxonomy that answers key research questions like how to encode time series as images and how to model imaged time series. Additionally, we address pre- and post-processing challenges in this framework and outline future directions for advancing the field.

NeurIPS Conference 2025 Conference Paper

Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting

• ChengAo Shen
• Wenchao Yu
• Ziming Zhao
• Dongjin Song
• Wei Cheng
• Haifeng Chen
• Jingchao Ni

Time series, typically represented as numerical sequences, can also be transformed into images and texts, offering multi-modal views (MMVs) of the same underlying signal. These MMVs can reveal complementary patterns and enable the use of powerful pre-trained large models, such as large vision models (LVMs), for long-term time series forecasting (LTSF). However, as we identified in this work, the state-of-the-art (SOTA) LVM-based forecaster poses an inductive bias towards "forecasting periods". To harness this bias, we propose DMMV, a novel decomposition-based multi-modal view framework that leverages trend-seasonal decomposition and a novel backcast-residual based adaptive decomposition to integrate MMVs for LTSF. Comparative evaluations against 14 SOTA models across diverse datasets show that DMMV outperforms single-view and existing multi-modal baselines, achieving the best mean squared error (MSE) on 6 out of 8 benchmark datasets. The code for this paper is available at: https: //github. com/D2I-Group/dmmv.

AAAI Conference 2023 Conference Paper

Purifier: Defending Data Inference Attacks via Transforming Confidence Scores

• Ziqi Yang
• Lijin Wang
• Da Yang
• Jie Wan
• Ziming Zhao
• Ee-Chien Chang
• Fan Zhang
• Kui Ren

Neural networks are susceptible to data inference attacks such as the membership inference attack, the adversarial model inversion attack and the attribute inference attack, where the attacker could infer useful information such as the membership, the reconstruction or the sensitive attributes of a data sample from the confidence scores predicted by the target classifier. In this paper, we propose a method, namely PURIFIER, to defend against membership inference attacks. It transforms the confidence score vectors predicted by the target classifier and makes purified confidence scores indistinguishable in individual shape, statistical distribution and prediction label between members and non-members. The experimental results show that PURIFIER helps defend membership inference attacks with high effectiveness and efficiency, outperforming previous defense methods, and also incurs negligible utility loss. Besides, our further experiments show that PURIFIER is also effective in defending adversarial model inversion attacks and attribute inference attacks. For example, the inversion error is raised about 4+ times on the Facescrub530 classifier, and the attribute inference accuracy drops significantly when PURIFIER is deployed in our experiment.

TIST Journal 2019 Journal Article

Edge-enabled Disaster Rescue

• Fang Liu
• Yeting Guo
• Zhiping Cai
• Nong Xiao
• Ziming Zhao

In the aftermath of earthquakes, floods, and other disasters, photos are increasingly playing more significant roles, such as finding missing people and assessing disasters, in rescue and recovery efforts. These disaster photos are taken in real time by the crowd, unmanned aerial vehicles, and wireless sensors. However, communications equipment is often damaged in disasters, and the very limited communication bandwidth restricts the upload of photos to the cloud center, seriously impeding disaster rescue endeavors. Based on edge computing, we propose Echo, a highly time-efficient disaster rescue framework. By utilizing the computing, storage, and communication abilities of edge servers, disaster photos are preprocessed and analyzed in real time, and more specific visuals are immensely helpful for conducting emergency response and rescue. This article takes the search for missing people as a case study to show that Echo can be more advantageous in terms of disaster rescue. To greatly conserve valuable communication bandwidth, only significantly associated images are extracted and uploaded to the cloud center for subsequent facial recognition. Furthermore, an adaptive photo detector is designed to utilize the precious and unstable communication bandwidth effectively, as well as ensure the photo detection precision and recall rate. The effectiveness and efficiency of the proposed method are demonstrated by simulation experiments.

AAMAS Conference 2017 Conference Paper

A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

• Sailik Sengupta
• Satya Gautam Vadlamudi
• Subbarao Kambhampati
• Adam Doupé
• Ziming Zhao
• Marthony Taguinod
• Gail-Joon Ahn

AAMAS Conference 2016 Conference Paper

Moving Target Defense for Web Applications Using Bayesian Stackelberg Games (Extended Abstract)

• Satya Gautam Vadlamudi
• Sailik Sengupta
• Marthony Taguinod
• Ziming Zhao
• Adam Doupé
• Gail-Joon Ahn
• Subbarao Kambhampati

Vulnerabilities in web applications allow hackers to access and/or modify restricted data. Here the hackers have the opportunity to perform reconnaissance so as to gain knowledge about the web application layout before launching an attack, whereas the defender (administrator of the web application) must secure the application even with its potential vulnerabilities. In order to mask such vulnerabilities which are primarily associated with different individual configurations, Moving Target Defense systems were proposed wherein the defender switches between various configurations thereby making it difficult to attack with success, while maintaining a seamless experience for the genuine users. In this paper, we present a way to find effective switching strategies by modeling this ecosystem as a Bayesian Stackelberg game with the administrator as the leader and the hackers as the followers, which as we show succinctly captures various aspects of the Moving Target Defense systems. Furthermore, we propose ways to find the most critical vulnerabilities and the most sensitive attacker types, which are key issues in such scenarios.