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Quantum computing technology is advancing rapidly. Yet, even accounting for these trends, a quantum leap would be needed for quantum computers to meaningfully impact deep learning over the coming decade or two. We arrive at this conclusion based on a first-of-its-kind survey of quantum algorithms and how they match potential deep learning applications. This survey reveals three important areas where quantum computing could potentially accelerate deep learning, each of which faces a challenging roadblock to realizing its potential. First, quantum algorithms for matrix multiplication and other algorithms central to deep learning offer small theoretical improvements in the number of operations needed, but this advantage is overwhelmed on practical problem sizes by how slowly quantum computers do each operation. Second, some promising quantum algorithms depend on practical Quantum Random Access Memory (QRAM), which is underdeveloped. Finally, there are quantum algorithms that offer large theoretical advantages, but which are only applicable to special cases, limiting their practical benefits. In each of these areas, we support our arguments using quantitative forecasts of quantum advantage that build on the work by Choi et al. [2023] as well as new research on limitations and quantum hardware trends. Our analysis outlines the current scope of quantum deep learning and points to research directions that could lead togreater practical advances in the field.

AAAI 2026

Quantum Deep Learning Still Needs a Quantum Leap

workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Graph Neural Networks (GNNs) have become indispensable in critical domains such as drug discovery, social network analysis, and recommendation systems, yet their black-box nature hinders deployment in scenarios requiring transparency and accountability. While Shapley value-based methods offer mathematically principled explanations by quantifying each component’s contribution to predictions, computing exact values requires evaluating 2^n coalitions (or aggregating over n! permutations), which is intractable for real-world graphs. Existing approximation strategies sacrifice either fidelity or efficiency, limiting their practical utility. We introduce QGShap, a quantum computing approach that leverages amplitude amplification to achieve quadratic speedups in coalition evaluation while maintaining exact Shapley computation. Unlike classical sampling or surrogate methods, our approach provides fully faithful explanations without approximation trade-offs for tractable graph sizes. We conduct empirical evaluations on synthetic graph datasets, demonstrating that QGShap achieves consistently high fidelity and explanation accuracy, matching or exceeding the performance of classical methods across all evaluation metrics. These results collectively demonstrate that QGShap not only preserves exact Shapley faithfulness but also delivers interpretable, stable, and structurally consistent explanations that align with the underlying graph reasoning of GNNs.

QGShap: Quantum Acceleration for Faithful GNN Explanations

Synthetic data generation plays an important role in enabling
data sharing, particularly in sensitive domains like healthcare and finance. Recent advances in diffusion models have
made it possible to generate realistic, high-quality tabular
data, but they may also memorize training records and leak
sensitive information. Membership inference attacks (MIAs)
exploit this vulnerability by determining whether a record
was used in training. While MIAs have been studied in images and text, their use against tabular diffusion models re-
mains underexplored despite the unique risks of structured
attributes and limited record diversity. In this paper, we introduce MIA-EPT, Membership Inference Attack via Error
Prediction for Tabular Data, a novel black-box attack specifically designed to target tabular diffusion models. MIA-EPT
constructs error-based feature vectors by masking and reconstructing attributes of target records, disclosing member-
ship signals based on how well these attributes are predicted.
MIA-EPT operates without access to the internal components
of the generative model, relying only on its synthetic data
output, and was shown to generalize across multiple stateof-the-art diffusion models. We validate MIA-EPT on three
diffusion-based synthesizers, achieving AUC-ROC scores of
up to 0.599 and TPR@10% FPR values of 22.0% in our internal tests. Under the MIDST 2025 competition conditions,
MIA-EPT achieved second place in the Black-box MultiTable track (TPR@10% FPR = 20.0%). These results demonstrate that our method can uncover substantial membership
leakage in synthetic tabular data, challenging the assumption
that synthetic data is inherently privacy-preserving.

MIA-EPT: Membership Inference Attack via Error Prediction for Tabular Data

Text-to-image diffusion models achieve high-fidelity image generation from natural language prompts. ControlNets extend these models by enabling conditioning on structural inputs (e.g., edge maps, depth, pose), providing fine-grained control over outputs. Yet their reliance on large, publicly scraped datasets and community fine-tuning makes them vulnerable to data poisoning. We introduce a model-poisoning attack that embeds a covert backdoor into a ControlNet, causing it to produce attacker-specified content when exposed to visual triggers, without textual prompts. Experiments show that poisoning only 1% of the fine-tuning corpus yields a 90–98\% attack success rate, while 5\% further strengthens the backdoor, all while preserving normal generation quality. To mitigate this risk, we propose clean fine-tuning (CFT): freezing the diffusion backbone and fine-tuning only the ControlNet on a sanitized dataset with a reduced learning rate. CFT lowers attack success rates on held-out data. These results expose a critical security weakness in open-source, ControlNet-guided diffusion pipelines and demonstrate that CFT offers a practical defense for responsible synthetic-data pipelines.

Backdoors in Conditional Diffusion: Threats to Responsible Synthetic Data Pipelines

The increased availability of genetic data has transformed genomics research, but raised many privacy concerns regarding its handling due to its sensitive nature. This work explores the use of language models (LMs) for the generation of synthetic genetic mutation profiles, leveraging differential privacy (DP) for the protection of sensitive genetic data. We empirically evaluate the privacy guarantees of our DP modes by introducing a novel **Biologically-Informed Hybrid Membership Inference Attack** (biHMIA), which combines traditional black box MIA with contextual genomics metrics for enhanced attack power. Our experiments show that both small and large transformer GPT-like models are viable synthetic variant generators for *small-scale genomics*, and that our hybrid attack leads, on average, to higher adversarial success compared to traditional metric-based MIAs.

Biologically-Informed Hybrid Membership Inference Attacks on Generative Genomic Models

The proliferation of AI-generated video technologies poses challenges to information integrity. While recent benchmarks advance AIGC video detection, they overlook a critical factor: many state-of-the-art generative models embed digital watermarks in outputs, and detectors may partially rely on these patterns. To evaluate this influence, we present RobustSora, the benchmark designed to assess watermark robustness in AIGC video detection. We systematically construct a dataset of 6,500 videos comprising four types: Authentic-Clean (A-C), Authentic-Spoofed with fake watermarks (A-S), Generated-Watermarked (G-W), and Generated-DeWatermarked (G-DeW). Our benchmark introduces two evaluation tasks: Task-I tests performance on watermark-removed AI videos, while Task-II assesses false alarm rates on authentic videos with fake watermarks. Experiments with ten models spanning specialized AIGC detectors, transformer architectures, and MLLM approaches reveal performance variations of 2-8pp under watermark manipulation. Transformer-based models show consistent moderate dependency (6-8pp), while MLLMs exhibit diverse patterns (2-8pp). These findings indicate partial watermark dependency and highlight the need for watermark-aware training strategies. RobustSora provides essential tools to advance robust AIGC detection research.

RobustSora: De-Watermarked Benchmark for Robust AI-Generated Video Detection

Large Language Models (LLMs) have shown advanced capabilities in tasks like counterfactual generation and style transfer using prompt strategies. However, previous strategies lacked detailed instructions, limiting effectiveness. To address this, we introduce Compare&Generate, an algorithm inspired by human comparison, where minimal instructions lead to substantial learning. Specifically, our method incorporates an objective function that quantitatively assesses alignment with the task goal and the content relevance in the output. Then, it constructs comparison pairs based on previous generation assessments and prompts the model to reconsider how to optimize its output. Through comparison, the model focuses on the critical aspects of the task objective and refines its outputs accordingly. We benchmark our method with single-instruction as well as iterative refinement approaches across three natural language generation tasks. Experimental results show that our approach outperforms other related methods; for instance, it surpasses its single-instruction base by 17% and a state-of-the-art refinement approach by 7% on IMDB datasets in generated label accuracy, highlighting the effectiveness of using comparisons in prompts to enhance LLMs.

Improving Synthetic Data Generation with LLMs through Strategic Comparisons

Knowledge Graphs (KGs) enable applications in various domains such as semantic search, recommendation systems, and natural language processing. KGs are often incomplete, missing entities and relations, an issue addressed by Knowledge Graph Completion (KGC) methods that predict missing elements. Different evaluation metrics, such as Mean Reciprocal Rank (MRR), Mean Rank (MR), and Hit@k (e.g., Hit@1), are commonly used to assess the performance of such KGC models. A major challenge in evaluating KGC models however, lies in comparing their performance across multiple datasets and metrics. A model may outperform others on one dataset but underperform on another, making it difficult to determine overall superiority. Moreover, even within a single dataset, different metrics such as MRR and Hit@1 can yield conflicting rankings, where one model excels in MRR while another performs better in Hit@1, further complicating model selection for downstream tasks. These inconsistencies hinder holistic comparisons and highlight the need for a unified meta-metric that integrates performance across all metrics and datasets to enable a more reliable and interpretable evaluation framework. To address this need, we propose KG \textit{E}valuation based on \textit{D}istance from \textit{A}verage \textit{S}olution (EDAS), a robust and interpretable meta-metric that synthesizes model performance across multiple datasets and diverse evaluation criteria into a single normalized score ($M_i \in [0,1]$). Unlike traditional metrics that focus on isolated aspects of performance, EDAS offers a global perspective that supports more informed model selection and promotes fairness in cross-dataset evaluation. Experimental results on benchmark datasets such as FB15k-237 and WN18RR demonstrate that EDAS effectively integrates multi-metric, multi-dataset performance into a unified ranking, offering a consistent, robust, and generalizable framework for evaluating KGC models.

KG-EDAS: A Meta-Metric Framework for Evaluating Knowledge Graph Completion Models

Dialogue State Tracking (DST) requires precise extraction of structured information from multi-domain conversations, a task where Large Language Models (LLMs) struggle despite their impressive general capabilities. We present GEM (Graph-Enhanced Mixture-of-Experts), a novel framework that combines language models and graph-structured dialogue understanding with ReAct agent-based reasoning for superior DST performance. Our approach dynamically routes between specialized experts: a Graph Neural Network that captures dialogue structure and turn-level dependencies, and a finetuned T5-Small encoder-decoder for sequence modeling, coordinated by an intelligent gating network. For complex value generation tasks, we integrate ReAct agents that perform structured reasoning over dialogue context. On MultiWOZ 2.2, GEM achieves 65.19% Joint Goal Accuracy, substantially outperforming end-to-end LLM approaches (best: 38.43%) and surpassing state-of-the-art (SOTA) methods including TOATOD (63.79%), D3ST (58.70%), and Diable (56.48%). Our graph-enhanced mixture-of-experts architecture with ReAct integration demonstrates that combining structured dialogue representation with dynamic expert routing and agent-based reasoning provides a powerful paradigm for dialogue state tracking, achieving superior accuracy while maintaining computational efficiency through selective expert activation.

GEM: Graph-Enhanced Mixture-of-Experts with ReAct Agents for Dialogue State Tracking

With the increase of data in day-to-day life, businesses and different stakeholders need to analyze the data for better predictions. Traditionally, relational data has been a source of various insights, but with the increase in computational power and the need to understand deeper relationships between entities, the need to design new techniques has arisen. For this graph data analysis has become an extraordinary tool for understanding the data, which reveals more realistic and flexible modelling of complex relationships. Recently, Graph Neural Networks (GNNs) have shown great promise in various applications, such as social network analysis, recommendation systems, drug discovery, and more. However, many adversarial attacks can happen over the data, whether during training (poisoning attack) or during testing (evasion attack), which can adversely manipulate the desired outcome from the GNN model. Therefore, it is crucial to make the GNNs robust to such attacks. The existing robustness methods are computationally demanding and perform poorly when the intensity of attack increases. This paper presents a computationally efficient framework, namely, pLAPGNN, based on weighted p-Laplacian for making GNNs robust. Empirical evaluation on real datasets establishes the efficacy and efficiency of the proposed method.

Enhancing Robustness of Graph Neural Networks through p-Laplacian

Many real-world systems, from neural circuits to economic networks, exhibit feedback loops that are best represented as directed cyclic graphs (DCGs). Yet most scalable causal discovery methods either impose hard acyclicity or rely on global backpropagation, making them unsuitable for feedback-rich settings. We propose PreCyc, a predictive coding framework for causal structure learning that combines node-wise energy minimisation with a soft acyclicity surrogate and sparsity regularisation. The algorithm alternates local state inference and weight updates, avoiding reverse-mode differentiation while remaining scalable to larger graphs. Our analysis shows convergence to a stationary point under standard smoothness assumptions, and we clarify the distinction between local error signals for data fit and the global nature of acyclicity enforcement. Experiments on synthetic Erdos–Renyi, Watts–Strogatz, and scale free graphs, as well as the 279-node C. elegans connectome, demonstrate competitive performance in both structure recovery and cycle identification compared with state-of-the art cyclic causal discovery methods. While the current implementation focuses on linear structural equation models with observational equilibrium data, PreCyc establishes predictive coding as a principled and scalable foundation for causal discovery in feedback-rich systems.

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