Traffic congestion continues to pose a significant challenge in urban environments, necessitating innovative approaches to traffic management. This paper explores the application of Quantum Annealing (QA) for real-world traffic optimization, expanding on the pioneering work of Volkswagen and D-Wave. In 2017, a collaborative team demonstrated the potential of QA to optimize traffic flow by solving a complex Quadratic Unconstrained Binary Optimization (QUBO) problem involving 418 cars, which required 1,254 qubits. Later, this research culminated in a pilot project at the Web Summit conference in Lisbon, one of Europe’s largest technology events, showcasing quantum computing-based traffic optimization. Since the QPU alone could not directly handle the full problem size, the team employed a hybrid classical-quantum approach, leading to significant improvements in traffic distribution. This paper builds on that foundation by investigating potential speedups using a purely quantum approach, particularly by utilizing the QPU for smaller QUBO problems. The proposed method (MTF) enhances traffic management by decomposing the overall optimization problem into smaller, more manageable subproblems. This decomposition enables us to harness the advantages of the QPU while tackling more complex traffic scenarios that previous approaches struggled to manage. By breaking the problem into smaller parts, we mitigate the challenges associated with embedding large-scale problems into the QPU, which often presents computational difficulties. To evaluate our approach, we conducted experiments involving 100, 200, 300, 400, and 500 cars on a complex traffic map featuring multiple start and end points. We successfully embedded the problem into the D-Wave Advantage Quantum Processing Unit, utilizing the “Pegasus” topology, which resulted in a significant acceleration of the solution process. The experiment results show improved speed and effectiveness in real-world scenarios by leveraging the QPU for better traffic optimization.

We study a proof-of-concept workflow that reformulates planar inverse kinematics (IK) for robotic manipulators as a Quadratic Unconstrained Binary Optimization (QUBO) using a linear binary discretization of joint angles and one-hot (big-M) constraints, and then solves the QUBO with quantum annealing on D-Wave hardware. We (i) define and report time-to-solution (TTS) rigorously, (ii) evaluate solution accuracy back in the original IK space via end-effector error and feasibility of one-hot constraints, and (iii) analyze embedding choices on Pegasus/Zephyr. On the tested two-link planar IK instances, Global Embedding on Zephyr uses fewer physical qubits and yields shorter access times than alternatives (see Tables 1,2), and a hybrid quantum-classical solver achieves lower TTS than classical QUBO baselines at larger Q sizes (see Figure 6). These results do not claim superiority over state-of-the-art continuous IK solvers; rather, they demonstrate that an IK-to-QUBO mapping can be executed on current quantum annealing systems with quantified accuracy and runtime, clarifying where such an approach may be useful as hardware improves.

This paper presents a comprehensive review of model compression and adversarial robustness, two critical facets of deep learning that enhance the efficiency and security of neural networks, particularly in resource-constrained environments or when security concerns are paramount. The core novelty of this work lies in its exploration of how various model compression techniques—pruning, quantization, and knowledge distillation—affect adversarial robustness. We specifically investigate whether compressing neural networks compromises their robustness or, under certain conditions, enhances it. Our review rigorously evaluates these compression methods, analyzing their effectiveness across standard testing setups and discussing the trade-offs they impose. Additionally, we introduce a general benchmarking pipeline that assesses the robustness of compressed models against a range of adversarial attacks, factoring in compression rate and model complexity. Through a comparative analysis, we examine the performance of different compression strategies, providing the first comprehensive study of their interaction with adversarial robustness. To the best of our knowledge, this work is the first to systematically explore and compare all three major model compression techniques within this context.

Our research examines the implications of dataset distillation—specifically the synthesis of datasets—on fairness in machine learning. Dataset distillation focuses on synthesizing smaller, representative datasets to improve computational efficiency. However, it is critical to explore how this synthesis process impacts the intrinsic biases within the data and subsequently influences the fairness of machine learning models. We conduct a comprehensive analysis to evaluate whether distilling datasets into smaller sizes affect the bias in trained models and how the size of these distilled datasets influences both accuracy and fairness across diverse classes. Our experiments reveal a significant trade-off between accuracy and fairness when applying the state-of-the-art Dataset Distillation by Matching Training Trajectories (MTT) method. Notably, we demonstrate that while increasing the size of the distilled dataset enhances model accuracy, it concurrently increases variance. This research is fundamental for understanding the delicate balance between efficient training and fairness in machine learning. The findings contribute to the ongoing discourse on ethical AI, providing actionable insights for practitioners seeking to optimize dataset size without compromising fairness. Our conclusions underscore the necessity of rigorously considering data distillation methodologies in real-world applications where both efficiency and fairness are of paramount importance.

We present SCOPE, a fast and efficient framework for modeling and manipulating deformable linear objects (DLOs). Unlike conventional energy-based approaches, SCOPE leverages convex approximations to significantly reduce computational cost while maintaining smooth and physically plausible deformations. This trade-off between speed and accuracy makes the method particularly suitable for applications requiring real-time or near-real-time response. The effectiveness of the proposed framework is demonstrated through comprehensive simulation experiments, highlighting its ability to generate smooth shape trajectories under geometric and length constraints.

Monte Carlo (MC) reinforcement learning suffers from high sample complexity, especially in environments with sparse rewards, large state spaces, and correlated trajectories. We address these limitations by reformulating episode selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solving it with quantum-inspired samplers. Our method, MC+QUBO, integrates a combinatorial filtering step into standard MC policy evaluation: from each batch of trajectories, we select a subset that maximizes cumulative reward while promoting state-space coverage. This selection is encoded as a QUBO, where linear terms favor high-reward episodes and quadratic terms penalize redundancy. We explore both Simulated Quantum Annealing (SQA) and Simulated Bifurcation (SB) as black-box solvers within this framework. Experiments in a finite-horizon GridWorld demonstrate that MC+QUBO outperforms vanilla MC in convergence speed and final policy quality, highlighting the potential of quantum-inspired optimization as a decision-making subroutine in reinforcement learning.