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Quantum Machine Learning, Quantitative Trading, Reinforcement Learning, Deep Learning
Jun-Hao Chen, Yu-Chien Huang, Yun-Cheng Tsai, Samuel Yen-Chi Chen
https://arxiv.org/abs/2509.09176

Quantum Machine Learning, Quantitative Trading, Reinforcement Learning, Deep Learning
The convergence of quantum-inspired neural networks and deep reinforcement learning offers a promising avenue for financial trading. We implemented a trading agent for USD/TWD by integrating Quantum Long Short-Term Memory (QLSTM) for short-term trend prediction with Quantum Asynchronous Advantage Actor-Critic (QA3C), a quantum-enhanced variant of the classical A3C. Trained on data from 2000-01-01 to 2025-04-30 (80\% training, 20\% testing), the long-only agent achieves 11.87\% return over aroun…

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Morteza Kimiaei, Vyacheslav Kungurtsev, Brian Olimba
https://arxiv.org/abs/2508.06906

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Integer and mixed-integer nonlinear programming (INLP, MINLP) are central to logistics, energy, and scheduling, but remain computationally challenging. This survey examines how machine learning and reinforcement learning can enhance exact optimization methods - particularly branch-and-bound (BB), without compromising global optimality. We cover discrete, continuous, and mixed-integer formulations, and highlight applications such as crew scheduling, vehicle routing, and hydropower planning. We i…

RIS-Assisted NOMA with Partial CSI and Mutual Coupling: A Machine Learning Approach
Bile Peng, Karl-Ludwig Besser, Shanpu Shen, Finn Siegismund-Poschmann, Ramprasad Raghunath, Daniel M. Mittleman, Vahid Jamali, Eduard A. Jorswieck
https://arxiv.org/abs/2508.07909

RIS-Assisted NOMA with Partial CSI and Mutual Coupling: A Machine Learning Approach
Non-orthogonal multiple access (NOMA) is a promising multiple access technique. Its performance depends strongly on the wireless channel property, which can be enhanced by reconfigurable intelligent surfaces (RISs). In this paper, we jointly optimize base station (BS) precoding and RIS configuration with unsupervised machine learning (ML), which looks for the optimal solution autonomously. In particular, we propose a dedicated neural network (NN) architecture RISnet inspired by domain knowledge…

Replicable Reinforcement Learning with Linear Function Approximation
Eric Eaton, Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell
https://arxiv.org/abs/2509.08660

Replicable Reinforcement Learning with Linear Function Approximation
Replication of experimental results has been a challenge faced by many scientific disciplines, including the field of machine learning. Recent work on the theory of machine learning has formalized replicability as the demand that an algorithm produce identical outcomes when executed twice on different samples from the same distribution. Provably replicable algorithms are especially interesting for reinforcement learning (RL), where algorithms are known to be unstable in practice. While replicab…

From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimation
Abdelhakim Benechehab, Gabriel Singer, Corentin L\'eger, Youssef Attia El Hili, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Bal\'azs K\'egl
https://arxiv.org/abs/2510.07624

From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimation
Generative models form the backbone of modern machine learning, underpinning state-of-the-art systems in text, vision, and multimodal applications. While Maximum Likelihood Estimation has traditionally served as the dominant training paradigm, recent work have highlighted its limitations, particularly in generalization and susceptibility to catastrophic forgetting compared to Reinforcement Learning techniques, such as Policy Gradient methods. However, these approaches depend on explicit reward …

Policy Newton methods for Distortion Riskmetrics
Soumen Pachal, Mizhaan Prajit Maniyar, Prashanth L. A
https://arxiv.org/abs/2508.07249 https://arxiv.org/p…

Policy Newton methods for Distortion Riskmetrics
We consider the problem of risk-sensitive control in a reinforcement learning (RL) framework. In particular, we aim to find a risk-optimal policy by maximizing the distortion riskmetric (DRM) of the discounted reward in a finite horizon Markov decision process (MDP). DRMs are a rich class of risk measures that include several well-known risk measures as special cases. We derive a policy Hessian theorem for the DRM objective using the likelihood ratio method. Using this result, we propose a natu…

Spotify enters into a direct music publishing agreement with BMG, following deals in 2025 with UMG, Sony Music, Warner Chappell, and Kobalt (Ethan Millman/The Hollywood Reporter)
https://www.hollywoodreporter.com/music/music-industry-news/s…

Spotify Signs Licensing Publishing Deal with BMG
"Working directly with Spotify helps us reinforce our mission to ensure songwriters are fairly represented and rewarded for their work," BMG CEO Thomas Coesfeld said.

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning
Aaron Mark Thomas, Yu-Cheng Chen, Hubert Okadome Valencia, Sharu Theresa Jose, Ronin Wu
https://arxiv.org/abs/2509.05051

QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning
Navigating the vast chemical space of molecular structures to design novel drug molecules with desired target properties remains a central challenge in drug discovery. Recent advances in generative models offer promising solutions. This work presents a novel quantum circuit Born machine (QCBM)-enabled Generative Adversarial Network (GAN), called QCA-MolGAN, for generating drug-like molecules. The QCBM serves as a learnable prior distribution, which is associatively trained to define a latent sp…

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Yuanjun Dai, Keqiang He, An Wang
https://arxiv.org/abs/2510.08522 https://

DYNAMIX: RL-based Adaptive Batch Size Optimization in Distributed Machine Learning Systems
Existing batch size selection approaches in dis- tributed machine learning rely on static allocation or simplistic heuristics that fail to adapt to heterogeneous, dynamic computing environments. We present DYNAMIX, a reinforcement learning framework that formulates batch size optimization as a sequen- tial decision-making problem using Proximal Policy Optimiza- tion (PPO). Our approach employs a multi-dimensional state representation encompassing network-level metrics, system-level resource uti…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
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- Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving
Zhihao Zhang, Chengyang Peng, Ekim Yurtsever, Keith A. Redmill