The paper introduces the Belief-State Engine (BSE), an inference module that supplies a large language model (LLM) with a Bayesian posterior over hidden states in a partially observable Markov decision process (POMDP). By keeping the raw action‑observation log hidden from the LLM, the BSE ensures the agent behaves as a sound Markov policy on the belief MDP, thereby inheriting classical POMDP optimality guarantees. Experiments on the Tiger POMDP and a red‑team attack‑graph task show that BSE‑augmented agents outperform six baselines in task return, belief calibration, and decision consistency.
By Arnab Chattopadhayay, Debdipta Halder
arXiv:2507. 18606v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as Markov decision processes and compactly represented through dynamic decision Bayesian networks.
By Gilberto Cunha, Alexandra Ram\^oa, Andr\'e Sequeira, Michael de Oliveira, Lu\'is Barbosa
arXiv:2606. 12808v1 Announce Type: cross Abstract: Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices.
By Yash Vardhan Tomar, Dheeraj Peddireddy, Vaneet Aggarwal
The paper introduces the Infinity Machine, a self‑improving AI agent designed to solve research problems whose solutions can be programmatically verified. The system operates autonomously: it generates candidate solutions, verifies them, and audits the results without human intervention, all within a sandboxed environment governed by a kernel‑enforced gate. In a six‑day experiment, the Infinity Machine achieved four new state‑of‑the‑art results in quantum information and improved qubit‑routing and hardware efficiency, while also documenting several failure modes.
By Harish Santhanalakshmi Ganesan
arXiv:2608. 07743v1 Announce Type: new Abstract: Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and output models, expose required promises, and remain within a defensible complexity scope.
By Yijing Zuo, Zhe Fu, Zihan Nie, Zhihui Zhu, Haohan Wang
arXiv:2607. 11317v1 Announce Type: new Abstract: Low-bit quantization makes small reasoning models inexpensive to deploy but can degrade their chains of thought.
By El Hassane Ettifouri (Novelis Research, Paris, France), Ayoub Belfatmi (Novelis Research, Paris, France), Mahaman Sanoussi Yahaya Alassan (Novelis Research, Paris, France), Walid Dahhane (Novelis Research, Paris, France)
The paper investigates how an attacker could manipulate an AI adviser to issue harmful quantum error‑correction updates. It identifies an ambiguity in passive syndrome records that can mislead recovery selection and demonstrates that additional calibration measurements can provide the missing sign information needed for safe updates. By introducing a separate evaluator that only accepts updates when calibration uncertainty and drift bounds certify improvement, the authors show through simulations and surface‑code experiments that harmful proposals are rejected while beneficial ones are retained.
By A. Bar{\i}\c{s} \"Ozg\"uler
arXiv:2607. 03193v1 Announce Type: cross Abstract: Calibrating a superconducting transmon chip is a sequential decision problem under noise, drift, and a finite budget: an expert must choose experiments, read ambiguous plots, judge fit quality, and revise stale beliefs as the chip drifts.
By Animesh Tripathy, Aswanth Krishnan
arXiv:2611.13815v1 Announce Type: cross
Abstract: Error mitigation stands between variational quantum algorithms and useful results on today's noisy hardware, yet in practice a mitigation method is u...
By Bisma Majid, Shabir Ahmad Sofi, Mir Mohammad Yousuf
Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next experiment is itself a computation.
The paper introduces QXymb, a framework for building observational declarative twins of quantum circuits, and presents its first complete order‑0 specialization, QILP‑0. QILP‑0 transforms observed circuit behavior into a finite multi‑valued propositional logic program by incrementally traversing a declared family of quantum observables, quantifying progress via reference‑relative coverage, and preserving observational semantics through deterministic mapping back to original observable columns. Validation on Bars & Stripes and MNIST quantum machine learning settings shows that the induced QILP‑0 theory achieves perfect, conflict‑free reconstruction of the discrete relations, with logical exactness separated from numerical and discretization uncertainties.
whyItMatters":"The work demonstrates a method to construct exact observational declarative twins of quantum circuits, enabling precise logical reconstruction of quantum behavior independent of numerical uncertainties."
By Marina de la Cruz Echeand\'ia, C\'esar Luis Alonso, Tony Ribeiro, Alfonso Ortega de la Puente
arXiv:2606. 19947v1 Announce Type: cross Abstract: Reliable quantum control in the presence of decoherence requires policies that combat the effect of environmental noise on the controlled dynamics.
By Merijn Moody, Zier Mensch, Miranda C. N. Cheng, Peter G. Bolhuis, Max Welling