Skip to content
Log in
#reinforcement learning Open access

Adaptive Quantum State Measurement and Information Extraction

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Quantum Information and Cryptography

Abstract

This paper investigates the development of an adaptive quantum state measurement protocol designed to enhance information extraction efficiency from quantum states while mitigating the detrimental effects of decoherence. The core idea revolves around employing a reinforcement learning algorithm to dynamically adjust the measurement basis, based on the observed quantum state characteristics and feedback signals generated during the measurement process. Real-time adjustments to measurement device parameters via quantum control techniques further optimize the measurement outcome. The proposed method addresses the limitations of traditional fixed-basis measurements, which are highly susceptible to decoherence. Through adaptive basis selection, this approach aims to maximize information extraction and minimize interference with the quantum state. The theoretical framework and underlying principles are presented, outlining the key components and their interrelationship. The potential impact of this technique on quantum information processing is discussed.

DOI
View source

Similar papers

#artificial intelligence Nov 2026

AI-Enabled Performance-Based Procurement and Life-Cycle Maintenance of Highway Bridges: Integrating Single-Bid Risk Analytics and PPP Payment Optimization

Model-based life-cycle evaluation indicates that AI-optimized PPP contracts reduce bridges reaching emergency condition by 30%–40% over a 30-year horizon while lowering life-cycle costs by 8%–12% compared with rule-based policies, providing infrastructure agencies and private concessionaires with an integrated AI-driven life-cycle management platform.

Ali Shehadeh, Odey Alshboul · 0 citations
#reinforcement learning Open access Aug 2026

Residual RL on a PSO-tuned Fuzzy Controller for Mobile Robot Trajectory Tracking

This paper presents a two-wheeled mobile robot trajectory-tracking controller combining a particle swarm optimization (PSO)-tuned fuzzy logic controller (FLC) with a residual reinforcement learning (RL) correction layer.PSO tuning reduces the global distance error by 35% and the integral absolute error by 44% over the initial FLC.The residual RL layer further reduces the global distance error by approximately 2.3% and improves cornering-region tracking by 3.9% in RMSE, 4.7% in IAE, and 5.2% in peak distance error.The proposed controller also reduces the global distance error by 41% and 66% relative to independently tuned PID and fuzzy-PID baselines.Trained across four trajectory families with a held-out test split, the generalized agent reduces the average test distance error by 18% relative to the tuned FLC baseline.These results show that a lightweight residual correction improves both accuracy and generalization while preserving the fuzzy controller's interpretability.

Le Ngoc Dung, Luu Hong Quan, Doan Cong Anh · 0 citations

Related blog posts