US Customs and Border Protection is paying General Dynamics to create prototype "quantum sensors," to be used with an AI database to detect fentanyl and other narcotics. United States Customs and Border Protection is paying General Dynamics to create a prototype of "quantum sensors" alongside a "database with artificial intelligence " designed "to detect illicit objects and substances (such as fentanyl) in vehicles, containers, and other devices," according to a contract justification published in a federal register last week. "This database and sensor project will integrate advanced quantum and classical sensing technologies with Artificial Intelligence and ultimately deploy proven concepts and end products anywhere in the CBP environment," the justification document reads. "Under this requirement, CBP will take additional steps to enhance its ability to detect, and thus, significantly reduce the harms of illicit contraband entering the United States of America, thus bolstering national security." The document redacts the name of the company developing the prototype; however, contract details included in the federal register entry reveal that the justification is for a $2.4 million General Dynamics contract that has been public since December 2025.

Quantum navigation could solve the military's GPS jamming problem The rise of GPS vulnerability is putting more resilient, atom-based navigational tools on the map. The Royal Navy partnered with Infleqtion to test a quantum clock on the uncrewed submarine XV Excalibur. In late September, a Spanish military plane carrying the country's defense minister to a base in Lithuania was reportedly the subject of a kind of attack --not by a rocket or anti-aircraft rounds, but by radio transmissions that jammed its GPS system. The flight landed safely, but it was one of thousands that have been affected by a far-reaching Russian campaign of GPS interference since the 2022 invasion of Ukraine. The growing inconvenience to air traffic and risk of a real disaster have highlighted the vulnerability of GPS and focused attention on more secure ways for planes to navigate the gauntlet of jamming and spoofing, the term for tricking a GPS receiver into thinking it's somewhere else. US military contractors are rolling out new GPS satellites that use stronger, cleverer signals, and engineers are working on providing better navigation information based on other sources, like cellular transmissions and visual data.

Large language models (LLM) exhibit broad utility but face limitations in quantum sensor development, stemming from interdisciplinary knowledge barriers and involving complex optimization processes. Here we present QCopilot, an LLM-based multi-agent framework integrating external knowledge access, active learning, and uncertainty quantification for quantum sensor design and diagnosis. Comprising commercial LLMs with few-shot prompt engineering and vector knowledge base, QCopilot employs specialized agents to adaptively select optimization methods, automate modeling analysis, and independently perform problem diagnosis. Applying QCopilot to atom cooling experiments, we generated 10${}^{\rm{8}}$ sub-$\rmμ$K atoms without any human intervention within a few hours, representing $\sim$100$\times$ speedup over manual experimentation. Notably, by continuously accumulating prior knowledge and enabling dynamic modeling, QCopilot can autonomously identify anomalous parameters in multi-parameter experimental settings. Our work reduces barriers to large-scale quantum sensor deployment and readily extends to other quantum information systems.

Quantum sensing harnesses the unique properties of quantum systems to enable precision measurements of physical quantities such as time, magnetic and electric fields, acceleration, and gravitational gradients well beyond the limits of classical sensors. However, identifying suitable sensing probes and measurement schemes can be a classically intractable task, as it requires optimizing over Hilbert spaces of high dimension. In variational quantum sensing, a probe quantum system is generated via a parameterized quantum circuit (PQC), exposed to an unknown physical parameter through a quantum channel, and measured to collect classical data. PQCs and measurements are typically optimized using offline strategies based on frequentist learning criteria. This paper introduces an adaptive protocol that uses Bayesian inference to optimize the sensing policy via the maximization of the active information gain. The proposed variational methodology is tailored for non-asymptotic regimes where a single probe can be deployed in each time step, and is extended to support the fusion of estimates from multiple quantum sensing agents.

Quantum sensing exploits non-classical effects to overcome limitations of classical sensors, with applications ranging from gravitational-wave detection to nanoscale imaging. However, practical quantum sensors built on noisy intermediate-scale quantum (NISQ) devices face significant noise and sampling constraints, and current variational quantum sensing (VQS) methods lack rigorous performance guarantees. This paper proposes an online control framework for VQS that dynamically updates the variational parameters while providing deterministic error bars on the estimates. By leveraging online conformal inference techniques, the approach produces sequential estimation sets with a guaranteed long-term risk level. Experiments on a quantum magnetometry task confirm that the proposed dynamic VQS approach maintains the required reliability over time, while still yielding precise estimates. The results demonstrate the practical benefits of combining variational quantum algorithms with online conformal inference to achieve reliable quantum sensing on NISQ devices.

Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models, while preserving data privacy. Although recent advances in QFL span different tasks like classification while leveraging several data types, no prior work has focused on developing a QFL framework that utilizes temporal data to approximate functions useful to analyze the performance of distributed quantum sensing networks. In this paper, a novel QFL framework that is the first to integrate quantum long short-term memory (QLSTM) models with temporal data is proposed. The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation. In this regard, three key use cases are presented: Bessel function approximation, sinusoidal delayed quantum feedback control function approximation, and Struve function approximation. Simulation results confirm that, for all considered use cases, the proposed FedQLSTM framework achieves a faster convergence rate under one local training epoch, minimizing the overall computations, and saving 25-33% of the number of communication rounds needed until convergence compared to an FL framework with classical LSTM models.

Researchers in the UK have developed an autonomous machine learning algorithm that dramatically simplifies quantum systems. Researchers at the University of Bristol's Quantum Engineering Technology Labs (QETLabs) developed a new protocol to formulate and validate approximate models for quantum systems of interest. The Quantum Model Learning Agent (QMLA) algorithm works autonomously, designing and performing experiments on the targeted quantum system, with the resultant data being fed back into the algorithm. It proposes candidate Hamiltonian models to describe the target system, and distinguishes between them using statistical metrics, namely Bayes factors. The researchers were able to use the algorithm on a real-life quantum experiment involving defect centres in a diamond, a well-studied platform for quantum information processing and quantum sensing.

Thales launched InnovDays in 2012 as a way for its customers and partners to gain exclusive insights into the technological excellence of its research facilities. Since then, successive InnovDays events have turned the spotlight on the latest technological innovations developed by more than 29,500 Thales engineers and researchers. This press release features multimedia. InnovDays is a reflection of Thales's objective of leveraging science and high technology to build a future we can all trust. By harnessing the unprecedented precision and power of quantum physics and augmenting the performance of its systems thanks to artificial intelligence, Thales is developing the new generation of technologies that will shape the world of tomorrow.