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We study a model-free federated linear quadratic regulator (LQR) problem where M agents with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to minimize an average quadratic cost while keeping their data private. To exploit the similarity of the agents' dynamics, we propose to use federated learning (FL) to allow the agents to periodically communicate with a central server to train policies by leveraging a larger dataset from all the agents. With this setup, we seek to understand the following questions: (i) Is the learned common policy stabilizing for all agents? (ii) How close is the learned common policy to each agent's own optimal policy? (iii) Can each agent learn its own optimal policy faster by leveraging data from all agents? To answer these questions, we propose a federated and model-free algorithm named FedLQR. Our analysis overcomes numerous technical challenges, such as heterogeneity in the agents' dynamics, multiple local updates, and stability concerns. We show that FedLQR produces a common policy that, at each iteration, is stabilizing for all agents. We provide bounds on the distance between the common policy and each agent's local optimal policy. Furthermore, we prove that when learning each agent's optimal policy, FedLQR achieves a sample complexity reduction proportional to the number of agents M in a low-heterogeneity regime, compared to the single-agent setting. 

Ecosystem conservation is fundamental to guarantee the survival of endangered species and to preserve other ecological functions important for human systems (e.g., water). Planning land conservation increasingly requires a landscape approach to mitigate the negative impacts of spatial threats such as urbanization, agricultural development, and climate change. In this context, landscape connectivity and compactness are vital characteristics for the effective functionality of conservation areas. Connectivity allows species to travel across landscapes, facilitating the flow of genes across populations from different protected areas. Compactness measures the spatial dispersion of protected sites, which can be used to mitigate risk factors associated with species leaving and reentering the reserve. This research describes an optimization model for the design of conservation areas, while inducing connectivity and compactness. We use the Reocks index, a metric of compactness that maximizes the ratio of area of the selected patches to the area of their smallest circumscribing circle. Our model includes budget and minimum protected area constraints to reflect realistic financial and ecological requirements. The initial nonlinear model is reformulated into a mixed-integer linear program, which is solved using an adaptation of the Newtons method for problems with integer variables. We characterize an optimal solution and derive cuts to improve the model performance. We illustrate our results using real life landscapes with irregular patches. 

We initiate the study of federated reinforcement learning under environmental heterogeneity by considering a policy evaluation problem. Our setup involves agents interacting with environments that share the same state and action space but differ in their reward functions and state transition kernels. Assuming agents can communicate via a central server, we ask: Does exchanging information expedite the process of evaluating a common policy? To answer this question, we provide the first comprehensive finite-time analysis of a federated temporal difference (TD) learning algorithm with linear function approximation, while accounting for Markovian sampling, heterogeneity in the agents' environments, and multiple local updates to save communication. Our analysis crucially relies on several novel ingredients: (i) deriving perturbation bounds on TD fixed points as a function of the heterogeneity in the agents' underlying Markov decision processes (MDPs); (ii) introducing a virtual MDP to closely approximate the dynamics of the federated TD algorithm; and (iii) using the virtual MDP to make explicit connections to federated optimization. Putting these pieces together, we rigorously prove that in a low-heterogeneity regime, exchanging model estimates leads to linear convergence speedups in the number of agents. 

We revisit the implementation of a two-qubit entangling gate, the Mølmer-Sørensen gate, using the adiabatic Rydberg dressing paradigm. We study the implementation of rapid adiabatic passage using a two-photon transition, which does not require the use of an ultra-violet laser, and can be implemented using only amplitude modulation of one field with all laser frequencies fixed. We find that entangling gate fidelities, comparable to the one-photon excitation, are achievable with the two-photon excitation. Moreover, we address how the adiabatic dressing protocol can be used to implement entangling gates outside the regime of a perfect Rydberg blockade. We show that using adiabatic dressing we can achieve a scaling of gate fidelity set by the fundamental limits to entanglement generated by the Rydberg interactions while simultaneously retaining limited population in the doubly excited Rydberg state. This allows for fast high fidelity gates for atoms separated beyond the blockade radius. 

This presentation reports different methods of making gallium-based liquid metal (LM) microfluidics passive frequency selective surfaces (FSS). In the first method Si wafer was dry-etched to form a mold and PDMS was replicated from the Si mold to create microfluidic channels with 5x5 array of 300μm width and 200 μm height for Jerusalem cross bars structure, surrounded by four fixed 2 x 1 x 0.2 mm structures. A PDMS lid having 1 mm diameter holes obtained from SLA 3D printed pillar array was aligned and bonded to the replicated PDMS to create sealed microfluidic channels. The bonded structure was placed with lid upwards in an open top 3D printed container measuring 64mm x 64mmarea and 25 mm height. LM was flooded into the container and loaded in Temescal e-beam evaporator at atmospheric pressure. Pressure in evaporator was dropped to 5.75 x 10-6Torr. After a vacuum period of 2 hours LM filling takes place in microfluidic structures because of positive pressure differential introduced by atmospheric pressure. In second method 70 μmthick SU8-2075 stencil consisting of a patterned 1x1 array of see-through FSS structure of above-mentioned dimensions was released from oxidized Si wafer using7:1 BOE. The SU8-2075 stencil was placed over a partially cured PDMS. After complete PDMS curing, an airbrush filled with LM operating at 36 psi with spraying time of less than 5 seconds, placed 4-5 cm over the stencil yields the patterned 1x1 FSS structure after removal of SU8-2075. 

Abstract The DarkSide-20k dark matter experiment, currently under construction at LNGS, features a dual-phase time projection chamber (TPC) with a ∼ 50 t argon target from an underground well. At this scale, it is crucial to optimise the argon flow pattern for efficient target purification and for fast distribution of internal gaseous calibration sources with lifetimes of the order of hours. To this end, we have performed computational fluid dynamics simulations and heat transfer calculations. The residence time distribution shows that the detector is well-mixed on time-scales of the turnover time (∼ 40 d). Notably, simulations show that despite a two-order-of-magnitude difference between the turnover time and the half-life of^83mKr of 1.83 h, source atoms have the highest probability to reach the centre of the TPC 13 min after their injection, allowing for a homogeneous distribution before undergoing radioactive decay. We further analyse the thermal aspects of dual-phase operation and define the requirements for the formation of a stable gas pocket on top of the liquid. We find a best-estimate value for the heat transfer rate at the liquid-gas interface of 62 W with an upper limit of 144 W and a minimum gas pocket inlet temperature of 89 K to avoid condensation on the acrylic anode. This study also informs the placement of liquid inlets and outlets in the TPC. The presented techniques are widely applicable to other large-scale, noble-liquid detectors. 

Abstract DarkSide-20k is a novel liquid argon dark matter detector currently under construction at the Laboratori Nazionali del Gran Sasso (LNGS) of the Istituto Nazionale di Fisica Nucleare (INFN) that will push the sensitivity for Weakly Interacting Massive Particle (WIMP) detection into the neutrino fog. The core of the apparatus is a dual-phase Time Projection Chamber (TPC), filled with 50 tonnes of low radioactivity underground argon (UAr) acting as the WIMP target. NUV-HD-cryo Silicon Photomultipliers (SiPM)s designed by Fondazione Bruno Kessler (FBK) (Trento, Italy) were selected as the photon sensors covering two$$10.5~\text {m}^2$$ $10.5{\text{m}}^2$Optical Planes, one at each end of the TPC, and a total of$$5~\text {m}^2$$ $5{\text{m}}^2$photosensitive surface for the liquid argon veto detectors. This paper describes the Quality Assurance and Quality Control (QA/QC) plan and procedures accompanying the production of FBK NUV-HD-cryo SiPM wafers manufactured by LFoundry s.r.l. (Avezzano, AQ, Italy). SiPM characteristics are measured at 77 K at the wafer level with a custom-designed probe station. As of March 2025, 1314 of the 1400 production wafers (94% of the total) for DarkSide-20k were tested. The wafer yield is$$93.2\pm 2.5$$ $93.2\pm 2.5$%, which exceeds the 80% specification defined in the original DarkSide-20k production plan. 

DarkSide-20k (DS-20k) is a dark matter detection experiment under construction at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It utilises ∼ 100 t of low radioactivity argon from an underground source (UAr) in its inner detector, with half serving as target in a dual-phase time projection chamber (TPC). The UAr cryogenics system must maintain stable thermodynamic conditions throughout the experiment's lifetime of over 10 years. Continuous removal of impurities and radon from the UAr is essential for maximising signal yield and mitigating background. We are developing an efficient and powerful cryogenics system with a gas purification loop with a target circulation rate of 1000 slpm. Central to its design is a condenser operated with liquid nitrogen which is paired with a gas heat exchanger cascade, delivering a combined cooling power of more than 8 kW. Here we present the design choices in view of the DS-20k requirements, in particular the condenser's working principle and the cooling control, and we show test results obtained with a dedicated benchmarking platform at CERN and LNGS. We find that the thermal efficiency of the recirculation loop, defined in terms of nitrogen consumption per argon flow rate, is 95 % and the pressure in the test cryostat can be maintained within ±(0.1–0.2) mbar. We further detail a 5-day cool-down procedure of the test cryostat, maintaining a cooling rate typically within -2 K/h, as required for the DS-20k inner detector. Additionally, we assess the circuit's flow resistance, and the heat transfer capabilities of two heat exchanger geometries for argon phase change, used to provide gas for recirculation. We conclude by discussing how our findings influence the finalisation of the system design, including necessary modifications to meet requirements and ongoing testing activities. 

The dual-phase liquid argon time projection chamber is presently one of the leading technologies to search for dark matter particles with masses below 10 GeV c−2. This was demonstrated by the DarkSide-50 experiment with approximately 50 kg of low-radioactivity liquid argon as target material. The next generation experiment DarkSide-20k, currently under construction, will use 1,000 times more argon and is expected to start operation in 2027. Based on the DarkSide-50 experience, here we assess the DarkSide-20k sensitivity to models predicting light dark matter particles, including Weakly Interacting Massive Particles (WIMPs) and sub-GeV c−2 particles interacting with electrons in argon atoms. With one year of data, a sensitivity improvement to dark matter interaction cross-sections by at least one order of magnitude with respect to DarkSide-50 is expected for all these models. A sensitivity to WIMP–nucleon interaction cross-sections below 1 × 10−42 cm2 is achievable for WIMP masses above 800 MeV c−2. With 10 years exposure, the neutrino fog can be reached for WIMP masses around 5 GeV c−2.