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Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization directions of local models. Aggregating local models usually results in a low-generalized global model, which performs worse on most of the clients. To address the above issue, inspired by the observation from a geometric perspective that a well-generalized solution is located in a flat area rather than a sharp area, we propose a novel and heuristic FL paradigm named FedMR (Federated Model Recombination). The goal of FedMR is to guide the recombined models to be trained towards a flat area. Unlike conventional FedAvg-based methods, in FedMR, the cloud server recombines collected local models by shuffling each layer of them to generate multiple recombined models for local training on clients rather than an aggregated global model. Since the area of the flat area is larger than the sharp area, when local models are located in different areas, recombined models have a higher probability of locating in a flat area. When all recombined models are located in the same flat area, they are optimized towards the same direction. We theoretically analyze the convergence of model recombination. Experimental results show that, compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without exposing the privacy of each client. 

Neutral atom arrays have become a promising platform for quantum computing, especially the field programmable qubit array (FPQA) endowed with the unique capability of atom movement. This feature allows dynamic alterations in qubit connectivity during runtime, which can reduce the cost of executing long-range gates and improve parallelism. However, this added flexibility introduces new challenges in circuit compilation. Inspired by the placement and routing strategies for FPGAs, we propose to map all data qubits to fixed atoms while utilizing movable atoms to route for 2-qubit gates between data qubits. Coined flying ancillas, these mobile atoms function as ancilla qubits, dynamically generated and recycled during execution. We present Q-Pilot, a scalable compiler for FPQA employing flying ancillas to maximize circuit parallelism. For two important quantum applications, quantum simulation and the Quantum Approximate Optimization Algorithm (QAOA), we devise domain-specific routing strategies. In comparison to alternative technologies such as superconducting devices or fixed atom arrays, Q-Pilot effectively harnesses the flexibility of FPQA, achieving reductions of 1.4x, 27.7x, and 6.3x in circuit depth for 100-qubit random, quantum simulation, and QAOA circuits, respectively. 

We present a systematic framework for real-time risk-based optimization via multi-parametric programming. A dynamic risk indicator is utilized to monitor online process safety performance and provide model-based prediction of risk propagation, as a function of safety-critical process variables. Risk-based explicit/multi-parametric model predictive control is then developed to generate fit-for-purpose control strategies for proactive risk management. Given the probabilistic nature of risk, the controller design is extended to adapt a chance-constrained programming setting coupled with Bayesian inference for continuous risk updating along the rolling time horizon. A hierarchical dynamic optimization formulation is further developed to integrate risk control, operational optimization, and fault prognosis across multiple temporal scales in an integral but computationally efficient manner. If a potential fault is detected and cannot be prevented by adjusting operating actions, an alarm will be raised well ahead of time with the controller and optimizer continuously performing to attenuate the fault propagation speed and severity. The potential and efficacy of the proposed framework are demonstrated on three safety-critical case studies with increasing level of complexity: (i) Tank filling, (ii) Batch reactor at T2 Laboratories, and (iii) Cyber-physical hydrogen water electrolysis prototype.