This research addresses the implementation of learning algorithms and generative design in string-based topology exploration methods. It aims to generate diverse structural patterns for shells and surface structures that align architectural, engineering, and construction objectives. By integrating reinforcement learning (RL) and quad-mesh grammars, surface topology is explored through quantitative metrics, demonstrating the strength and generality of this approach. The research ultimately promotes creative exploration during the conceptual stages of structural design, emphasizing collaboration between form-designers and form-analyzers to harness emerging computational techniques.

The quad-mesh grammar was first formulated within a Markovian decision framework to integrate with open-source RL Python packages. States, actions, and rewards were defined with sufficient generality to avoid over fitting while evaluating the RL agent's ability to navigate between two specified string-action sequences and their associated mesh layouts. Initially, simple tasks involving four design steps were tested, followed by more generalized target terminal states with longer design sequences. The impact of different reward structures and varied model parameter setups on convergence and accumulated rewards was also analyzed.

The findings indicate that reward functions based solely on topological and grammatical characteristics did not fully guide the agent from an initial coarse mesh to a target state. However, extended design episodes demonstrated potential for improved RL outcomes. The DQN struggled with non-optimal policies due to negative rewards and sparse positive reinforcement, suggesting that customized model architectures or alternative RL algorithms could enhance performance. The exploration phases yielded suboptimal but diverse mesh configurations, highlighting the need for additional structural and geometric parameters, as well as more complex grammar operations to improve diversity while mitigating computational challenges. These insights underscore the importance of balancing feasibility, exploration, and optimization in computational design workflows.

We detect correlations in qubit-energy fluctuations of non-neighboring qubits defined in isotopically purified Si/Si-Ge quantum dots. At low frequencies (where the noise is strongest), the correlation coefficient reaches 10% for a next-nearest-neighbor qubit-pair separated by 200 nm. Correlations with the charge-sensor signal reach up to 70%, proving that the observed noise is of electrical origin. A simple theoretical model quantitatively reproduces the measurements and predicts a polynomial decay of correlations with interqubit distance. Our results quantify long-range correlations of noise in quantum-dot spin-qubit arrays, essential for their scalability and fault tolerance.

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