Large-Batch, Neural Multi-Objective Bayesian Optimization

Bayesian optimization provides a powerful framework for global optimization of black-box, expensive-to-evaluate functions. However, it has a limited capacity in handling data-intensive problems, especially in multi-objective settings, due to the poor scalability of default Gaussian Process surrogates. We present a novel Bayesian optimization framework specifically tailored to address these limitations. Our method leverages a Bayesian neural networks approach for surrogate modeling. This enables efficient handling of large batches of data, modeling complex problems, and generating the uncertainty of the predictions. In addition, our method incorporates a scalable, uncertainty-aware acquisition strategy based on the well-known, easy-to-deploy NSGA-II. This fully parallelizable strategy promotes efficient exploration of uncharted regions. Our framework allows for effective optimization in data-intensive environments with a minimum number of iterations. We demonstrate the superiority of our method by comparing it with state-of-the-art multi-objective optimizations. We perform our evaluation on two real-world problems - airfoil design and color printing - showcasing the applicability and efficiency of our approach. Code is available at: https://github.com/an-on-ym-ous/lbn\_mob

Reducing destructive effects of drought stress on cucumber through seed priming with silicic acid, pyridoxine, and ascorbic acid along with foliar spraying with silicic acid

Cucumber is considered as a drought-sensitive plant so that a decrease in the soil moisture causes decreased yield and quality of cucumber. This study investigates the effects of seed priming and foliar application with silicic acid on biochemical traits of cucumber (Cucumis sativus L.) under drought stress through a split-split plot experiment with three replications. The main plot was allocated to different levels of drought stress including moderate drought stress (80-85% Field Capacity (FC)), severe drought stress (60-65% FC), and without stress – control (90-95% FC). The sub-plot was allocated to seed priming treatments at three levels: control (hydro-priming), ascorbic acid 150 mgL-1, and pyridoxine 0.04%. The sub-sub plot was assigned to foliar spraying with silicic acid at three levels: 0, 100, and 200 mgL-1. The results obtained from the evaluation of all traits showed that under free-stress condition, the best seed priming treatment belonged to pyridoxine 0.04% alone or along with foliar spraying of silicic acid at 100 mgL-1. In moderate drought stress, the best seed priming treatment belonged to pyridoxine 0.04% and foliar spraying with silicic acid at 200 mgL-1, and under severe drought stress, the best seed priming treatment belonged to pyridoxine 0.04% or ascorbic acid at 150 mgL-1

TrustMol: Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics

Data-driven generation of molecules with desired properties, also known as inverse molecular design (IMD), has attracted significant attention in recent years. Despite the significant progress in the accuracy and diversity of solutions, existing IMD methods lag behind in terms of trustworthiness. The root issue is that the design process of these methods is increasingly more implicit and indirect, and this process is also isolated from the native forward process (NFP), the ground-truth function that models the molecular dynamics. Following this insight, we propose TrustMol, an IMD method built to be trustworthy. For this purpose, TrustMol relies on a set of technical novelties including a new variational autoencoder network. Moreover, we propose a latent-property pairs acquisition method to effectively navigate the complexities of molecular latent optimization, a process that seems intuitive yet challenging due to the high-frequency and discontinuous nature of molecule space. TrustMol also integrates uncertainty-awareness into molecular latent optimization. These lead to improvements in both explainability and reliability of the IMD process. We validate the trustworthiness of TrustMol through a wide range of experiments

Quantum-limited time-frequency estimation through mode-selective photon measurement

By projecting onto complex optical mode profiles, it is possible to estimate arbitrarily small separations between objects with quantum-limited precision, free of uncertainty arising from overlapping intensity profiles. Here we extend these techniques to the time-frequency domain using mode-selective sum-frequency generation with shaped ultrafast pulses. We experimentally resolve temporal and spectral separations between incoherent mixtures of single-photon level signals ten times smaller than their optical bandwidths with a ten-fold improvement in precision over the intensity-only Cram\'er-Rao bound.Comment: Six pages, three figures. Comments welcome

Realization of high-fidelity unitary operations on up to 64 frequency bins

The ability to apply user-chosen large-scale unitary operations with high fidelity to a quantum state is key to realizing future photonic quantum technologies. Here, we realize the implementation of programmable unitary operations on up to 64 frequency-bin modes. To benchmark the performance of our system, we probe different quantum walk unitary operations, in particular Grover walks on four-dimensional hypercubes with similarities exceeding 95\% and quantum walks with 400 steps on circles and finite lines with similarities of 98\%. Our results open a new path towards implementing high-quality unitary operations, which can form the basis for applications in complex tasks, such as Gaussian boson sampling

An Optimized Photon Pair Source for Quantum Circuits

We implement an ultrafast pulsed type-II parametric down conversion source in a periodically poled KTP waveguide at telecommunication wavelengths with almost identical properties between signal and idler. As such, our source resembles closely a pure, genuine single mode photon pair source with indistinguishable modes. We measure the joint spectral intensity distribution and second order correlation functions of the marginal beams and find with both methods very low effective mode numbers corresponding to a Schmidt number below 1.16. We further demonstrate the indistinguishability as well as the purity of signal and idler photons by Hong-Ou-Mandel interferences between signal and idler and between signal/idler and a coherent field, respectively. Without using narrowband spectral filtering, we achieve a visibility for the interference between signal and idler of 94.8% and determine a purity of more than 80% for the heralded single photon states. Moreover, we measure raw heralding efficiencies of 20.5% and 15.5% for the signal and idler beams corresponding to detector-loss corrected values of 80% and 70%.Comment: 11 pages, 8 figure