Synergizing quantum techniques with machine learning for advancing drug discovery challenge.

The Quantum Computing for Drug Discovery Challenge, held at the 42nd International Conference on Computer-Aided Design (ICCAD) in 2023, was a multi-month, research-intensive competition. Over 70 teams from more than 65 organizations from 12 different countries registered, focusing on the use of quantum computing for drug discovery. The challenge centered on designing algorithms to accurately estimate the ground state energy of molecules, specifically OH+, using quantum computing techniques.

Selecting robust silicon photonic designs after Bayesian optimization without extra simulations.

Optimization methods are frequently exploited in the design of silicon photonic devices. In this paper, we demonstrate that pushing the objective function to its minimum during optimization often results in devices that gradually become more sensitive to perturbations of design variables. The dominant strategy of selecting the design with the smallest objective function can lead to fabrication failure or yield loss due to manufacturing process variations.

Active Sampling for Accelerated MRI with Low-Rank Tensors.

Magnetic resonance imaging (MRI) is a powerful imaging modality that revolutionizes medicine and biology. The imaging speed of high -dimensional MRI is often limited, which constrains its practical utility. Recently, low-rank tensor models have been exploited to enable fast MR imaging with sparse sampling.

A Time-Space Domain Information Fusion Method for Specific Emitter Identification Based on Dempster-Shafer Evidence Theory.

Specific emitter identification plays an important role in contemporary military affairs. However, most of the existing specific emitter identification methods haven't taken into account the processing of uncertain information. Therefore, this paper proposes a time-space domain information fusion method based on Dempster-Shafer evidence theory, which has the ability to deal with uncertain information in the process of specific emitter identification.

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion.

In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster-Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD).