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Abstract 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. Participants utilized the IBM Qiskit platform within the constraints of the Noisy Intermediate Scale Quantum (NISQ) era, characterized by noise and limited quantum computing resources. The contest emphasized the importance of accurate estimation, efficient use of quantum resources, and the integration of machine learning techniques. This competition highlighted the potential of hybrid classical-quantum frameworks and machine learning in advancing quantum computing for practical applications, particularly in drug discovery.

Abstract Computationally hard combinatorial optimization problems (COPs) are ubiquitous in many applications. Various digital annealers, dynamical Ising machines, and quantum/photonic systems have been developed for solving COPs, but they still suffer from the memory access issue, scalability, restricted applicability to certain types of COPs, and VLSI-incompatibility, respectively. Here we report a ferroelectric field effect transistor (FeFET) based compute-in-memory (CiM) annealer for solving larger-scale COPs efficiently. Our CiM annealer converts COPs into quadratic unconstrained binary optimization (QUBO) formulations, and uniquely accelerates in-situ the core vector-matrix-vector (VMV) multiplication operations of QUBO formulations in a single step. Specifically, the three-terminal FeFET structure allows for lossless compression of the stored QUBO matrix, achieving a remarkably 75% chip size saving when solving Max-Cut problems. A multi-epoch simulated annealing (MESA) algorithm is proposed for efficient annealing, achieving up to 27% better solution and ~ 2X speedup than conventional simulated annealing. Experimental validation is performed using the first integrated FeFET chip on 28nm HKMG CMOS technology, indicating great promise of FeFET CiM array in solving general COPs.

Abstract Congenital heart disease (CHD) is one of the leading causes of mortality among birth defects, and due to significant variations in the whole heart and great vessel, automatic CHD segmentation using CT images has been always under-researched. Even though some segmentation algorithms have been developed in the literature, none perform very well under the complex structure of CHD. To deal with the challenges, we take advantage of deep learning in processing regular structures and graph algorithms in dealing with large variations and propose a framework combining both the whole heart and great vessel segmentation in complex CHD. We benefit from deep learning in segmenting the four chambers and myocardium based on the blood pool, and then we extract the connection information and apply graph matching to determine the categories of all the vessels. Experimental results on 68 3D CT images covering 14 types of CHD illustrate our framework can increase the Dice score by 12% on average compared with the state-of-the-art whole heart and great vessel segmentation method in normal anatomy. We further introduce two cardiovascular imaging specialists to evaluate our results in the standard of the Van Praagh classification system, and achieves well performance in clinical evaluation. All these results may pave the way for the clinical use of our method in the incoming future.

Content addressable memory (CAM) is widely used in associative search tasks due to its parallel pattern matching capability. As more complex and data‐intensive tasks emerge, it is becoming increasingly important to enhance CAM density for improved performance and better area efficiency. To reduce the area overheads, various nonvolatile memory (NVM) devices, such as ferroelectric field‐effect transistors (FeFETs), are used in CAM design. Herein, a novel ultracompact 1FeFET CAM design that enables parallel associative search and in‐memory hamming distance calculation is used, as well as a multibit CAM for exact search using the same CAM cell. The proposed CAM design leverages the 1FeFET1R structure, and compact device designs that integrate the series resistor current limiter into the intrinsic FeFET structure are demonstrated to turn the 1FeFET1R structure into an effective 1FeFET cell. A two‐step search operation of the proposed binary and multibit 1FeFET CAM array through both experiments and simulations is proposed, showing a sufficient sensing margin despite unoptimized FeFET device variation. In genome pattern matching applications, using the hyperdimensional computing paradigm, the design results in a 89.9× speedup and 66.5× improvement in energy efficiency over the state‐of‐the‐art alignment tools on GPU.

BackgroundIn ST-segment elevation myocardial infarction (STEMI) with the restoration of TIMI 3 flow by percutaneous coronary intervention (PCI), visually defined microvascular obstruction (MVO) was shown to be the predictor of poor prognosis, but not an ideal risk stratification method. We intend to introduce deep neural network (DNN) assisted myocardial contrast echocardiography (MCE) quantitative analysis and propose a better risk stratification model.Methods194 STEMI patients with successful primary PCI with at least 6 months follow-up were included. MCE was performed within 48 h after PCI. The major adverse cardiovascular events (MACE) were defined as cardiac death, congestive heart failure, reinfarction, stroke, and recurrent angina. The perfusion parameters were derived from a DNN-based myocardial segmentation framework. Three patterns of visual microvascular perfusion (MVP) qualitative analysis: normal, delay, and MVO. Clinical markers and imaging features, including global longitudinal strain (GLS) were analyzed. A calculator for risk was constructed and validated with bootstrap resampling.ResultsThe time-cost for processing 7,403 MCE frames is 773 s. The correlation coefficients of microvascular blood flow (MBF) were 0.99 to 0.97 for intra-observer and inter-observer variability. 38 patients met MACE in 6-month follow-up. We proposed A risk prediction model based on MBF [HR: 0.93 (0.91–0.95)] in culprit lesion areas and GLS [HR: 0.80 (0.73–0.88)]. At the best risk threshold of 40%, the AUC was 0.95 (sensitivity: 0.84, specificity: 0.94), better than visual MVP method (AUC: 0.70, Sensitivity: 0.89, Specificity: 0.40, IDI: −0.49). The Kaplan-Meier curves showed that the proposed risk prediction model allowed for better risk stratification.ConclusionThe MBF + GLS model allowed more accurate risk stratification of STEMI after PCI than visual qualitative analysis. The DNN-assisted MCE quantitative analysis is an objective, efficient and reproducible method to evaluate microvascular perfusion.

In most popular public accessible cryptocurrency systems, the mining pool plays a key role because mining cryptocurrency with the mining pool turns the non-profitable situation into profitable for individual miners. In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload. Thus, the computation power of miners will not purely be spent on the hash puzzle. In this way, the hardware and energy will support the blockchain service and deep learning training simultaneously. While the incentive of miners is to earn tokens, individual miners are motivated to join mining pools to become more competitive. In this paper, we are the first to demonstrate a mining pool solution for novel consensuses based on deep learning.The mining pool manager partitions the full searching space into subspaces, and all miners are scheduled to collaborate on the Neural architecture search (NAS) tasks in the assigned subspace. Experiments demonstrate that the performance of this type of mining pool is more competitive than that of an individual miner. Due to the uncertainty of miners' behaviors, the mining pool manager checks the standard deviation of the performance of high reward miners and prepares backup miners to ensure completion of the tasks of high reward miners.

The advantages coming from involving quantum systems in machine learning are still not fully clear. Here, the authors propose a software/hardware co-design framework towards quantum-friendly neural networks showing quantum advantage, representing data as either random variables or numbers in unitary matrices.

Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences. As manual segmentation is tedious, time-consuming, and with low replicability, automatic MYO segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium. Specifically, we propose to combine conventional neural networks and recurrent neural networks to incorporate temporal information between sequences to ensure temporal consistency. We evaluated our framework on the automated cardiac diagnosis challenge (ACDC) dataset. The experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in the Dice coefficient.

Twisted light carrying orbital angular momentum inherently possesses a handedness, which would produce chiroptical responses by chiral matter. In this work, a scheme of vortical differential scattering (VDS) was utilized to investigate the chiroptical responses of dielectric chiral particles to the twisted light. The simulation results showed that the dielectric chiral particles have obvious VDS signals under the illumination of twisted light with opposite topological charges. The larger the relative chiral parameter of the particles, the more obvious the VDS signals. The extreme value of the VDS signals can be enhanced by reducing the waist radius of the twisted light or by adopting the circularly polarized twisted light. In addition, non-spherical dielectric chiral particles exhibit more obvious VDS signals compared with spherical ones. These findings are expected to find potential applications in the detection and identification of chiral substances.

Retinal vessel segmentation plays an important role in the diagnosis of eye-related diseases and biomarkers discovery. Existing works perform multi-scale feature aggregation in an inter-layer manner, namely inter-layer feature aggregation. However, such an approach only fuses features at either a lower scale or a higher scale, which may result in a limited segmentation performance, especially on thin vessels. This discovery motivates us to fuse multi-scale features in each layer, intra-layer feature aggregation, to mitigate the problem. Therefore, in this paper, we propose Pyramid-Net for accurate retinal vessel segmentation, which features intra-layer pyramid-scale aggregation blocks (IPABs). At each layer, IPABs generate two associated branches at a higher scale and a lower scale, respectively, and the two with the main branch at the current scale operate in a pyramid-scale manner. Three further enhancements including pyramid inputs enhancement, deep pyramid supervision, and pyramid skip connections are proposed to boost the performance. We have evaluated Pyramid-Net on three public retinal fundus photography datasets (DRIVE, STARE, and CHASE-DB1). The experimental results show that Pyramid-Net can effectively improve the segmentation performance especially on thin vessels, and outperforms the current state-of-the-art methods on all the adopted three datasets. In addition, our method is more efficient than existing methods with a large reduction in computational cost. We have released the source code at https://github.com/JerRuy/Pyramid-Net.

In this work, a series of Cu2O/S (S = α-MnO2, CeO2, ZSM-5, and Fe2O3) supported catalysts with a Cu2O loading amount of 15% were prepared by the facile liquid-phase reduction deposition–precipitation strategy and investigated as CO oxidation catalysts. It was found that the Cu2O/α-MnO2 catalyst exhibits the best catalytic activity for CO oxidation. Additionally, a series of Cu2O-CuO/α-MnO2 heterojunctions with varied proportion of Cu+/Cu2+ were synthesized by further calcining the pristine Cu2O/α-MnO2 catalyst. The ratio of the Cu+/Cu2+ could be facilely regulated by controlling the calcination temperature. It is worth noting that the Cu2O-CuO/α-MnO2-260 catalyst displays the best catalytic performance. Moreover, the kinetic studies manifest that the apparent activation energy could be greatly reduced owing to the excellent redox property and the Cu2O-CuO interface effect. Therefore, the Cu2O-CuO heterojunction catalysts supported on α-MnO2 nanotubes are believed to be the potential catalyst candidates for CO oxidation with advanced performance.

A series of CuO-based catalysts supported on the α-MnO2 nanowire were facilely synthesized and employed as the CO oxidation catalysts. The achieved catalysts were systematically characterized by XRD, SEM, EDS-mapping, XPS and H2-TPR. The catalytic performances toward CO oxidation had been carefully evaluated over these CuO-based catalysts. The effects of different loading methods, calcination temperatures and CuO loading on the low temperature catalytic activity of the catalyst were investigated and compared with the traditional commercial MnO2 catalyst with a block structure. It was found that the slenderness ratio of a CuO/α-MnO2 nanowire catalyst decreases with the increase in CuO loading capacity. The results showed that when CuO loading was 3 wt%, calcination temperature was 200 °C and the catalyst that was supported by the deposition precipitation method had the highest catalytic activity. Besides, the α-MnO2 nanowire-supported catalysts with excellent redox properties displayed much better catalytic performances than the commercial MnO2-supported catalyst. In conclusion, the CuO-based catalysts that are supported by α-MnO2 nanowires are considered as a series of promising CO oxidation catalysts.