The rapid execution of complex quantum circuit simulations is essential for validating theoretical algorithms, thereby facilitating their successful implementation on quantum hardware. Although mainstream CPU-based platforms for circuit simulations are well established, they tend to be slower. Conversely, the adoption of GPU platforms remains limited because of the necessity for specialized quantum simulation frameworks tailored to different hardware architectures, each requiring distinct implementation and optimization strategies. Therefore, we introduced Q-Gear, a platform-agnostic framework that transforms Qiskit quantum circuits into Cuda-Q kernels. By leveraging Cuda-Q seamless execution on GPUs, Q-Gear accelerates both CPU- and GPU-based simulations by two orders of magnitude and ten times, respectively, with minimal coding effort. Furthermore, Q-Gear leverages the Cuda-Q configuration to interconnect the memory of GPUs, allowing the execution of much larger circuits beyond the memory limit set by a single GPU or CPU node. Additionally, we created and deployed a Podman container and Shifter image at Perlmutter (NERSC/LBNL), both derived from an NVIDIA public image. These public NERSC containers were optimized for the Slurm job scheduler, allowing approximately 100% utilization of up to 1,024 GPUs. We present various benchmarks for Q-Gear to demonstrate the efficiency of our computational paradigm.

The “Lala-type” copper-iron deposits in the Lala area are primarily located in the central part of the Kangdian tectonic zone, distributed along an East-West (EW) trending Neoproterozoic gabbro-dolerite belt that is prominently controlled by regional EW-oriented tectonic structures. The genesis of these deposits is widely believed to be closely related to ancient volcanic structures, but their relationship with deep-seated basic intrusive rocks remains highly controversial. This article proposes an improved 3-D re-weighting regularized conjugate gradient (RRCG) focusing inversion method, constrained by the background field to recover the anomaly with a high resolution. The magnetic structure in a real case indicates the presence of a distinctly oriented basic intrusive rock mass in the deep part of the “Lala-type” Shilong copper deposit. Large-scale, high-precision magnetic profiles and magnetotelluric inversion results show that this strongly magnetic and high-resistance intrusive rock mass, which extends to a depth of 1.5 km, has transformed the ore-bearing basement into a clearly imaged anticlinal uplift and fold structure. Trenches and drill holes reveal that the ore bodies are primarily located in the fold and detachment spaces formed at the intersections of EW and North-South (NS) faults. The breccia formed by the basic rock intrusion along the faults provides favorable conditions for the occurrence and enrichment of ore bodies. The multiple intrusive thermal events in the Lala area not only supplied the fluids necessary for the enrichment of the deposits but also facilitated the transformation of the basement and the formation of ore-bearing spaces.

Salt domes are very important in hydrocarbon exploration and identification of potential drilling hazards. While seismic data is indispensable for detailed subsurface imaging, especially in delineating the geometry and properties of salt bodies and their boundaries, gravity inversion provides an additional layer of data by exploiting the density differential. However, traditional methodologies for tackling this problem are complicated by the ill-posedness of the inverse problems. The alternative approach to gravity image is based on machine learning algorithms. Despite the appealing attributes of Convolutional Neural Networks, they are not exempt from limitations, including diminished precision in pinpointing geological features, complications in managing the varying scales of geological structures, and inefficiencies in processing voluminous, high-dimensional data. These deficits can be mitigated by the proposed multi-scale functional MS-UNets network, which, through integration with Squeeze-and-Excitation and Strip Pooling modules, are designed to enhance the capture of detailed information about salt domes. These networks were subjected to rigorous testing using both synthetic and real gravity data, showcasing their robustness across diverse scenarios. This testing highlighted their significant potential for applications in geophysical data interpretation, structural modeling, and inversion processes.

With the rapid development of the Internet of Things (IoT), more IoT devices are connected in the same network and communicate frequently for sharing data and switching instructions. So that the traditional centralized security architecture of IoT will be limited in terms of data storage space, data reliability, scalability, and operating costs. In this article, we propose a novel communication data management model based on blockchain technology. Specifically, all encrypted IoT communication data files are uploaded to the public cloud server for obtaining enough storage space, but the key information extracted from these data files (called “communication logs”) will be recorded in an “IoT ledger” maintained by all IoT devices. In fact, the IoT ledger is a kind of blockchain structured distributed database, which can solve the problem of data reliability, scalability and would not involve high operating costs. Besides, in order to efficiently search communication logs and not reveal any sensitive information of communication data, we design a secure search scheme over such blockchain database, the asymmetric scalar-product-preserving encryption (ASPE) approach is exploited to guarantee the data security, and the two layers index improves the search efficiency. Security analysis and experiments on synthetic data set show that our scheme is secure and efficient.

Searchable encryption (SE) is a powerful technology that enables keyword-based search over encrypted data becomes possible. However, most SE schemes focus on exact keyword search which can not tolerate misspellings and typos. Existing fuzzy keyword search schemes only support fuzzy search within a limited similarity threshold$d$, the storage cost will grow exponentially or the precision of search results will greatly decrease as$d$increases. Moreover, the current fuzzy keyword ranked search schemes consider only the keyword weight, and disregard the influence of keyword morphology similarity on the ranking. In this article, we propose a scalable fuzzy keyword ranked search scheme over encrypted data under hybrid clouds architecture. We use the edit distance to measure the similarity of keywords and design an edit distance algorithm over encrypted data, in which our scheme achieves fuzzy keyword search for any similarity threshold$d$with a constant storage size and accurate search results. Furthermore, we design a two-factor ranking function combining keyword weight with keyword morphology similarity, which is utilized to rank the search results and enhance system usability. Extensive experiments are performed to demonstrate the trade-off of efficiency and security of the proposed scheme.

Due to a variety of advantages of data outsourcing, some Location Based Services (LBS) providers are motivated to outsource the geographic data and query service to commercial cloud. However, for protecting data confidentiality, the valuable data should be encrypted before outsourcing, which obstructs the utilization like geographic information query. To address this problem, some previous works regarding to secure search on encrypted database could be applied in outsourced LBS scenario directly, but none of them is tailor-made for linear region search (LRS). The LRS is a kind of LBS that widely used in navigation system, it finds the nearby points of interest (POI) for a query segment. In this paper, for the first time, we explore and solve the challenging problem of privacy-preserving linear region search. Specifically, we choose the quadtree structure to build index for POI database, then the results of LRS can be efficiently obtained by finding out the rectangular regions that query segment passes through. In order to preserve the privacy of both LBS providers and users, according to computational geometry and Asymmetric Scalar-product Preserving Encryption (ASPE) approach, we design a novel algorithm for accurately determining whether a segment intersects with a rectangle on ciphertext. Moreover, this algorithm also provides a new idea to solve other computational problems in encrypted 2-dimensional geometry space. Based on different privacy requirements of two threat models, we propose two privacy-preserving LRS schemes and corresponding dynamic update operations. Security analysis and experiments on real-world dataset show that our schemes are secure and efficient.

The recent outbreak of industrial cyberattacks indicates that the current industrial network security architecture is under serious challenges. As one of the critical industrial networks, the heterogeneous and real-time substation network lacks compatibility with the conventional cryptography architecture represented by secure sockets layer/transport layer security (SSL/TLS) and public key infrastructure (PKI). To enhance the security of smart substations under the premise of low latency, in this article, we present a novel encryption-as-a-service architecture based on fog computing in this article. The architecture offloads encryption to dedicated devices and makes certificate and key management available through unified web services on the fog and cloud layers. Based on this architecture, we propose MX-SORTS, maximizing security on real-time communication of different services, an algorithm for adaptive configuration of encrypting and signing substation network traffic. By the contrast experiments with the conventional cryptography architecture, we prove that the encryption-as-a-service architecture can significantly improve the real-time and security performance of substation networks.