大众集团

X-ray absorption spectroscopy (XAS) is a leading technique for understanding structural changes in advanced battery materials such as lithium-excess cathodes. However, extracting critical information like oxidation states from the experimental spectra requires expensive and time-consuming simulations. Building upon a recent proposal to simulate XAS using quantum computers, this work proposes a highly-optimized implementation of the time-domain algorithm for X-ray absorption. Among a host of improvements to Hamiltonian representation, circuit implementation, and measurement strategies, three optimizations are key to the efficiency of the algorithm. The first is the use of product formulas with the compressed double factorized form of the Hamiltonian. The second is recognizing that for spectroscopy applications, it is sufficient to control the error in the eigenvalues of the (approximate) Hamiltonian being implemented by the product formula, rather than the generic error on the full time evolution operator. Using perturbation theory to estimate this eigenvalue error, we find that significantly fewer Trotter steps are needed than expected from the time evolution error bound. The third is the choice of an optimized distribution of samples that takes advantage of the exponentially decaying Lorentzian kernel. Through constant factor resource estimates, we show that a challenging model Li 4 Mn 2 O cluster system with 18 spatial orbitals and 22 electrons in the active space can be simulated with 100 logical qubits and less than 4 × 10 8 Toffoli gates per circuit. Finally, the algorithm is implemented on a simulator, and the reconstructed spectrum is verified against a classical computational reference. The low cost of our algorithm makes it attractive to use on fault-tolerant quantum devices to accelerate the development and commercialization of high-capacity battery cathodes.

Existing time series tokenization methods predominantly encode a constant number of samples into individual tokens. This inflexible approach can generate excessive tokens for even simple patterns like extended constant values, resulting in substantial computational overhead. Inspired by the success of byte pair encoding, we propose the first pattern-centric tokenization scheme for time series analysis. Based on a discrete vocabulary of frequent motifs, our method merges samples with underlying patterns into tokens, compressing time series adaptively. Exploiting our finite set of motifs and the continuous properties of time series, we further introduce conditional decoding as a lightweight yet powerful post-hoc optimization method, which requires no gradient computation and adds no computational overhead. On recent time series foundation models, our motif-based tokenization improves forecasting performance by 36% and boosts efficiency by 1990% on average. Conditional decoding further reduces MSE by up to 44%. In an extensive analysis, we demonstrate the adaptiveness of our tokenization to diverse temporal patterns, its generalization to unseen data, and its meaningful token representations capturing distinct time series properties, including statistical moments and trends.

Safety- and security-critical systems have to be thoroughly tested against their specifications. The state of practice is to have natural-language specifications, from which test cases are derived manually—a process that is slow, error-prone, and difficult to scale. Formal specifications, on the other hand, are well-suited for automated test generation, but are tedious to write and maintain. In this work, we propose a two-stage pipeline that uses large language models (LLMs) to bridge the gap: First, we extract protocol elements from natural-language specifications; second, leveraging a protocol implementation, we synthesize and refine a formal protocol specification from these elements, which we can then use to massively test further implementations. We see this two-stage approach to be superior to end-to-end LLM-based test generation, as 1) it produces an inspectable specification that preserves traceability to the original text; 2) the generation of actual test cases no longer requires an LLM; 3) the resulting formal specs are human-readable, and can be reviewed, version-controlled, and incrementally refined; and 4) over time, we can build a corpus of natural-language-to-formal-specification mappings that can be used to further train and refine LLMs for more automatic translations. Our prototype, AutoFan, successfully demonstrated the feasibility of our approach on five widely used internet protocols(SMTP, POP3, IMAP, FTP, and ManageSieve) by applying its methods to their RFC specifications written in natural language, and the recent interaction grammar formalism for protocol specification and fuzzing. In its evaluation, AutoFan recovers on average 92.8% of client and 80.2% of server message types, and achieves 81.5% message acceptance across diverse, real-world systems—a first step toward automatic formalization of natural-language specifications for comprehensive test generation.

We evaluate the Sample-based Krylov Quantum Diagonalization (SKQD) algorithm on one- and two-dimensional Heisenberg models, including strongly correlated regimes in which the ground state is dense. Using problem-informed initial states and magnetization sector sweeps, we investigate SKQD for problems with non-sparse ground states, where energy accuracy and sampling efficiency are theoretically anticipated to degrade. Our studies reveal that SKQD reproduces ground-state energies and field-dependent magnetization across a range of anisotropies. Benchmarks against DMRG and exact diagonalization show consistent qualitative agreement, with accuracy improving systematically in more anisotropic regimes. We further demonstrate SKQD on quantum hardware by implementing 18- and 30-qubit Heisenberg chains, obtaining magnetization curves that match theoretical expectations. Simulations on the IBM Nighthawk processor for 64-qubit two-dimensional square lattice systems further indicate that the method remains effective beyond one-dimensional geometries.