Quantum generative adversarial networks (QGANs) have been studied in the context of quantum machine learning for several years, but there has not been yet a proposal for a fully QGAN with both, a quantum generator and discriminator. We introduce a fully QGAN intended for use with binary data. The architecture incorporates several features found in other classical and quantum machine learning models, which up to this point had not been used in conjunction.

The collective modes of two-dimensional ordered atomic arrays can modify the radiative environment of embedded atomic impurities. We analyze the role of the lattice geometry on the impurity's emission linewidth by comparing the effective impurity decay rate obtained for all noncentered Bravais lattices and an additional honeycomb lattice. We demonstrate that the lattice geometry plays a crucial role in determining the effective decay rate for the impurity.

Despite extensive research efforts, few quantum algorithms for classical optimization demonstrate a realizable
quantum advantage. The utility of many quantum algorithms is limited by high requisite circuit depth and non-
convex optimization landscapes. We tackle these challenges by introducing a variational quantum algorithm that
benefits from two innovations: multibasis graph encodings using single-qubit expectation values and nonlinear
activation functions. Our technique results in increased observed optimization performance and a factor-of-two

Modern VLSI design flows demand scalable global routing techniques applicable across diverse design stages. In response, the ISPD 2024 contest pioneers the first GPU/ML-enhanced global routing competition, selecting advancements in GPU-accelerated computing platforms and machine learning techniques to address scalability challenges. Large-scale benchmarks, containing up to 50 million cells, offer test cases to assess global routers' runtime and memory scalability.

State-of-the-art hypergraph partitioners, such as hMETIS, usually adopt a multi-level paradigm for efficiency and scalability. However, they are prone to getting trapped in local minima due to their reliance on refinement heuristics and overlooking global structural information during coarsening. SpecPart, the most advanced academic hypergraph partitioning refinement method, improves partitioning by leveraging spectral information. Still, its success depends heavily on the quality of initial input solutions.

We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups. Our approach can be instantly applied at test-time to a novel object without fine-tuning, as long as its CAD model is given, or a small number of reference images are captured. We bridge the gap between these two setups with a neural implicit representation that allows for effective novel view synthesis, keeping the downstream pose estimation modules invariant under the same unified framework.

This article presents an inverter-based short-reach ac-coupled toggle (ISR-ACT) link targeted for short-reach die-to-die communication over silicon interposer or similar high-density interconnect. The ISR-ACT’s transmitter (TX) sends non-return-to-zero (NRZ) data through a small series capacitor to inject low-swing pulses into the line. These pulses are amplified and latched by a two-stage receiver (RX), where the 1st-stage transimpedance amplifier (TIA) amplifies the pulse data and positive feedback around both stages captures the data and maintains the dc level on the line.

We present XCube, a novel generative model for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model can generate millions of voxels with a finest effective resolution of up to $1024^3$ in a feed-forward fashion without time-consuming test-time optimization. To achieve this, we employ a hierarchical voxel latent diffusion model which generates progressively higher resolution grids in a coarse-to-fine manner using a custom framework built on the highly efficient VDB data structure.