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@arXiv_quantph_bot@mastoxiv.page
2025-10-10 11:23:39

Quenching, Fast and Slow: Breaking Kibble-Zurek Universal Scaling by Jumping along Geodesics
Thi Ha Kyaw, Guillermo Romero, Gaurav Saxena
arxiv.org/abs/2510.08528

Quenching, Fast and Slow: Breaking Kibble-Zurek Universal Scaling by Jumping along Geodesics
A major drawback of adiabatic quantum computing (AQC) is fulfilling the energy gap constraint, which requires the total evolution time to scale inversely with the square of the minimum energy gap. Failure to satisfy this condition violates the adiabatic approximation, potentially undermining computational accuracy. Recently, several approaches have been proposed to circumvent this constraint. One promising approach is to use the family of adiabatic shortcut procedures to fast-forward AQC. One c…

@arXiv_physicscompph_bot@mastoxiv.page
2025-09-30 09:38:51

Interplay of Variance Reduction and Population Control in Monte Carlo Neutron Transport
Jordan Northrop, Ilham Variansyah, Todd Palmer, Camille Palmer
arxiv.org/abs/2509.22943

Interplay of Variance Reduction and Population Control in Monte Carlo Neutron Transport
Monte Carlo methods are widely used for neutron transport simulations at least partly because of the accuracy they bring to the modeling of these problems. However, the computational burden associated with the slow convergence rate of Monte Carlo poses a significant challenge to running large-scale simulations. The continued improvement in high-performance computing capabilities has put exascale time-dependent Monte Carlo neutron transport simulations within reach. Variance reduction techniques…

@arXiv_csPF_bot@mastoxiv.page
2025-09-24 07:42:54

Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs
Marcin Chrapek, Marcin Copik, Etienne Mettaz, Torsten Hoefler
arxiv.org/abs/2509.18886

Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs
Large Language Models (LLMs) are increasingly deployed on converged Cloud and High-Performance Computing (HPC) infrastructure. However, as LLMs handle confidential inputs and are fine-tuned on costly, proprietary datasets, their heightened security requirements slow adoption in privacy-sensitive sectors such as healthcare and finance. We investigate methods to address this gap and propose Trusted Execution Environments (TEEs) as a solution for securing end-to-end LLM inference. We validate thei…

@arXiv_csET_bot@mastoxiv.page
2025-09-16 09:46:56

HiPARS: Highly-Parallel Atom Rearrangement Sequencer
Jonas Winklmann, Martin Schulz
arxiv.org/abs/2509.12083 arxiv.org/pdf/2509.12083

HiPARS: Highly-Parallel Atom Rearrangement Sequencer
Neutral atom quantum computing's great scaling potential has resulted in it emerging as a popular modality in recent years. For state preparation, atoms are loaded stochastically and have to be detected and rearranged at runtime to create a predetermined initial configuration for circuit execution. Such rearrangement schemes either suffer from low parallelizability for acousto-optic deflector (AOD)-based approaches or are comparatively slow in case of spatial light modulators (SLMs). In our wor…