8 citations · 20 across the 28 of their papers we have counts for
29 papers
Engineering Low-Latency, High-Throughput Quantum Decoders on NVIDIA GPUs
Stefano Markidis, Luca Pennati, Ivy Peng
We design and optimize a quantum error correction (QEC) decoder for NVIDIA GPUs, combining Relay belief propagation, a state-of-the-art message-passing algorithm, with order-zero o…
Fewer Histories, Faster Paths: Distributed Quantum Circuit Feynman Simulation via History Reduction, Checkpointing, and Pruning
Frej Larssen, Luca Pennati, Erik M. Åsgrim +2
We present a distributed method for exact sparse-output quantum circuit simulation based on the pure Feynman sum-over-histories formulation. The method computes selected computatio…
Advancing Heliophysics and Space Weather Modeling through Open Science
C. Corti, M. M. Kuznetsova, M. A. Reiss +85
We present a community-wide effort to develop a strategy and action plan to advance heliophysics and space weather modeling through open science. While open science has the potenti…
Which Superconducting Qubit Model is Good Enough? From Effective Two-Level to Circuit-Based Hamiltonians for Pulse-Level Simulation
Frej Larssen, Ivy Peng, Stefano Markidis
Pulse-level simulators are the lowest-level, most widely used abstraction layer for studying how quantum hardware responds to control signals, but they can be built on Hamiltonian…
Robust Matrix-Free Newton-Krylov Solvers via Automatic Differentiation
Marco Pasquale, Stefano Markidis
Jacobian-Free Newton-Krylov (JFNK) methods avoid forming the full Jacobian, but still require Jacobian-vector products, i.e., Gateaux derivatives of the nonlinear residual along Kr…
Post-Moore Technologies for Plasma Simulation: A Community Roadmap
Luca Pennati, Erik M. Åsgrim, Jeremy J. Williams +21
Plasma simulations are among the most computationally demanding scientific workloads, combining high-dimensional kinetic evolution, particle-mesh coupling, field solves, and data-i…
Mass Matrix Assembly on Tensor Cores for Implicit Particle-In-Cell Methods
Luca Pennati, Stefano Markidis
Matrix-multiply-accumulate (MMA) units, or tensor cores, are now widespread across modern computing architectures. Yet, their use for particle-grid operators remains limited. In im…
BVH-Accelerated Ray Tracing for High-Frequency Electromagnetic Backscattering
Marco Pasquale, Andong Hu, Luca Pennati +2
As computational complexity in electromagnetics increases with frequency, full-wave solvers become computationally infeasible for electrically large problems. To address this limit…
Making Room for AI: Multi-GPU Molecular Dynamics with Deep Potentials in GROMACS
Luca Pennati, Andong Hu, Ivy Peng +2
GROMACS is a de-facto standard for classical Molecular Dynamics (MD). The rise of AI-driven interatomic potentials that pursue near-quantum accuracy at MD throughput now poses a si…
Multi-GPU Hybrid Particle-in-Cell Monte Carlo Simulations for Exascale Computing Systems
Jeremy J. Williams, Jordy Trilaksono, Stefan Costea +13
Particle-in-Cell (PIC) Monte Carlo (MC) simulations are central to plasma physics but face increasing challenges on heterogeneous HPC systems due to excessive data movement, synchr…
Unsupervised Physics-Informed Operator Learning through Multi-Stage Curriculum Training
Paolo Marcandelli, Natansh Mathur, Stefano Markidis +2
Solving partial differential equations remains a central challenge in scientific machine learning. Neural operators offer a promising route by learning mappings between function sp…
Enabling AI Deep Potentials for Ab Initio-quality Molecular Dynamics Simulations in GROMACS
Andong Hu, Luca Pennati, Stefano Markidis +1
State-of-the-art AI deep potentials provide ab initio-quality results, but at a fraction of the computational cost of first-principles quantum mechanical calculations, such as dens…
Tensor Network Compression for Fully Spectral Vlasov-Poisson Simulation
Erik M. Åsgrim, Luca Pennati, Marco Pasquale +1
We propose a numerical method for kinetic plasma simulation in which the phase-space distribution is represented by a low-rank tensor network with an adaptive level of compression.…
QPU Micro-Kernels for Stencil Computation
Stefano Markidis, Luca Pennati, Marco Pasquale +2
We introduce QPU micro-kernels: shallow quantum circuits that perform a stencil node update and return a Monte Carlo estimate from repeated measurements. We show how to use them to…
An HPC-Inspired Blueprint for a Technology-Agnostic Quantum Middle Layer
Stefano Markidis, Gilbert Netzer, Luca Pennati +1
We present a blueprint for a quantum middle layer that supports applications across various quantum technologies. Inspired by concepts and abstractions from HPC libraries and middl…
Exascale Implicit Kinetic Plasma Simulations on El~Capitan for Solving the Micro-Macro Coupling in Magnetospheric Physics
Stefano Markidis, Andong Hu, Ivy Peng +9
Our fully kinetic, implicit Particle-in-Cell (PIC) simulations of global magnetospheres on up to 32,768 of El Capitan's AMD Instinct MI300A Accelerated Processing Units (APUs) repr…
Partitioned Hybrid Quantum Fourier Neural Operators for Scientific Quantum Machine Learning
Paolo Marcandelli, Yuanchun He, Stefano Mariani +2
We introduce the Partitioned Hybrid Quantum Fourier Neural Operator (PHQFNO), a generalization of the Quantum Fourier Neural Operator (QFNO) for scientific machine learning. PHQFNO…
A Hybrid Quantum-Classical Particle-in-Cell Method for Plasma Simulations
Pratibha Raghupati Hegde, Paolo Marcandelli, Yuanchun He +4
We present a hybrid quantum-classical electrostatic Particle-in-Cell (PIC) method, where the electrostatic field Poisson solver is implemented on a quantum computer simulator using…
Discovering Governing Equations of Geomagnetic Storm Dynamics with Symbolic Regression
Stefano Markidis, Jonah Ekelund, Luca Pennati +2
Geomagnetic storms are large-scale disturbances of the Earth's magnetosphere driven by solar wind interactions, posing significant risks to space-based and ground-based infrastruct…
Physics-Aware Compression of Plasma Distribution Functions with GPU-Accelerated Gaussian Mixture Models
Andong Hu, Luca Pennati, Ivy Peng +1
Data compression is a critical technology for large-scale plasma simulations. Storing complete particle information requires Terabyte-scale data storage, and analysis requires ad-h…
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions
Jonah Ekelund, Savvas Raptis, Vicki Toy-Edens +4
Analyzing multi-featured time series data is critical for space missions making efficient event detection, potentially onboard, essential for automatic analysis. However, limited o…
Apple vs. Oranges: Evaluating the Apple Silicon M-Series SoCs for HPC Performance and Efficiency
Paul Hübner, Andong Hu, Ivy Peng +1
This paper investigates the architectural features and performance potential of the Apple Silicon M-Series SoCs (M1, M2, M3, and M4) for HPC. We provide a detailed review of the CP…
Harnessing CUDA-Q's MPS for Tensor Network Simulations of Large-Scale Quantum Circuits
Gabin Schieffer, Stefano Markidis, Ivy Peng
Quantum computer simulators are an indispensable tool for prototyping quantum algorithms and verifying the functioning of existing quantum computer hardware. The current largest qu…
Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs
Jonah Ekelund, Stefano Markidis, Ivy Peng
Graphics Processing Units (GPUs) have become the standard in accelerating scientific applications on heterogeneous systems. However, as GPUs are getting faster, one potential perfo…