103 citations · 361 across the 39 of their papers we have counts for
7 papers · 1 filter
PureMagic: A Dynamic Scheduler for Lattice Surgery
Steven Hofmeyr, Mathias Weiden, Justin Kalloor +2
Fault-tolerant quantum computation on surface codes requires magic states for universal computation. Traditional distillation factories deliver magic states deterministically but c…
Application Scale Quantum Circuit Compilation with Controlled Error
Justin Kalloor, Lucas Kovalsky, Mathias Weiden +4
Compilation and optimization of quantum circuits are critical components in the execution of algorithms on quantum computers. These components must successfully balance two competi…
Optimizing PyTorch Inference with LLM-Based Multi-Agent Systems
Kirill Nagaitsev, Luka Grbcic, Samuel Williams +1
Maximizing performance on available GPU hardware is an ongoing challenge for modern AI inference systems. Traditional approaches include writing custom GPU kernels and using specia…
Intermediate-temperature topological Uhlmann phase on IBM quantum computers
Christopher Mastandrea, Costin Iancu, Hao Guo +1
A spin-1 system can exhibit an intermediate-temperature topological regime with a quantized Uhlmann phase sandwiched by topologically trivial low- and high-temperature regimes. We…
QTurbo: A Robust and Efficient Compiler for Analog Quantum Simulation
Junyu Zhou, Yuhao Liu, Shize Che +5
Analog quantum simulation leverages native hardware dynamics to emulate complex quantum systems with great efficiency by bypassing the quantum circuit abstraction. However, convent…
Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions
Keita Teranishi, Harshitha Menon, William F. Godoy +25
We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular…
Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis
Mathias Weiden, Justin Kalloor, John Kubiatowicz +1
Machine Learning with deep neural networks has transformed computational approaches to scientific and engineering problems. Central to many of these advancements are precisely tune…