qStack: International Workshop on Quantum Systems

Abstract

Recent contributions are presented toward realizing fault-tolerant quantum computing at scale, including AI methods. Advances include more flexible choice of error correction codes, parallel methods of error decoding based on syndrome flips and GNN inference, cost modeling of logical transversals for distributed large-scale architectures, and novel simulation support for high-level operations with circuit-level error modeling.

Bio

Frank Mueller is a Professor in Computer Science and a member of multiple research centers at North Carolina State University. Previously, he held positions at Lawrence Livermore National Laboratory and Humboldt University Berlin, Germany. He received his Ph.D. from Florida State University in 1994. He has published papers in the areas of quantum computing, parallel and distributed systems, embedded and real-time systems, and compilers. He is a member of ACM SIGPLAN, ACM SIGBED and an ACM Fellow as well as an IEEE Fellow. He is a recipient of an NSF Career Award, an IBM Faculty Award, a Google Research Award and two Fellowships from the Humboldt Foundation.

Abstract

Lattice surgery is one of the mainstream methods for executing logical gates between two error-corrected qubits. It is a general technique that has been demonstrated across many promising technologies, including surface codes, quantum low-density parity-check (QLDPC) codes, trapped ions, superconducting qubits, and neutral atoms. Existing lattice surgery schedulers take a static approach to minimizing program space-time volume: they statically allocate routing ancillae and assume unconstrained availability of high-fidelity T states, among other simplifications. Realistic fault-tolerant quantum computers, however, will be resource-constrained — deployed schedulers will need to accommodate cheaper, stochastic magic state cultivation, allocate a minimal number of distillation factories, and so on. These real-world constraints require replacing static approaches with dynamic scheduling subject to real-time space and fidelity constraints. In this talk, I will present several online, dynamic lattice surgery schedulers and discuss future research directions, along with the implications of dynamic scheduling for the implementation of the classical control stack.

Bio

Costin Iancu is a senior scientist at Lawrence Berkeley National Laboratory. He has extensive experience in systems for large-scale computing and, more recently, launched the Berkeley Quantum Synthesis Toolkit (BQSKit) project. BQSKit is a full-stack compiler for fault-tolerant quantum computing, encompassing Clifford+T synthesis, Pauli-based computation compilation, and lattice surgery scheduling. The software package is widely used in both academia and industry, with more than 800,000 downloads, and its underlying technologies have received three Best Paper Awards at IEEE QCE.

Abstract

Quantum computing is at an inflection point with the emergence of larger systems, demonstrations of early fault-tolerance, growing interests in exploring the feasibility of quantum advantage for critical applications. The quantum software remains a critical bridge that will transform algorithmic promise into practical runtime benefits. In this talk, I will discuss advanced compilation and job scheduling techniques to enhance the capabilities of the quantum software stack.

Bio

Poulami Das is an Assistant Professor and a Fellow of the Advanced Micro Devices (AMD) Chair in Computer Engineering in Electrical and Computer Engineering at The University of Texas at Austin. Her research focuses on software and architecture for improving the reliability of quantum computers. She is also interested in designing systems for efficient inference.

Abstract

Neutral atom quantum computers are rapidly emerging as a leading architecture for scalable quantum computing. Their unique capabilities and challenges, including movable qubits, reconfigurable atom arrays, and atom loss, fundamentally reshape the role of systems software. Unlike fixed-topology quantum processors, neutral atom platforms require software that jointly manages atom placement, movement, compilation, scheduling, and hardware-aware optimization.

This talk presents a systems perspective on building a software stack for neutral atom quantum computers. I will discuss compiler and runtime techniques that coordinate atom movement and execution, architecture models that enable hardware-software co-design, and system-level optimizations that improve scalability and reliability. I will conclude by outlining the challenges that lie ahead as neutral atom systems transition toward large-scale, fault-tolerant quantum computing, and why advances in systems software will be central to realizing their full potential.

Bio

Tirthak Patel is an Assistant Professor of Computer Science at Rice University. His research lies at the intersection of quantum computing and high performance systems, with a particular focus on building the systems software stack for neutral atom quantum computers. His work spans compiler design, runtime systems, architecture modeling, and hardware-software co-design to improve the scalability, reliability, and programmability of emerging quantum hardware. He is a recipient of the IEEE Computer Society TCHPC Early Career Researchers Award for Excellence in High Performance Computing. His research has appeared in leading venues, including SC, ICS, SIGMETRICS, ICCAD, and DAC, and has also been recognized with SC and ICCAD Best Paper nominations, the ACM-IEEE George Michael Memorial HPC Fellowship, and honorable mentions for the ACM SIGHPC and IEEE SPEC doctoral dissertation awards.

Abstract

How fast can a fault-tolerant quantum computer run? The answer depends not only on quantum hardware, but also on the classical processing that turns noisy measurements into error-correction decisions. At the edge of the quantum processor, machine-learning pipelines can improve readout accuracy and shorten measurement time. Yet their inference and data-movement costs can consume these gains or introduce new bottlenecks. Faster readout therefore requires more than a better classifier: it requires a holistic view of the measurement-to-decoding pipeline.

In this talk, we will present our work on readout for superconducting and neutral-atom quantum computers. For superconducting qubits, we combine signal processing with compact neural networks to deliver accurate, low-latency readout with a small hardware footprint. For neutral atoms, we combine learned image reconstruction with confidence-guided speculation, recovering reliable measurement outcomes from shorter fluorescence exposures while selectively invoking expensive denoising. Although these technologies produce different measurement data and operate on different timescales, they expose a common architectural challenge: balancing measurement quality, acquisition time, and classical processing cost.

Bio

Swamit Tannu is an Assistant Professor of Computer Sciences at the University of Wisconsin–Madison, where he leads the QUEST Research Group. His research asks how to turn fragile quantum devices into computers that can run useful programs reliably, spanning quantum systems architecture, compilers, error correction, and full-stack design — including scalable classical control for quantum processors, efficient fault-tolerant architectures, and quantum runtimes. He is a recipient of the NSF CAREER Award and a Best Paper Award at ACM Computing Frontiers, and has been inducted into the MICRO Hall of Fame. More at swamittannu.com.