The current quantum software stack, while foundational, faces critical scaling challenges that threaten to bottleneck the future of quantum computing. Developed alongside the first generation of online quantum hardware platforms, these software stacks are defined by a common set of quantum software architectures and ideas: Python-based libraries, small loosely-structured programs, shared but restrictive program representations (OpenQASM), online queues, wasteful execution models (e.g., unnecessary repetition, client-server latency), and a computational separation between classical and quantum instructions. A number of these components will not scale, bottlenecking the performance of quantum computing overall. To overcome these limitations and unlock the potential of large-scale quantum computing, a paradigm shift is needed: the development of Quantum Software 2.1.
Anticipating such limitations, a number of organizations have already been looking towards the next generation of ideas. We can expect a more complex and multi-faceted tech stack: deeper, wider, and more complex circuits, first versions of quantum error correction, just-in-time compilation, multi-level IRs, heterogeneous execution models, co-location, and making better use of existing classical software tools.
In this workshop, we highlight a number of software barriers that will have to be overcome to unlock this next stage of development. We will hear from guest speakers, panelists, and attendees who have begun experimenting, prototyping, and releasing early versions of next-generation quantum software technologies. We hope to identify and form consensus about the most promising approaches to pursue in the future that could enable scaling for effective and robust heterogeneous infrastructure development, as well as foster interest in developing these technologies collectively under open models for the benefit of the quantum industry as a whole.
10:00 - 10:05: Introduction
10:05 - 10:25: Opening talk
Steve Habegger, Google Quantum AI
10:25 - 11:10: Lightning talks
Ed Younis, Lawrence Berkeley National Laboratory
Amir Shehata, Oak Ridge National Laboratory
Ian Hincks, IBM
Kai-Hsin Wu, QuEra Computing Inc.
11:10 - 11:30: Panel discussion
13:00 - 13:15: Opening talk
David Ittah, Xanadu
13:15 - 14:10: Lightning talks
Jordan Sullivan, Unitary Foundation
Lukas Burgholzer, Technical University of Munich & MQSC
Justin Lietz, NVIDIA
Purva Thakre, Southern Illinois University in Carbondale
Agustín Borgna, Quantinuum
14:10 - 14:30: Panel discussion
15:00-16:00: Lightning talks
Kristine Rezai, IQM Quantum Computers
Andy Goldschmidt, Postdoc, University of Chicago
Leonardo Disilvestro, Entropica Labs
Sara Sussman, Fermilab
Laleh Beni, Google
16:00 - 16:30: Panel discussion, structured brainstorming, and Q&A
Bio
Steve has been involved with the QuantumAI group since joining Google seven years ago and joined the team full-time almost four years ago. He specializes in web applications and provides technical leadership for the web app team, which produces monitoring, planning and management tools for the rest of Google QuantumAI. Steve holds a Master's degree in physics from the University of Washington.
Abstract
Quantum gates are the fundamental instructions of digital quantum computers. Current programming languages, systems, and software development toolkits identify these operational gates by their titles, which requires a shared understanding of their meanings. However, in the continuously developing software ecosystem surrounding quantum computing—spanning high-level programming systems to low-level control stacks—this identification process is often error-prone, challenging to debug, maintenance-heavy, and resistant to change. In this talk, we introduce unitary expressions, a form of symbolic computation, that aims to shift this burden of identification away from gate labels.
Bio
Ed is an engineer at the Lawrence Berkeley National Laboratory, where he designs and implements quantum compilers. He graduated from the University of California, Berkeley after studying quantum computing and computer science. Currently, he is the lead engineer for the BQSKit project and has developed many of its algorithms. Recently, he has started the OpenQudit project with the goal of making the quantum software ecosystem more robust and extensible.
Abstract
This talk presents a software framework for integrating quantum computing into high-performance computing environments. The framework is hardware-agnostic and supports both current noisy quantum devices and future fault-tolerant systems. It provides APIs for resource management, scheduling, and quantum hardware integration, allowing quantum and classical workloads to run together efficiently. A prototype of this system has been tested with hybrid algorithms like the variational quantum linear solver, demonstrating its ability to support scalable quantum-HPC workflows.
Bio
Amir Shehata is a systems engineer at Oak Ridge National Laboratory, focused on integrating quantum computing with high-performance computing systems. He leads the development of software frameworks for scalable quantum-HPC execution and has over 20 years of experience in networking, MPI, and distributed systems.
Bio
Ian Hincks is a software developer at IBM quantum. He obtained his PhD from the University of Waterloo in 2018 with a focus on quantum control, Hamiltonian characterization, and statistical inference. He spent several years at Quantum Benchmark Inc., subsequently Keysight Technologies, as the lead software developer, researching, implementing, and testing quantum simulators, compilers, and scalable randomized characterization protocols. He currently works on implementing error mitigation and gate characterization tooling and its integration with low-level interfaces to QPUs.
Abstract
The emergence of quantum computing introduces a new class of domain-specific compilation challenges, including analog-digital instruction scheduling, quantum-classical hybrid programming, and architecture-specific constraints such as spatial layout and atom movement in QuEra’s neutral atom platforms. Addressing these demands requires compiler infrastructure that blends quantum-native abstractions with traditional compiler design principles.
Compiling quantum programs—from high-level abstractions down to pulse-level controls—requires deep domain expertise, making direct scientific involvement essential. However, mainstream compiler frameworks such as LLVM and MLIR often pose steep learning curves for scientists, particularly physicists and quantum engineers, who need to contribute directly to compilation pipelines. At the same time, many scientists prefer dynamic languages like Python and Julia, which emphasize interactivity and ease of use. This highlights the need for compiler infrastructures that natively support dynamic language features, facilitate rapid prototyping, and are tailored to the workflows and needs of scientific users.
We present Kirin, a lightweight and extensible compiler infrastructure designed to empower scientists and researchers to build embedded domain-specific languages (eDSLs) and intermediate representations (IRs) tailored to their computational models. Inspired by modern compiler architectures, Kirin supports modular IR construction, transformation and analysis passes, and abstract interpreter inspired from Julia, enabling rapid prototyping of language semantics and backend targets.
Built on Kirin, our open-source package Bloqade offers a set of eDSLs for expressing programs from high-level quantum circuit to atom shuttling representations with control flow. Designed to take advantage of QuEra’s unique transversal gate architecture, Bloqade allows users to naturally express highly parallel quantum operations aligned with the capabilities of QuEra’s neutral atom hardware.
In addition, Bloqade Circuit supports common quantum IRs such as OpenQASM and Stim, facilitating integration with existing toolchains. Together, Kirin and Bloqade enable an end-to-end workflow—from high-level algorithm design to simulation and execution on QuEra’s hardware models—providing a flexible platform for exploring new compilation strategies, abstractions, and quantum algorithm designs optimized for parallel execution.
Bio
Scientific Software Engineer at QuEra Computing, developing compiler pipeline and SDK and integration with quantum hardware. Kai did his PhD at Boston University focusing on theoretical aspects and large scale numerical study of quantum many-body physics, and quantum information.
Speaker: David Ittah, Senior Quantum Software Developer, Xanadu
Talk: jeff: bridging quantum compilers
Bio
David is a senior quantum software developer and Technical Lead of Compilation at Xanadu, working on the Catalyst compilation stack for PennyLane. His interests lie in quantum compilation, quantum intermediate representations, and the intersection of classical compilation infrastructure with quantum programming.
Abstract
The Unitary Compiler Collection (UCC) is a Python library for frontend-agnostic, high performance compilation of quantum circuits. UCC's goal is to gather together the best of open source compilation to make quantum programming simpler, faster, and more scalable. This talk will give a high-level overview of UCC's development, and how being philosophically open-source -- not just incidentally open-source -- drives our success as the most performant OS quantum compiler library.
Bio
Jordan Sullivan has worked in the field of Quantum Computing in cross-functional R&D and Product Management roles, from developer relations at Amazon Braket supporting all major quantum hardware modalities, to theoretical research and software engineering in academia (UC Berkeley) and industry (PsiQuantum - $3.15B valuation in 2021). Jordan currently leads development of the open-source Unitary Compiler Collection (UCC) at the Unitary Foundation.
Abstract
Quantum software frameworks increasingly rely on intermediate representations (IRs) to transform high-level algorithms into hardware-specific instructions. Two LLVM-based frameworks—Quantum Intermediate Representation (QIR) and Multi-Level Intermediate Representation (MLIR)—provide foundations for quantum compilation. In this talk, we present experience from the Munich Quantum Toolkit (MQT) on leveraging both QIR and MLIR as a unified compilation stack and how to work we these frameworks in practice. Drawing on over a year of development, this practical guide aims to equip quantum software engineers with actionable patterns and code templates to adopt LLVM-based IRs effectively, fostering the adoption of these frameworks in the community.
Bio
Lukas Burgholzer works as a research scientist at the Technical University of Munich in the Chair for Design Automation of Prof. Wille and as CTO of the Munich Quantum Software Company. He received his PhD from JKU Linz, Austria, in 2024 working as part if the Institute for Integrated Circuits. His research focuses on design automation tools and software for quantum computing. In these areas, he has published more than 60 papers in international conferences and journals. He is the chief developer of the Munich Quantum Toolkit (MQT) as well as one of the technical leads of the Munich Quantum Software Stack (MQSS) project. For his research, he was awarded the EDAA Outstanding Dissertation Award, the Heinz Zemanek Prize, and more.
Abstract
CUDA-Q is NVIDIA's open-source QPU-agnostic platform for accelerated quantum supercomputing. CUDA-QX is a collection of libraries built on top of CUDA-Q for accelerating quantum research and development in a variety of domains. Here we will focus on the quantum error correction (QEC) library in CUDA-QX, and discuss how we enable QEC researchers with a variety of tools including advanced noisy simulations, pre-built QEC codes, detector error model (DEM) generation, and GPU-accelerated decoders.
Bio
Justin Lietz is a senior quantum computing software architect on the NVIDIA HPC and Quantum Computing team. He is focused on quantum error correction and integrating quantum computers with classical HPC systems. Prior to NVIDIA, Justin was on the research staff at the National Center for Computational Sciences at Oak Ridge National Laboratory. Justin received his Ph.D. in computational nuclear physics in 2019 from Michigan State University.
Abstract
Lattice surgery-based surface code circuits require a non-trivial effort to manually encode complex logical computations. This process is highly fallible for large-scale quantum computations, as the manually designed computation will be compiled into a low-level language that can be understood by simulated or physical devices. In this talk, we present tqec (Topological Quantum Error Correction), a Python-based design automation software for representing, constructing, and compiling large-scale fault-tolerant quantum computations based on surface code and lattice surgery. The tool provides numerous 3D space-time primitives representing verified circuit implementations, which can be helpful in building and compiling large-scale complex computations. Moreover, the talk will also focus on ongoing work for tqec to further compile and simulate a large-scale circuit optimized via PyZX.
Bio
Purva is an ABD PhD Candidate at Southern Illinois University in Carbondale. Her research interests are based on understanding quantum evolutions that cannot be described by existing reversibility formalisms but are influenced by joint system-environment interactions for qubits and qutrits. She is also working towards integrating QEM with QEC.
She has been active in the quantum open-source community for more than four years. She was also recognized as a Quantum Open Source Fellow by the Unitary Foundation due to her numerous contributions to projects like Mitiq and toqito. She started actively contributing to TQEC in late 2024 as the project’s goals aligned with her interests in quantum software and quantum compilation.
Abstract
In recent years, the field of quantum computing has advanced rapidly, supported by the growing availability of open-source software tools that empower both researchers and developers. Ensuring transparency in quantum circuit execution is essential for quantum engineers to fully understand the transition from high-level circuit representations to low-level pulse instructions. In this talk, we introduce Pulla, an open-source software package developed by IQM Quantum Computers, designed to provide users with detailed access to the pulse-level implementation of quantum circuits. We will explore the benefits of exposing these low-level controls to end users, demonstrate how pulse-level programming can drive research in various domains, and highlight how such access can foster new avenues of innovation and discovery in quantum computing.
Bio
Kristine Rezai is the Technical Pre-Sales Engineer for North America at IQM Quantum Computers. In her role, she helps people learn about quantum computing at IQM by working individually with clients to assist in integrating quantum computing into their solutions, giving product demos and other presentations, and supporting existing users. She has a bachelor’s degree in Engineering Physics from UC Berkeley and a PhD in Physics from Harvard. Her educational background focuses on experimental quantum science, studying quantum sensing and simulation topics with nitrogen-vacancy center impurities in diamond for her graduate work. After graduate school, Kristine worked in venture capital, helping build early-stage companies with the goal of solving important problems at the intersection of engineering and research, before joining IQM Quantum Computers.
Abstract
Piccolo.jl provides access to modern control methods at the very forefront of technology in an accessible, extensible open source ecosystem (OSE). We discuss the unique advantages offered by the Piccolo.jl OSE, from minimum time control to optimization of bosonic systems. We highlight how design choices have enabled a wide range of scientific applications. We also survey some unique extensions to the trajectory optimization framework being developed in the OSE: (1) trajectory bundles, a massively parallel, derivative free approach to optimal control, and (2) quantum iterative learning control, an integrated approach for model-based calibration of optimal controls.
Bio
Andy J. Goldschmidt is an IC Postdoctoral Research Fellow studying novel control and readout schemes for gate-based quantum computing at UChicago, working with Fred Chong. His open source quantum software contributions include co-development of Piccolo.jl, a Julia ecosystem for fine tuned quantum control and calibration. Andy completed his Ph.D. in Physics in 2022 at the University of Washington in Seattle, focused on physics-informed machine learning and control of dynamical systems.
Bio
Leonardo has 10+ years of experience in quantum computing and holds a mathematical physics degree from the University of Edinburgh and a Ph.D. in quantum information theory from Telecom ParisTech.
As Head of Integrations at Entropica Leonardo works at the interface between Entropica’s software stack and partners, with the goal of bringing fault-tolerant quantum computations closer. One partnership at a time!
On a more personal note, Leo is pretty pleased with the way things have turned so far: after a stint in the accademia he has decided to get himself a real job, and now he enjoys working at Entropica Labs while living between Italy and Singapore. In his free time, he likes to cook, walk his dog, and train for the next marathon.
Bio
Sara Sussman is a Lederman Fellow at Fermilab and a visiting scholar at Northwestern University. She completed her PhD in 2023 at Princeton University on superconducting qubit design, fabrication and control. Sara enjoys contributing to open source qubit hardware projects.
Abstract
Tesseract is a Most-Likely Error decoder designed for low-density-parity-check quantum error-correcting codes. Tesseract conducts a search through a graph on the set of all subsets of errors to find the lowest cost subset of errors consistent with the input syndrome. Although this graph is exponentially large, the search can be made efficient in practice for random errors using A* search technique along with a few pruning heuristics. We show through benchmark circuits for surface, color, and bivariate-bicycle codes that Tesseract is significantly faster than integer programming-based decoders while retaining comparable accuracy at moderate physical error rates. We also find that Tesseract can decode transversal CNOT protocols for surface codes on neutral atom quantum computers. Finally, we compare surface code and bivariate bicycle code circuits, finding that the [[144,12,12]] bivariate bicycle code is 14× to 19× more efficient than surface codes using our most-likely error decoding, whereas using correlated matching and BP+OSD decoders would have implied only a 10× improvement. Assuming instead that long-range couplers are 10× noisier, the improvement drops to around 4× using Tesseract or 2× using correlated matching and BP+OSD.
Bio
Laleh is a software engineer on the Quantum Error Correction (QEC) team at Google Quantum AI, where she works on real-time decoding and QEC compilers. She earned her Ph.D. in high-performance computing and compilers from UCI, and her work focuses on building scalable, low-latency systems for fault-tolerant quantum computing.
Xanadu
Quantinuum
Unitary Foundation
Oak Ridge National Lab
Xanadu