Quantum Resource Estimation Workshop

IEEE Quantum Week 2024 conference

Palais des Congrès, Montreal

1001 Pl. Jean-Paul-Riopelle, Montréal, QC H2Z 1H5, Canada

Room: 522AB

Date: Thursday 19th September

Duration: 4.5 hours (3 x 1.5 hours)

About

Quantum Resource Estimation (QRE)

Quantum Resource Estimation (QRE) is an essential aspect of quantum information processing and quantum technologies. It refers to the process of quantifying the resources (time, qubits, magic states, etc.) required for performing a given quantum computation or task. It is essential to have an accurate understanding of resource requirements to analyze the tradeoffs between the benefit/utility of performing quantum computations versus their cost. Costs can have wide-ranging impacts on investment decisions made by corporations, academia, research institutes, and governments. Building useful QRE tools and performing high-quality QRE research relies on strong connections among researchers with expertise in various subdomains of quantum computing. For example, algorithm developers benefit from realistic hardware assumptions rather than idealized models to direct their optimization; and hardware architecture design can be directed by requirements from algorithms analyzed to be most promising.

Why You Should Attend This Workshop

After the success of the first edition of this workshop last year, this year we want to recreate a forum for sharing research and experience related to QRE issues, tools, and techniques with a stronger focus on applications of QRE. On top of the talks and panels discussion during the workshop, we also plan to organize a QRE-focused research challenge for students and young professionals prior to the conference and present the winning projects during the workshop.

Schedule

Sessions:

10:00 - 11:30 Session 1: Tools and Methods

13:00 - 14:30 Session 2: Applications and Algorithms

15:00 - 16:30 Session 3: Panel & Challenge

Session 1: Tools and Methods

Chair: Matt Harrigan, Google

Program:

Mariia Mykhailova: "Modeling Error Correction in the Azure Quantum Resource Estimator"
Arianne Meijer - van de Griend: "Pandora: Ultra-Large-Scale Quantum Circuit Compilation and Optimization"
Murphy Niu: "A SAT Scalpel for Lattice Surgery: Representation and Synthesis of Subroutines for Fault-Tolerant Quantum Computing"

Session 2: Applications and Algorithms

Chair: Peter Johnson, Zapata AI

Program:

Sophia Simon: "Superpolynomial improvement in precision for quantum simulations of coupled quantum-classical dynamics"
Fionn Malone: "Resource estimation for the quantum computation of stopping power"
Amara Katabarwa: "Feasibility of accelerating incompressible computational fluid dynamics simulations with fault-tolerant quantum computers"

Session 3: Panel & Challenge

Chair: Kevin Obenland, MIT LL

Panel: Athena Caesura (PsiQuantum), Antonio Corcoles (IBM), Neil Gillespie (Riverlane)
QRE Challenge winners:
- Petra Brčić: "Comparison of resources required for monolithic and distributed versions of quantum phase estimation"
- Walden Killick: "Quantum resource estimation for matrix inversion by QSVT"

Speakers

Modeling Error Correction in the Azure Quantum Resource Estimator
Mariia Mykhailova, Microsoft

Abstract: Azure Quantum Resource Estimator is an open-source tool, shipped as part of Azure Quantum Development Kit, that allows you to estimate the resources needed to run a quantum program on a fault-tolerant quantum computer, taking into account the various sources of overhead: planar mapping, error correction, and magic state distillation. This talk gives an overview of Azure Quantum Resource Estimator, the default architecture of fault-tolerant quantum computers it uses to perform the estimates, and the extensibility API that allows to customize the assumed architecture. The extensibility API is illustrated using the example of customizing this abstraction to model cat qubit architecture with a biased repetition QEC code and Toffoli magic state factories.

Mariia Mykhailova is a Principal Quantum Software Engineer at Microsoft Quantum. Her main interests are software for fault-tolerant quantum computation and quantum education and outreach. She is the author of the books “Q# Pocket Guide” (2022, O'Reilly) and "Quantum Programming in Depth" (2024, Manning). She holds MS in Applied Math from Kyiv Polytechnic Institute, Ukraine.

Pandora: Ultra-Large-Scale Quantum Circuit Compilation and Optimization
Arianne Meijer - van de Griend, University of Helsinki & IQM

Abstract: There is an enormous gap between what circuit sizes are feasible with the current generation of quantum circuit design automation tools and the sizes required in terms of compilation, optimisation and resource estimation of practical circuits. The latter, such as the ones for quantum chemistry or Shor's algorithm, include many orders of magnitude more gates than what the current tools can handle. We bridge the gap by exploiting the performance and scalability of relational database management systems. We architect, implement and benchmark a tool for compiling and optimising quantum circuits through template rewrite rules. The tool can easily handle quantum circuits with tens of millions of gates, and can operate in a multi-threaded manner offering almost linear speed-ups. We are benchmarking it against TKET, and determine a performance advantage for circuits of more than 10 000 gates. Our tool can apply thousands of complex circuit rewrites per second at random circuit locations. Our tool is integrated with Google Qualtran and the source code is open at https://github.com/ioanamoflic/pandora.

Bio: Arianne is a postdoc at the University of Helsinki and a Quantum Software Engineer at IQM. She is originally from the Netherlands where she studied computer science and artificial intelligence. Via the ZX Calculus, she found her way into quantum computing. She worked on TKET at Cambridge Quantum Computing (now Quantinuum) before she started her PhD at the University of Helsinki. She successfully defended her thesis entitled “Advances in Quantum Compilation in the NISQ Era” and graduated with distinction earlier this year.

A SAT Scalpel for Lattice Surgery: Representation and Synthesis of Subroutines for Fault-Tolerant Quantum Computing

Murphy Yuezhen Niu, University of California, Santa Barbara

Abstract: Quantum error correction is necessary for large-scale quantum computing. A promising quantum error correcting code is the surface code. For this code, fault-tolerant quantum computing (FTQC) can be performed via lattice surgery, i.e., splitting and merging patches of code. Given the frequent use of certain lattice-surgery subroutines (LaS), it becomes crucial to optimize their design in order to minimize the overall spacetime volume of FTQC. In this study, we define the variables to represent LaS and the constraints on these variables. Leveraging this formulation, we develop a synthesizer for LaS, LaSsynth, that encodes a LaS construction problem into a SAT instance, subsequently querying SAT solvers for a solution. Starting from a baseline design, we can gradually invoke the solver with shrinking spacetime volume to derive more compact designs. Due to our foundational formulation and the use of SAT solvers, LaSsynth can exhaustively explore the design space, yielding optimal designs in volume. For example, it achieves 8% and 18% volume reduction respectively over two states-of-the-art human designs for the 15-to-1 T-factory, a bottleneck in FTQC.

Murphy Yuezhen Niu is an Assistant Professor and Stansbury Chair in Computer Science at the University of California, Santa Barbara since 2024. Previously, she was a research scientist in the Google Quantum AI team. Her work focused on intelligent quantum control optimization and metrology, quantum machine learning, quantum algorithm design and near-term quantum error correction.

Superpolynomial improvement in precision for quantum simulations of coupled quantum-classical dynamics
Sophia Simon, University of Toronto

Abstract: In this talk, we discuss a novel quantum algorithm for simulating coupled quantum-classical dynamics, such as molecular dynamics within the Born-Oppenheimer approximation, on a fault-tolerant quantum computer. Our approach is based on the Koopman-von Neumann formulation of classical mechanics and provides a superpolynomial improvement in the precision scaling compared to previous work.

Sophia Simon is a PhD student in Nathan Wiebe's group at the University of Toronto. Her research focuses on fault-tolerant quantum algorithms for simulating many-body systems.

Resource estimation for the quantum computation of stopping power
Fionn Malone, Google

Abstract: Quantum chemistry is often highlighted as one of the most natural application areas for fault tolerant quantum computers. Most applications to date have focused on computing ground states of strongly correlated molecules. In this talk I will present our recent work on computing resource estimates for applications in dynamics. I will focus on computing the stopping power in the warm dense matter regime which is relevant for inertial confinement fusion reactor design. Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it and is challenging to compute accurately classically. We will present leading order Toffoli costs for problem sizes of interest and also outline how Qualtran can be used to provide further insight into the underlying quantum algorithms.

Fionn Malone is a research scientist on the quantum ai team whose research focuses on fault tolerant resource estimation for quantum chemistry.

Feasibility of accelerating incompressible computational fluid dynamics simulations with fault-tolerant quantum computers
Amara Katabarwa, Zapata AI

Abstract: Across industries, traditional design and engineering workflows are being upgraded to simulation driven processes. Many workflows include computational fluid dynamics (CFD). Simulations of turbulent flow are notorious for high compute costs and reliance on approximate methods that compromise accuracy. Improvements in the speed and accuracy of CFD calculations would potentially reduce design workflow costs by reducing computational costs and eliminating the need for experimental testing. This study explores the feasibility of using fault-tolerant quantum computers to improve the speed and accuracy of CFD simulations in the incompressible or weakly compressible regime. For the example of simulation-driven ship design, we consider simulations for calculating the drag force in steady-state flows and provide analysis on economic utility and classical hardness. As a waypoint toward assessing the feasibility of our chosen quantum approach, we estimate the quantum resources required for the simpler case of drag force on a sphere. We estimate the product of logical qubits × T gates to range from 10^20 to 10^28.

Amara Katabarwa is a research scientist at Zapata AI, he received his undergraduate degree in physics from Emory University in 2011 and his Ph.D. in, physics from University of Georgia in 2019.

His goal is to understand how quantum computers work and where their applications might be. He has done work in try to understand this goal for both near term quantum computers currently on the market and is currently pursuing this goal for more long-term fault tolerant quantum devices. This has led him to study areas as diverse as Quantum Error Mitigation, Quantum Error Correction, Quantum Compiling and Quantum Algorithms.