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The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute

Gilles Buchs, Thomas Beck, Ryan Bennink, Daniel Claudino, Andrea Delgado, Nur Aiman Fadel, Peter Groszkowski, Kathleen Hamilton, Travis Humble, Neeraj Kumar, Ang Li, Phillip Lotshaw, Olli Mukkula, Ryousei Takano, Amit Saxena, In-Saeng Suh, Miwako Tsuji, Roel Van Beeumen, Ugo Varetto, Yan Wang, Kazuya Yamazaki, Mikael P. Johansson

TL;DR

The paper assesses how quantum computing can advance scientific HPC by framing QC as a hybrid accelerator within co-designed workflows. It surveys hardware paradigms (NISQ vs FTQC), integration models (loose vs tight), software ecosystems, and AI-enhanced QC, and it synthesizes ADAC member insights to outline practical use cases across CMP, quantum chemistry, HEP, cryptography, and optimization. Emulation and benchmarking are emphasized as critical for validating quantum advantage and guiding cross-technology integration. The work highlights challenges in standardization, real-time QEC, energy accounting, and workforce development, while underscoring the strategic value of global collaboration to realize quantum-accelerated HPC for large-scale scientific problems.

Abstract

Quantum computing (QC) has gained significant attention over the past two decades due to its potential for speeding up classically demanding tasks. This transition from an academic focus to a thriving commercial sector is reflected in substantial global investments. While advancements in qubit counts and functionalities continues at a rapid pace, current quantum systems still lack the scalability for practical applications, facing challenges such as too high error rates and limited coherence times. This perspective paper examines the relationship between QC and high-performance computing (HPC), highlighting their complementary roles in enhancing computational efficiency. It is widely acknowledged that even fully error-corrected QCs will not be suited for all computational task. Rather, future compute infrastructures are anticipated to employ quantum acceleration within hybrid systems that integrate HPC and QC. While QCs can enhance classical computing, traditional HPC remains essential for maximizing quantum acceleration. This integration is a priority for supercomputing centers and companies, sparking innovation to address the challenges of merging these technologies. The Accelerated Data Analytics and Computing Institute (ADAC) is comprised of globally leading HPC centers. ADAC has established a Quantum Computing Working Group to promote and catalyze collaboration among its members. This paper synthesizes insights from the QC Working Group, supplemented by findings from a member survey detailing ongoing projects and strategic directions. By outlining the current landscape and challenges of QC integration into HPC ecosystems, this work aims to provide HPC specialists with a deeper understanding of QC and its future implications for computationally intensive endeavors.

The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute

TL;DR

The paper assesses how quantum computing can advance scientific HPC by framing QC as a hybrid accelerator within co-designed workflows. It surveys hardware paradigms (NISQ vs FTQC), integration models (loose vs tight), software ecosystems, and AI-enhanced QC, and it synthesizes ADAC member insights to outline practical use cases across CMP, quantum chemistry, HEP, cryptography, and optimization. Emulation and benchmarking are emphasized as critical for validating quantum advantage and guiding cross-technology integration. The work highlights challenges in standardization, real-time QEC, energy accounting, and workforce development, while underscoring the strategic value of global collaboration to realize quantum-accelerated HPC for large-scale scientific problems.

Abstract

Quantum computing (QC) has gained significant attention over the past two decades due to its potential for speeding up classically demanding tasks. This transition from an academic focus to a thriving commercial sector is reflected in substantial global investments. While advancements in qubit counts and functionalities continues at a rapid pace, current quantum systems still lack the scalability for practical applications, facing challenges such as too high error rates and limited coherence times. This perspective paper examines the relationship between QC and high-performance computing (HPC), highlighting their complementary roles in enhancing computational efficiency. It is widely acknowledged that even fully error-corrected QCs will not be suited for all computational task. Rather, future compute infrastructures are anticipated to employ quantum acceleration within hybrid systems that integrate HPC and QC. While QCs can enhance classical computing, traditional HPC remains essential for maximizing quantum acceleration. This integration is a priority for supercomputing centers and companies, sparking innovation to address the challenges of merging these technologies. The Accelerated Data Analytics and Computing Institute (ADAC) is comprised of globally leading HPC centers. ADAC has established a Quantum Computing Working Group to promote and catalyze collaboration among its members. This paper synthesizes insights from the QC Working Group, supplemented by findings from a member survey detailing ongoing projects and strategic directions. By outlining the current landscape and challenges of QC integration into HPC ecosystems, this work aims to provide HPC specialists with a deeper understanding of QC and its future implications for computationally intensive endeavors.

Paper Structure

This paper contains 58 sections, 2 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Context and Motivation
  3. Objectives
  4. Structure of the article
  5. Quantum Computing
  6. Quantum Computing Basics
  7. Noisy Intermediate-Scale Quantum (NISQ) Devices
  8. Fault Tolerant Quantum Computing (FTQC)
  9. Qubit Platforms
  10. Integration of Quantum Computing, HPC, and AI
  11. Hardware Integration
  12. Loose Integration Model
  13. Tight Integration Model
  14. Energy Efficiency
  15. Software Integration
  16. ...and 43 more sections

Figures (3)

  • Figure 1: Generic execution flow of a hybrid classical HPC+QC task.
  • Figure 2: Schematic representation of the scaling characteristics of classical and quantum algorithms with problem size (axes not to scale for illustrative clarity), highlighting the potential of quantum computers and algorithms to show advantages over their classical counterparts in computing speed as problem size exceeds a certain threshold.
  • Figure 3: Architectures of tensor network methods. Tensors are represented as circles or triangles with multiple legs. MPS is optimal for one-dimensional systems with local interaction. PEPS is suitable for multiple dimensions. The tree-like structure of TTN makes it suitable for systems with hierarchical entanglements. MERA contains "disentanglers" (pink circles) reducing short-range entanglement, and is suitable for systems with multiple length scales.