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  • Perspective
  • Open Access

Grand Challenge of Quantum Applications

Ryan Babbush*, Robbie King†, Sergio Boixo, William Huggins, Tanuj Khattar, Guang Hao Low, Jarrod R. McClean, Thomas O’Brien, and Nicholas C. Rubin

  • *Contact author: babbush@google.com
  • †Contact author: robbieking@google.com
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PRX Quantum 7, 020101 – Published 15 June, 2026

DOI: https://doi.org/10.1103/6r9l-lynr

Abstract

This perspective outlines promising pathways and critical obstacles on the road to developing useful quantum computing applications, drawing on insights from the Google Quantum AI team. We propose a five-stage framework for this process, spanning from theoretical explorations of quantum advantage to the practicalities of compilation and resource estimation. For each stage, we discuss key trends, milestones, and inherent scientific and sociological impediments. We argue that two central stages—identifying concrete problem instances expected to exhibit quantum advantage, and connecting such problems to real-world use cases—represent essential and currently under-resourced challenges. Throughout, we touch upon related topics, including the promise of generative artificial intelligence for aspects of this research, criteria for compelling demonstrations of quantum advantage, and the future of compilation as we enter the era of early fault-tolerant quantum computing.

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Popular Summary

It is a special moment in the development of quantum computing. Several platforms are operating below the fault-tolerance threshold, and many experts now believe we can scale to hundreds of logical qubits and beyond. But the big question is not “Can we build them?” It is “What will we do with them that’s unmistakably valuable?”

This piece lays out a practical roadmap for realizing the potential of quantum computing in applications. It breaks the journey into five stages and argues that two in particular are both essential and under-explored: finding the right problem instances, and connecting them to real-world needs. Along the way, we discuss the importance of verifiability, algorithm architecture codesign, and how generative AI can help us to find quantum computing applications.

With hardware momentum and error-correction maturing, the field is now poised for algorithmic breakthroughs, and the leverage of quantum algorithms research has never been higher. This article offers a perspective to help researchers and stakeholders understand the landscape of quantum applications, and how to best contribute.

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PRX Quantum 7, 020101– Published 15 June, 2026
  • Received 12 November 2025
  • Revised 13 March 2026
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