Assessing requirements to scale to practical quantum advantage
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Abstract (arXiv)
While quantum computers promise to solve some scientifically and commercially valuable problems thought intractable for classical machines, delivering on this promise will require a large-scale quantum machine. Understanding the impact of architecture design choices for a scaled quantum stack for specific applications, prior to full realization of the quantum system, is an important open challenge. To this end, we develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required across these layers for large-scale quantum applications. Using a tool that implements this framework, we assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. We identify three qubit parameters, namely size, speed, and controllability, that are critical at scale to rendering these applications practical. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack, from algorithms to qubits.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Nov 2022.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 6.
10 papers.
- 2025End-to-end workflow for machine learning-based qubit readout with QICK and hls4mlcited by 8
- 2024In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methodscited by 6
- 2026Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computationcited by 4
- 2023A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correctioncited by 1
- 2023Myths and Legends in High-Performance Computingcited by 0
- 2026An Algorithm for Fast Assembling Large-Scale Defect-Free Atom Arrayscited by 0
- 2026Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scalingcited by 0
- 2026Practical advantage beyond the quadratic speedup limit with fully-quantum walkscited by 0
- 2026AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimizationcited by 0
- 2026Evaluating Verified Autonomy in Quantum Engineeringcited by 0
- 2026Evaluating Verified Autonomy in Quantum Engineering15 Sep 2026
- 2026AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization13 Aug 2026
- 2026Practical advantage beyond the quadratic speedup limit with fully-quantum walks24 Jul 2026
- 2026Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling15 Jul 2026
- 2026An Algorithm for Fast Assembling Large-Scale Defect-Free Atom Arrays9 Apr 2026
- 2026Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computation9 Apr 2026
- 2025End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml24 Jan 2025
- 2024In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods22 Jul 2024
- 2023A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction18 Jul 2023
- 2023Myths and Legends in High-Performance Computing6 Jan 2023