# Assessing requirements to scale to practical quantum advantage

arXiv: https://arxiv.org/abs/2211.07629 · HTML: https://mlsanity.com/paper/2211.07629/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2022.

## Built on by (most cited first, 10 of 10)

- [End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml](https://mlsanity.com/paper/2501.14663/) (2025)
- [In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods](https://mlsanity.com/paper/2407.15776/) (2024)
- [Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computation](https://mlsanity.com/paper/2604.08358/) (2026)
- [A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction](https://mlsanity.com/paper/2307.09463/) (2023)
- [Myths and Legends in High-Performance Computing](https://mlsanity.com/paper/2301.02432/) (2023)
- [An Algorithm for Fast Assembling Large-Scale Defect-Free Atom Arrays](https://mlsanity.com/paper/2604.08669/) (2026)
- [Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling](https://mlsanity.com/paper/2607.15313/) (2026)
- [Practical advantage beyond the quadratic speedup limit with fully-quantum walks](https://mlsanity.com/paper/2607.22818/) (2026)
- [AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization](https://mlsanity.com/paper/2608.12936/) (2026)
- [Evaluating Verified Autonomy in Quantum Engineering](https://mlsanity.com/paper/2609.17439/) (2026)

## 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.