# Three Birds with One Stone: Improving Performance, Convergence, and System Throughput with Nest

arXiv: https://arxiv.org/abs/2510.09578 · HTML: https://mlsanity.com/paper/2510.09578/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 59 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Noise-Induced Barren Plateaus in Variational Quantum Algorithms](https://mlsanity.com/paper/2007.14384/) (2020)
- [QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits](https://mlsanity.com/paper/2107.10845/) (2021)
- [Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers](https://mlsanity.com/paper/1901.11054/) (2019)
- [Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits](https://mlsanity.com/paper/2012.07711/) (2020)
- [CAFQA: A classical simulation bootstrap for variational quantum algorithms](https://mlsanity.com/paper/2202.12924/) (2022)
- [Navigating the dynamic noise landscape of variational quantum algorithms with QISMET](https://mlsanity.com/paper/2209.12280/) (2022)
- [Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders](https://mlsanity.com/paper/2504.13113/) (2025)
- [EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data](https://mlsanity.com/paper/2503.14473/) (2025)
- [VarSaw: Application-tailored Measurement Error Mitigation for Variational Quantum Algorithms](https://mlsanity.com/paper/2306.06027/) (2023)
- [Better Schedules for Low Precision Training of Deep Neural Networks](https://mlsanity.com/paper/2403.02243/) (2024)
- [Parallax: A Compiler for Neutral Atom Quantum Computers under Hardware Constraints](https://mlsanity.com/paper/2409.04578/) (2024)

## Abstract (arXiv)

Variational quantum algorithms (VQAs) have the potential to demonstrate quantum utility on near-term quantum computers. However, these algorithms often get executed on the highest-fidelity qubits and computers to achieve the best performance, causing low system throughput. Recent efforts have shown that VQAs can be run on low-fidelity qubits initially and high-fidelity qubits later on to still achieve good performance. We take this effort forward and show that carefully varying the qubit fidelity map of the VQA over its execution using our technique, Nest, does not just (1) improve performance (i.e., help achieve close to optimal results), but also (2) lead to faster convergence. We also use Nest to co-locate multiple VQAs concurrently on the same computer, thus (3) increasing the system throughput, and therefore, balancing and optimizing three conflicting metrics simultaneously.