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

arXiv 2510.09578 ↗First posted 10 Oct 2025quant-ph cs.ET cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 59 references that are in the map. First posted on arXiv 10 Oct 2025.

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References · 13

  1. 2020Variational Quantum Algorithmscited by 687
  2. 2018Supervised learning with quantum enhanced feature spacescited by 654
  3. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 192
  4. 2021QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuitscited by 97
  5. 2019Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computerscited by 22
  6. 2020Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuitscited by 6
  7. 2022CAFQA: A classical simulation bootstrap for variational quantum algorithmscited by 6
  8. 2022Navigating the dynamic noise landscape of variational quantum algorithms with QISMETcited by 4
  9. 2025Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoderscited by 3
  10. 2025EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Datacited by 2
  11. 2023VarSaw: Application-tailored Measurement Error Mitigation for Variational Quantum Algorithmscited by 1
  12. 2024Better Schedules for Low Precision Training of Deep Neural Networkscited by 1
1 more
  1. 2024Parallax: A Compiler for Neutral Atom Quantum Computers under Hardware Constraintscited by 1

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