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Related Experiment Video

Updated: Mar 29, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Training-Free Quantum Architecture Search Under Realistic Noise via Expressibility-Guided Evolution.

Seyedali Mousavi1, Seyedhamidreza Mousavi1, Paul Pettersson1

  • 1Department of Computer Science and Engineering, Mälardalen University, 72123 Västerås, Sweden.

Entropy (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

We introduce a new quantum architecture search method that uses expressibility measures instead of noisy circuit training. This training-free approach significantly reduces computational cost and is device-agnostic for designing noise-robust parameterized quantum circuits (PQCs).

Keywords:
Kullback–Leibler divergenceNISQ devicesevolutionary optimizationexpressibilityinformation-theoretic complexityparameterized quantum circuitsquantum architecture search

Related Experiment Videos

Last Updated: Mar 29, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Area of Science:

  • Quantum Computing
  • Quantum Information Science

Background:

  • Designing noise-robust parameterized quantum circuits (PQCs) is crucial for the noisy intermediate-scale quantum (NISQ) era.
  • Current quantum architecture search methods are computationally expensive due to training large SuperCircuits and noisy SubCircuit evaluations.

Purpose of the Study:

  • To develop a training-free quantum architecture search framework for noise-robust PQCs.
  • To establish information-theoretic expressibility measures as effective surrogates for performance-based estimators in noisy quantum environments.

Main Methods:

  • Proposed a novel framework utilizing information-theoretic expressibility measures, specifically KL-divergence, for quantum architecture search.
  • Introduced an expressibility-guided evolutionary search that bypasses the need for SuperCircuit training and noisy executions.
  • Demonstrated device-agnostic applicability by evaluating expressibility independent of specific hardware noise models.

Main Results:

  • Empirically demonstrated a monotonic association between noise-free KL-divergence-based expressibility and noisy task loss across various architectures and noise models.
  • Achieved competitive performance compared to SuperCircuit-based methods with substantially reduced computational cost.
  • Validated the approach using IBM-derived Qiskit noise models.

Conclusions:

  • Information-theoretic expressibility serves as an effective and computationally efficient surrogate for ranking PQC architectures under realistic noise.
  • The proposed training-free, device-agnostic framework accelerates the design of noise-robust PQCs for NISQ devices.
  • This approach enables reusable architectures across different quantum devices without re-searching.