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Quantum Circuit Architecture Search on a Superconducting Processor.

Kehuan Linghu1, Yang Qian2,3, Ruixia Wang1

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Summary
This summary is machine-generated.

Quantum architecture search (QAS) enhances variational quantum algorithms (VQAs) by automatically designing efficient ansätze. This approach significantly boosts classification accuracy on quantum hardware, overcoming limitations of traditional methods.

Keywords:
noisy intermediate-scale quantumquantum architecture searchvariational quantum algorithms

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Quantum Machine Learning

Background:

  • Variational quantum algorithms (VQAs) offer computational advantages but struggle with ansatz expressivity-trainability trade-offs on noisy intermediate-scale quantum (NISQ) machines.
  • Heuristic ansätze in VQAs can lead to degraded performance due to limitations in balancing model complexity and learning efficiency.

Purpose of the Study:

  • To demonstrate the first proof-of-principle experiment applying quantum architecture search (QAS) to enhance VQAs.
  • To tailor ansätze for classification tasks using QAS on superconducting quantum processors.
  • To address the performance limitations of heuristic ansätze in VQAs on NISQ devices.

Main Methods:

  • Implemented an efficient automatic ansatz design technique, quantum architecture search (QAS).
  • Applied QAS to tailor a hardware-efficient ansatz for classification tasks on an 8-qubit superconducting quantum processor.
  • Analyzed loss landscapes and effective parameters to explain performance differences.

Main Results:

  • The QAS-designed ansatz significantly improved test accuracy from 31% to 98% compared to heuristic ansätze.
  • Demonstrated superior performance of QAS-tailored ansätze in quantum classification tasks.
  • Provided insights into the enhanced performance through loss landscape visualization and parameter analysis.

Conclusions:

  • Quantum architecture search (QAS) is an effective method for designing optimized ansätze in variational quantum algorithms (VQAs).
  • This approach overcomes limitations of heuristic ansätze, leading to substantial performance gains in quantum machine learning tasks.
  • The study offers guidance for developing adaptive ansätze for large-scale quantum learning problems.