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Related Experiment Video

Updated: May 25, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Image Similarity Quantum Algorithm and Its Application in Image Retrieval Systems.

Qingchuan Yang1,2, Xianing Feng1, Lianfu Wei1

  • 1Information Quantum Technology Laboratory, International Cooperation Research Center of China Communication and Sensor Networks for Modern Transportation, School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China.

Entropy (Basel, Switzerland)
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

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A new quantum algorithm for image similarity measurement, the amplitude estimation quantum inner product (AE-QIP) algorithm, offers improved efficiency. It achieves comparable precision to the swap test-based quantum inner product (ST-QIP) algorithm with reduced resource consumption.

Area of Science:

  • Quantum computing
  • Image processing
  • Computational intelligence

Background:

  • Image similarity measurement is crucial in image processing.
  • Quantum algorithms, like the swap test-based quantum inner product (ST-QIP), enhance similarity measurement.
  • The ST-QIP algorithm's destructive nature limits its practical application due to high resource and time costs.

Purpose of the Study:

  • To introduce an advanced quantum inner product algorithm based on amplitude estimation (AE-QIP) for computing image similarity.
  • To address the limitations of the ST-QIP algorithm by optimizing quantum resource utilization and computational time.
  • To evaluate the AE-QIP algorithm's performance in image retrieval tasks.

Main Methods:

  • Implementation of the AE-QIP algorithm using a quantum simulator.
Keywords:
AE-QIPST-QIPalgorithm performanceimage retrievalimage similarity

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  • Evaluation of AE-QIP for image similarity measurement and retrieval.
  • Comparative analysis of AE-QIP against the ST-QIP algorithm in terms of precision, qubit consumption, and processing time.

    • Main Results:

    • The AE-QIP algorithm demonstrates comparable precision to the ST-QIP algorithm.
    • AE-QIP significantly reduces qubit consumption and average processing time compared to ST-QIP.
    • Increasing ancillary qubits in AE-QIP can further improve accuracy.
    • AE-QIP shows enhanced efficiency within acceptable error thresholds.

    Conclusions:

    • The AE-QIP algorithm presents a more efficient approach to quantum image similarity measurement.
    • AE-QIP offers a promising alternative to ST-QIP, balancing precision with resource efficiency.
    • Further research is required to optimize overall quantum retrieval system performance.