Experimental Quantum Principal Component Analysis via Parametrized Quantum Circuits.

Area of Science:

• Quantum Computing
• Machine Learning
• Data Analysis

Background:

• Principal Component Analysis (PCA) is a computationally intensive machine learning technique.
• Existing quantum PCA (qPCA) algorithms face experimental hurdles, limiting practical implementation.
• Challenges include preparing multiple quantum state copies and executing quantum phase estimations.

Purpose of the Study:

• To propose a new, experimentally feasible qPCA algorithm.
• To reduce the complexity of implementing qPCA.
• To demonstrate the application of the novel qPCA algorithm in a real-world task.

Main Methods:

• Developed a hybrid classical-quantum control approach for qPCA.
• Utilized parameterized quantum circuits optimized with simple measurement observables.
• Applied the algorithm to human face recognition using the Yale Face Dataset.

Main Results:

• The proposed qPCA algorithm significantly reduces experimental complexity.
• Successfully encoded eigenface information into a quantum processor.
• Achieved high-fidelity face recognition on a test dataset.

Conclusions:

• The novel qPCA algorithm offers a practical pathway for experimental implementation.
• Demonstrates the potential of quantum machine learning for pattern recognition tasks.
• Opens new avenues for theoretical and experimental qPCA research.