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The power of one clean qubit in supervised machine learning
Mahsa Karimi1,2, Ali Javadi-Abhari3, Christoph Simon4,5
1Department of Physics and Astronomy, University of Calgary, Calgary, AB, T2N 1N4, Canada. mahsa.karimi1@ucalgary.ca.
Scientific Reports
|November 15, 2023
Summary
This study shows deterministic quantum computing with one qubit (DQC1) can efficiently estimate complex machine learning kernels using quantum coherence and discord. Quantum discord offers noise resilience, outperforming entanglement in noisy quantum computing environments.
Area of Science:
- Quantum Computing
- Machine Learning
- Quantum Information Theory
Background:
- Supervised machine learning often relies on complex kernel functions.
- Quantum computing offers potential speedups for computationally intensive tasks.
- Deterministic Quantum Computing with One Qubit (DQC1) is a non-universal quantum computing model.
Purpose of the Study:
- To explore the utility of quantum coherence and quantum discord within the DQC1 model for supervised machine learning.
- To develop an efficient method for estimating complex kernel functions using DQC1.
- To analyze the performance and noise resilience of DQC1 in machine learning tasks.
Main Methods:
- Leveraging quantum coherence and discord in the DQC1 model.
- Developing a method for estimating complex kernel functions.
- Implementing a binary classification problem on IBM quantum hardware.
- Analyzing the impact of quantum coherence, quantum discord, and hardware noise.
Main Results:
- Demonstrated an efficient method for estimating complex kernel functions using DQC1.
- Established a direct relationship between coherence consumption and kernel function estimation.
- Showcased a practical implementation on IBM hardware, analyzing noise effects.
- Highlighted the noise resilience of quantum discord compared to entanglement.
Conclusions:
- The DQC1 model, utilizing quantum coherence and discord, provides an efficient approach for supervised machine learning kernel estimation.
- Quantum discord presents a noise-resilient alternative to entanglement for quantum machine learning applications.
- Hardware implementation on IBM systems validates the potential of DQC1 for practical quantum machine learning.
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