Full metadata
Title
QPMeL: Quantum Polar Metric Learning
Description
Deep metric learning has recently shown extremely promising results in the classical data domain, creating well-separated feature spaces. This idea was also adapted to quantum computers via Quantum Metric Learning (QMeL). QMeL consists of a 2 step process with a classical model to compress the data to fit into the limited number of qubits, then train a Parameterized Quantum Circuit (PQC) to create better separation in Hilbert Space. However, on Noisy Intermediate Scale Quantum (NISQ) devices, QMeL solutions result in high circuit width and depth, both of which limit scalability. The proposed Quantum Polar Metric Learning (QPMeL ), uses a classical model to learn the parameters of the polar form of a qubit. A shallow PQC with Ry and Rz gates is then utilized to create the state and a trainable layer of ZZ(θ)-gates to learn entanglement. The circuit also computes fidelity via a SWAP Test for the proposed Fidelity Triplet Loss function, used to train both classical and quantum components. When compared to QMeL approaches, QPMeL achieves 3X better multi-class separation, while using only 1/2 the number of gates and depth. QPMeL is shown to outperform classical networks with similar configurations, presentinga promising avenue for future research on fully classical models with quantum loss functions.
Date Created
2024
Contributors
- Sharma, Vinayak (Author)
- Shrivastava, Aviral (Thesis advisor)
- Jiang, Zilin (Committee member)
- Kambhampati, Subbarao (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
42 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.193330
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2024
Field of study: Computer Science
System Created
- 2024-05-02 01:05:37
System Modified
- 2024-05-02 01:05:43
- 6 months 3 weeks ago
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