QPMeL - Quantum-Aware Classically-Trained Embeddings via Projective Metric Learning
This work addresses scalability issues in quantum machine learning for researchers in the field, though it is incremental as it builds on existing QMeL methods.
The paper tackles the scalability limitations of Quantum Metric Learning (QMeL) on noisy quantum devices by proposing Quantum Polar Metric Learning (QPMeL), which uses a classical model to learn qubit parameters and a shallow quantum circuit with a fidelity-based loss function, achieving 3X better multi-class separation and using half the gates and depth compared to QMeL.
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. We propose Quantum Polar Metric Learning (QPMeL) that uses a classical model to learn the parameters of the polar form of a qubit. We then utilize a shallow PQC with $R_y$ and $R_z$ gates 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 our 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. We also demonstrate that QPMeL outperforms classical networks with similar configurations, presenting a promising avenue for future research on fully classical models with quantum loss functions.