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 $ZZ(\theta)$-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.

The liberalization process of the energy sector and global Organization of the Petroleum Exporting Countries (OPEC) crisis in the 1970s are two major drivers of the decentralization and decarbonization energy generation systems. Distributed energy systems, especially renewable energy sources (RES), have become more economically viable, and their market share has significantly increased in the last two decades. Wind and solar energy plants are the most prominent RES, which generates a fluctuating and weather dependent power output. Power systems are operated according to certain national and international norms where the voltage and frequency parameters should not exceed certain operational boundaries. Power networks are also designed to carry specific maximum power capacities. The power output characteristics of RES increase the vulnerability and uncertainty levels of power systems, which makes it challenging for the power systems operators to integrate higher amounts of RES into their control zones. Energy forecasting is one of the most promising methods which increases the operational capabilities of RES. Wind and solar forecasting algorithms have been used for two decades by various energy market players, such as utilities, RES plant operators, and power traders. Transmission and distribution system operators use energy forecasting algorithms to schedule their daily energy generation profiles, thus minimizing last-minute balancing power needs.