Background
Recent studies have showed the ability of Quantum Computers to produce atypical patterns which are hard to produce classically, which gives them a distinct advantage in the domain of machine learning. However, most devices today are considered Noisy Intermediate Scale Quantum (NISQ) devices, which are limited in the circuit breadth and suffer from high noise at larger circuit depth. Due to this, recent research has focused on creating Quantum Machine Learning (QML) models that can be run on NISQ devices. The main challenge in Quantum Machine Learning remains to defy an efficient mapping that encodes the classical data in the Hilbert Spaces. Traditional methods that utilize handcrafted schemes such as Angle encoding which are space-inefficient or require complex circuits.
Invention Description
Researchers at Arizona State University have developed a Quantum Polar Metric Learning (QPMeL) system, which is a novel hybrid classical-quantum algorithm designed to improve machine learning tasks like classification and similarity search. QPMeL addresses a critical limitation of existing quantum metric learning methods that struggle with the noise and limited resources of current quantum computers. Instead of relying on complex data compression circuits, QPMeL cleverly utilizes a classical model to learn the model representation of data encoding into a single qubit. This, combined with a shallow quantum circuit design and a unique 'Fidelity Triplet Loss' function, allows QPMeL to achieve superior data separation while being significantly more efficient in terms of quantum circuit depth and complexity. QPMeL's effciency, expressiveness, and key innovations make it well-suited for real world applications where optimizing resource usage and extracting complex relationships from data are crucial.
Potential Applications:
- Advanced Machine Learning
- Material Design
- Drug Discovery
- Climate Modeling
Benefits and Advantages:
- Efficiency- well suited for current NISQ-era quantum devices
- Sustainability- reduces the computational resources compared to traditional quantum machine learning methods
- Accuracy & Stability – within the constraints of existing hardware