Overview

Quantum machine learning is a field that combines principles from quantum physics and machine learning to develop algorithms and models. It uses the unique properties of quantum systems, such as superposition and entanglement, to perform computations for data analysis, pattern recognition, and predictive modeling.

Unlike classical machine learning, which operates on classical bits (0 or 1), quantum machine learning leverages qubits. This enables quantum computers to process and explore exponentially more data and possibilities than classical computers, potentially leading to more efficient algorithms and improved predictions. Quantum machine learning algorithms include quantum versions of classical techniques like clustering, classification, and optimization, as well as novel methods designed specifically for quantum systems. These algorithms aim to solve complex problems and exploit the computational advantages offered by quantum mechanics.

Challenges with QML

Quantum machine learning is still an emerging field, and there are many challenges to overcome, including the need for more powerful and stable quantum computers, robust quantum error correction, and effective mapping of classical machine learning problems to quantum algorithms. Nonetheless, researchers, such as Terra Quantum, are actively exploring its potential applications in various domains, such as drug discovery, or optimization problems.

Terra Quantum and Machine Learning

We enhance prediction accuracy and learning capacity through our hybrid quantum machine learning techniques. This means better quality answers with less data.

You can learn more on Quantum Machine Learning via TQ Academy. Find out more here.