Quantum Machine LearningView All Blogs

Discover how machine learning is pivotal in overcoming the 'reality gap' in quantum devices, enhancing predictions and performance by addressing inherent variability through a physics-informed approach.

Exploring the unprecedented capabilities of quantum neural networks, this article delves into how quantum computing could renaissanceize machine learning, offering advantages over classical approaches through increased model capacity and trainability.

IBM researchers have demonstrated a quantum advantage in machine learning, revealing that quantum kernels can identify patterns in data sets that appear as random noise to classical computers, offering a new pathway for quantum machine learning.

A groundbreaking study by Los Alamos National Laboratory reveals that quantum entanglement significantly enhances the scalability of quantum machine learning, overcoming the previous assumption of needing exponentially large datasets for training quantum neural networks.

Financial institutions need to continuously interpret complex data streams to extract information necessary for providing accurate credit risk evaluation, managing market-making services, and predicting emissions in the context of green finance, among other things. Classical machine learning techniques used to assist and provide insights to these services have limitations as these data streams are, in general, complex. Combining quantum computing with classical machine learning methodology could offer more powerful resources for processing these data streams, given the potential for quantum computers to process some types of information more efficiently than with classical resources alone.

Quantum machine learning algorithms based on parameterized quantum circuits are promising candidates for near-term quantum advantage. Although these algorithms are compatible with the current generation of quantum processors, device noise limits their performance, for example by inducing an exponential flattening of loss landscapes. Error suppression schemes such as dynamical decoupling and Pauli twirling alleviate this issue by reducing noise at the hardware level.