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D-Wave and Zapata AI have joined forces in a strategic partnership to harness the capabilities of quantum computing and generative AI. This collaboration aims to develop commercial applications for quantum-enabled machine learning, focusing on accelerating discoveries and solving complex optimization problems in various industries.

Researchers at MIT CSAIL and Project CETI have discovered a sperm whale 'alphabet' using machine learning technologies. The study, titled 'Contextual and Combinatorial Structure in Sperm Whale Vocalizations,' points to key breakthroughs in understanding cetacean communication by analyzing sperm whale codas in context.

Togal.ai is transforming the construction industry by automating the estimation process with advanced AI, reducing time and enhancing accuracy. This case study explores how Togal leverages machine learning to streamline construction workflows, making them more efficient and less error-prone.

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 revolutionize 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.

From a quantum algorithms perspective, stochastic gradient descent processes are solved here using quantum ordinary differential equation (ODE) solvers derived from the findings of ref. 30, based on linearizing non-linear equations using so-called quantum Carleman linearization. We find that the corresponding differential equation solvers can, in principle, also be used in the discrete setting and for stochastic gradient descent in machine learning. However, in the discrete setting, the theoretical details are significantly different from those applicable in the small learning rate limit. In this work, we systematically establish a novel discrete Carleman linearization in the supplementary material, including reformulations of the Carleman linearization theory, a tensor network diagrammatic notation for the discretization error, analytic derivations of higher-order corrections, and explicit examples for lower order expansions. Further details about the novelty of our algorithms beyond the findings of ref. 30 are summarized in the supplementary material.

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.

Quantum computing can help and now that CERN openlabs current Quantum Technology Initiative comprises dozens of projects across computing, sensing, communication, and theory. Read the case study.CERN has become the newest IBM Quantum Hub, the the CERN-IBM relationship will be closer than ever before. Until now, scientists have been using classical machine learning techniques to analyze raw data captured by the particle detectors, automatically selecting the best candidate events. But we think we can greatly improve this screening process by boosting machine learning with quantum. In particular, exploiting the exponentially large qubit A Hilbert space can be thought of as the state space in which all quantum state vectors live. The main difference between a Hilbert space and any random vector space is that a Hilbert space is equipped with an inner product, which is an operation that can be performed between two vectors, returning a scalar. Read more about linear algebra in the Qiskit TextbookHilbert space, quantum computers should be able to capture quantum correlations in the particle collision datasets more efficiently and accurately than conventional, classical, machine learning algorithms. This ability should lead to a better interpretation of experiments.