By submitting this form, you are agreeing to the Terms of Use and Privacy Policy.
The field of quantum machine learning (QML), which combines machine learning and quantum computing, is quickly expanding. In order to accelerate machine learning algorithms and allow the creation of novel machine learning models that are not feasible with classical computers, it aims to take advantage of the power of quantum computers.
Fundamentally, QML seeks to improve conventional machine learning tasks like classification, clustering, and regression using quantum computing. This is accomplished by using quantum algorithms, which process and analyse huge datasets more quickly and accurately than classical computers. Quantum computing, for instance, could be used to speed up neural network training, a common machine learning method.
However, QML also investigates fresh approaches to machine learning that benefit from the special features of quantum physics. Quantum neural networks are one illustration of this, which use qubits rather than conventional bits as their fundamental computational unit. Quantum support vector machines are another illustration; they use quantum algorithms to improve the decision boundary in a classification issue.
Since QML is still a young field, much of the study is still in its infancy. However, it has the ability to completely change the machine learning industry and bring about fresh developments in artificial intelligence.
Global Quantum Machine Learning Market accounted for $XX Billion in 2023 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2024 to 2030.
For the OceanTM SDK, D-Wave has released a new hybrid solution plug-in to assist businesses in utilising quantum technology. The plug-in makes it simple to incorporate optimisation in feature selection efforts and allows developers to more easily incorporate quantum into feature selection and machine learning workflows.
With less needed development time or ramp up and a quicker time to value, the new plug-in makes it simple for developers to incorporate feature selection tools. To reduce the size of computation with quantum neural networks, Mitsubishi Electric has created a quantum artificial intelligence (AI) technology that automatically designs and optimises inference models.
Compact inference models are realised by the novel quantum machine learning (QML) technology by fully utilising the incredible ability of quantum computers to express exponentially larger-state space with the number of quantum bits (qubits). Even with little data, the technology can use a hybrid of quantum and classical AI to overcome the drawbacks of the former and achieve better performance while drastically reducing the size of AI models.
