Quantum AI

Quantum advantage provided by the fundamental quantum properties superposition, entanglement and the quantum parallelism that they provide, is revolutionising the way in which AI is delivered.

Quantum machine learning utilizes qubits and quantum operations to improve computational speed. This is especially beneficial to machine learning algorithms which often process immense quantities of data. Quantum-assisted machine learning uses hybrid methods that involve both classical and quantum processing in which computationally difficult tasks are reserved to a quantum processing unit (QPU) where they can be executed more quickly.

Here at Mewburn Ellis, we work on patent portfolios for a wide range of quantum technologies including quantum-assisted AI and machine learning. We regularly advise our clients in relation to the use of quantum algorithms for quantum-assisted computing.

Our team

Andrew Fearnside circle

Andrew Fearnside

Andrew has a particular interest in quantum algorithms. He handles patent applications for quantum technologies across the full spectrum of this technology sector including drafting and prosecuting patent applications for quantum computing, such as quantum information processing circuits, quantum algorithms, and quantum error correction, as well as inventions relating to AI, such as classical machine-learning algorithms and those employing quantum information. 

Johannes Biniok circle-1

Johannes Biniok

Johannes has a special focus on quantum algorithms. His undergraduate and postgraduate education focussed on quantum physics within the context of quantum foundations and quantum information, with a particular interest in the characterisation of information extractable from quantum states. His academic background is complemented with practical experience prosecuting consumer technology implementing AI/ML, such as cameras and televisions.

Amelia Sloan circle-1

Amelia Sloan

Amelia is a technical assistant in our AI team with particular expertise in the application of AI to digital security and quantum technologies. She has an MSci in mathematics from Imperial College London, and has experience prosecuting AI patents in relation to med-tech. While personally specialising in topology, her degree covers a wide range of subject areas, with knowledge in machine learning and number theory being especially useful in relation to cryptography and digital security. 

Case study: Quantum tech

We supported an international manufacturer of NMR systems in growing a strong patent portfolio related to innovations in NMR image classification.

 

Using a programmable quantum annealing device including a Restricted Boltzmann Machine, the Restricted Boltzmann Machine was trained to act as a classifier of image data and used to initialize a neural network for image classification thereby providing a quantum-enhanced system for use in classification of NMR images.

3D Redner of Quantum Computer

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Mewburn Ellis Forward is a biannual publication that celebrates the best of innovation and exploration. Through its pages we hope to inform and entertain, but also to encourage discussion about the most compelling developments taking place in the scientific and entrepreneurial world. Along the way, we’ll engage with the IP challenges that international organisations face every day.

 

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