QuantrolOx has secured £1.4 million in seed investment led by Nielsen Ventures and Hoxton Ventures to expand quantum computing. The round was also led by Voima Ventures, Remus Capital, Dr. Hermann Hauser, and Laurent Caraffa. Founded by Oxford professor Andrew Briggs, tech entrepreneur Vishal Chatrath, the company’s chief scientist Natalia Ares, and head of quantum technologies Dominic Lennon co-founded the company, the company aims to manage qubits within quantum computers using machine learning.
Instead of the straightforward manipulation of ones and zeros in traditional binary-based computers, quantum computers employ quantum bits or qubits. In addition, these qubits feature a third state known as “superposition,” which may represent either a one or a zero at the same time. Instead of having the value of either a one or a zero, superposition allows two qubits to represent four situations at the same time. This characteristic can allow a computing revolution in which future computers will be capable of more than just mathematical computations and algorithms.
Quantum computers also use the entanglement principle, which Albert Einstein described as “spooky action at a distance.” The fact that the state of particles from the same quantum system cannot be represented independently of each other is known as entanglement. They are still part of the same system, even though they are separated by huge distances.
QuantrolOx is developing automated machine learning-based control software for quantum technologies that allows them to tune, stabilize, and optimize qubits. Quantum computers need thousands of qubits, yet qubits vary somewhat owing to errors in control instruments, manufacture, and design, necessitating distinct sets of control parameters to make each one useful. To create a functional quantum computer, a complex procedure is necessary. The issues of turning and characterizing qubits become increasingly difficult and significant as the number of qubits grows.
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QuantrolOx’s software is technology-neutral, meaning it may be used with any quantum technology. However, for the time being, the company is concentrating on solid-state qubits. That’s primarily because they are systems to which the company has access, including through a tight collaboration with a Finnish lab that the company wasn’t ready to reveal. QuantrolOx, like any other machine learning challenge, requires a large amount of data in order to create successful machine learning models.
QuantrolOx is now focusing on forming new agreements with quantum computer manufacturers. These are significant collaborations since the team not only requires physical access to the equipment but also the source code that controls them in order to interact with these systems.
