I have a basic idea of Machine Learning and Quantum Computing, but couldn't grasp how they come together. Could you go into more detail into how they integrate with each other?
Most existing systems are built on and within the paradigm of classical computers. This includes Machine Learning as is popularly known. So models from the linear regressor to attention based models are built to process data on a system that works with regular (classical) bits.
Since we began exploring quantum computing, we created qubits and as such we needed a new processor (hence all the research that has been and is being done). This processor takes us a step away from the generalisation that we made with the regular bits (0 or 1).
However, this also means that systems built to take advantage of the classical bit have to be "corrected" to utilize the qubit.
So, data storage as we know it can now be optimized using qubits to store way more information in the new unit called the qubit. Machine learning as you may know involves the use and processing of large amounts of data so this is a plus. The model’s operation is optimized by the more efficient processing.
Still about processing, with qubits, information transfer is exponentially faster (refer to the Quantum Entanglement section). This means that the machine learning workflow as we know it becomes more efficient as we can process more in less time.
The fundamentals behind different algorithms will probably not change much but how they are implemented (to take advantage of this upgrade in the processor) will change. And new ones with no classical history will be created.
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I have a basic idea of Machine Learning and Quantum Computing, but couldn't grasp how they come together. Could you go into more detail into how they integrate with each other?
Most existing systems are built on and within the paradigm of classical computers. This includes Machine Learning as is popularly known. So models from the linear regressor to attention based models are built to process data on a system that works with regular (classical) bits.
Since we began exploring quantum computing, we created qubits and as such we needed a new processor (hence all the research that has been and is being done). This processor takes us a step away from the generalisation that we made with the regular bits (0 or 1).
However, this also means that systems built to take advantage of the classical bit have to be "corrected" to utilize the qubit.
So, data storage as we know it can now be optimized using qubits to store way more information in the new unit called the qubit. Machine learning as you may know involves the use and processing of large amounts of data so this is a plus. The model’s operation is optimized by the more efficient processing.
Still about processing, with qubits, information transfer is exponentially faster (refer to the Quantum Entanglement section). This means that the machine learning workflow as we know it becomes more efficient as we can process more in less time.
The fundamentals behind different algorithms will probably not change much but how they are implemented (to take advantage of this upgrade in the processor) will change. And new ones with no classical history will be created.