Google’s Quantum Control Optimization
Though reported back in April, this article [at the source] has a collection of disruptive technology ‘news items’ raising some interesting questions. Not all are directly related to quantum computing…yet. Because Quantum is Coming. Qubit.
Google causes more facial-recog pain, machine learning goes quantum – and how to lose a job if an AI doesn’t like your face
+ Reinforcement learning can aid quantum computers: AI researchers over at Google have built a machine learning algorithm to model unwanted noise that can disrupt qubits in quantum computers.
“Our results open a venue for wider applications in quantum simulation, quantum chemistry and quantum supremacy tests using near-term quantum devices,” Google concluded.
+ Qubits have to be carefully controlled to get them to interact with one another in a quantum system. The smallest disturbances from external energy sources can knock them out of a quantum state, preventing them from performing some sort of calculation correctly. So Google engineers have developed a reinforcement learning algorithm for something they call “quantum control optimization”.
+ “Our framework provides a reduction in the average quantum logic gate error of up to two orders-of-magnitude over standard stochastic gradient descent solutions and a significant decrease in gate time from optimal gate synthesis counterparts,” it said this week.
+ The algorithm’s goal is to predict the amount of error introduced in a quantum system based on the state its in and model how that error can be reduced in simulations.
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