Machine learning drives the self-discovery of pulses that stabilize quantum systems in the face of environmental noise.
Controlling the trajectory of a basketball is relatively straightforward, as it only requires the application of mechanical force and human skill. However, controlling the movement of quantum systems like atoms and electrons poses a much greater challenge. These tiny particles are prone to perturbations that can cause them to deviate from their intended path in unexpected ways. Additionally, movement within the system degrades, known as damping, and noise from environmental factors like temperature further disrupts its trajectory.
To counteract the effects of damping and noise, researchers from Okinawa Institute of Science and Technology (OIST) in Japan have found a way to use artificial intelligence to discover and apply stabilizing pulses of light or voltage with fluctuating intensity to quantum systems. This method was able to successfully cool a micro-mechanical object to its quantum state and control its motion in an optimized way. The research was recently published in the journal Physical Review Research.
Cooling Micro-Mechanical Objects to Quantum States
Micro-mechanical objects, which are large compared to an atom or electron, behave classically when kept at a high temperature, or even at room temperature. However, if such mechanical modes can be cooled down to their lowest energy state, which physicists call the ground state, quantum behavior could be realized in such systems. These kinds of mechanical modes then can be used as ultra-sensitive sensors for force, displacement, gravitational acceleration, etc. as well as for quantum information processing and computing.
“Technologies built from quantum systems offer immense possibilities,” said Dr. Bijita Sarma, the article’s lead author and a Postdoctoral Scholar at OIST Quantum Machines Unit in the lab of Professor Jason Twamley. “But to benefit from their promise for ultraprecise sensor design, high-speed quantum information processing, and quantum computing, we must learn to design ways to achieve fast cooling and control of these systems.”
Machine Learning for Quantum Optimization
The machine learning-based method that she and her colleagues designed demonstrates how artificial controllers can be used to discover non-intuitive, intelligent pulse sequences that can cool a mechanical object from high to ultracold temperatures faster than other standard methods. These control pulses are self-discovered by the machine learning agent. The work showcases the utility of artificial machine intelligence in the development of quantum technologies.
Quantum computing has the potential to revolutionize the world by enabling high computing speeds and reformatting cryptographic techniques. That is why many research institutes and big-tech companies such as Google and IBM are investing a lot of resources in developing such technologies. But to enable this, researchers must achieve complete control over the operation of such quantum systems at very high speed, so that the effects of noise and damping can be eliminated.
“In order to stabilize a quantum system, control pulses must be fast – and our artificial intelligence controllers have shown the promise to achieve such a feat,” Dr. Sarma said. “Thus, our proposed method of quantum control using an AI controller could provide a breakthrough in the field of high-speed quantum computing, and it might be a first step to achieving quantum machines that are self-driving, similar to self-driving cars. We are hopeful that such methods will attract many quantum researchers for future technological developments.”
Reference: “Accelerated motional cooling with deep reinforcement learning” by Bijita Sarma, Sangkha Borah, A Kani and Jason Twamley, 29 November 2022, Physical Review Research.
DOI: 10.1103/PhysRevResearch.4.L042038
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