Using artificial intelligence to tame quantum systems

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Abstract quantum physics illustration alien technology

Quantum systems refer to the study of systems that operate on the principles of quantum mechanics. These systems include atoms, molecules, and subatomic particles, and are known for their unique properties such as superposition, entanglement, and quantum interference.

Machine learning drives the self-detection of pulses that stabilize quantum systems in the face of environmental noise.

Controlling a basketball lane is relatively simple, requiring only the application of mechanical force and human skill. However, controlling the motion of quantum systems such as atoms and electrons poses a much greater challenge. These tiny particles are vulnerable to turbulence that can cause them to deviate from their intended path in unexpected ways. In addition, motion within the system deteriorates, known as damping, and noise from environmental factors such as temperature further disrupt its path.

To counter the effects of damping and noise, researchers from the Okinawa Institute of Science and Technology (OIST) in Japan have found a way to use artificial intelligence to detect and apply pulses of light or voltage with fluctuating intensities to quantum systems. This method was able to successfully cool a micromechanical object to its quantum state and control its motion in an improved manner. The research was recently published in the journal Physical review research.

Quantum control through the application of artificial intelligence

The basic idea is to achieve quantum control by applying the AI ​​factor (left). For example, to cool the quantum sphere (red) down to the bottom of the well in the presence of environmental noise, an AI controller, which relies on reinforcement learning, will select intelligent control pulses (middle polar graph). Credit: OIST

Micromechanical objects, which are large compared to[{” attribute=””>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

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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