Two teams of physicists at University of Innsbruck have designed the first programmable quantum sensor, and tested it in the laboratory. To do so they applied techniques from quantum information processing to a measurement problem. The innovative method promises quantum sensors whose precision reaches close to the limit set by the laws of nature.
The physicists used a method from quantum information processing: variational quantum algorithms describe a circuit of quantum gates that depends on free parameters. Through optimization routines, the sensor autonomously finds the best settings for an optimal result.
With the new approach, scientists can optimize quantum sensors to the point where they achieve the best possible precision technically permissible.
For some time, it has been understood that atomic clocks could run even more accurately by exploiting quantum mechanical entanglement. However, there has been a lack of methods to realize robust entanglement for such applications. The Innsbruck physicists are now using tailor-made entanglement that is precisely tuned to real-world requirements. With their method, they generate exactly the combination consisting of quantum state and measurements that is optimal for each individual quantum sensor. This allows the precision of the sensor to be brought close to the optimum possible according to the laws of nature, with only a slight increase in overhead.
This theoretical concept was now implemented in practice for the first time at the University of Innsbruck.
The physicists performed frequency measurements based on variational quantum calculations on their ion trap quantum computer. Because the interactions used in linear ion traps are still relatively easy to simulate on classical computers, the theory colleagues were able to check the necessary parameters on a supercomputer at the University of Innsbruck. Although the experimental setup is by no means perfect, the results agree surprisingly well with the theoretically predicted values. Since such simulations are not feasible for all sensors, the scientists demonstrated a second approach: They used methods to automatically optimize the parameters without prior knowledge.
The paper has been published in Nature.
