David Layden, a Ph.D. student at the Massachusetts Institute of Technology (MIT) Interdisciplinary Quantum Engineering Group (QEG), has adopted a new spatial noise filtering method that can facilitate the development of ultra-sensitive quantum sensors.
David Layden, Ph.D., Interdisciplinary Quantum Engineering Group (QEG), Massachusetts Institute of Technology
Although quantum technology has great long-term potential in computing applications, they are closer to reality in sensing applications. Because of the ability to measure structures as small as photons, particles, and neurons, quantum technology will be in metrology, biology. Many other fields, such as neuroscience, open up new application prospects.
A new study by the Massachusetts Institute of Technology's Interdisciplinary Quantum Engineering Group (QEG) is focused on addressing one of the fundamental challenges facing quantum sensor systems: removing environmental noise from the signal being measured.
According to QEG PhD student David Layden, the root of the problem lies in the extreme sensitivity of quantum sensors to the surrounding environment. These sensors are usually based on the additive effect of two different states of quantum. A small external force can cause a phase change between the two states, which can be used to measure physical quantities such as temperature, motion, electric field, and magnetic field, and the measurement accuracy can reach an unprecedented resolution.
But such high sensitivity means that the sensor picks up many unrelated ambient noise in addition to the signal of interest. Through a process called decoherence, this noise introduces uncertainty into the phase relationship of quantum sensors and limits their ability to make accurate measurements.
Some noise reduction techniques have been developed to increase sensitivity by reducing decoherence. One common technique is dynamic decoupling—a series of control pulses are introduced into the system to filter the noise in the signal based on frequency. However, this technique is not compatible with DC signals, and typically the signal measured by the sensor is the DC signal.
In the past few decades, research on quantum computing has also added error correction schemes, such as the use of redundant qubits. Although these are effective in information processing applications, they have significant limitations on sensors.
"The standards in the computing world are a bit overkill here," Layden said. "It's really good at correcting errors and reducing noise, but it often corrects the normal signal because it doesn't distinguish between the two."
Recently, in the developed error correction quantum sensing (ECQS) technology, a recovery operation can effectively remove noise that affects the sensor in different directions from the signal—such as noise along the x-axis and the signal along the z-axis. However, these geometry-based techniques are difficult to distinguish between noise and signals from sensors in the same direction, and such situations are more common.
In a recent paper published in the journal npj Quantum Information, Layden and Paola Cappellaro (Esther and Harold E. Edgerton Associate Professor of Nuclear Science and Engineering and QEG Leader) revealed a new approach that will The established ECQS correction technique is applied to signals and noise emitted from the same direction. This method enables frequency-independent filtering because it takes advantage of spatial rather than temporal noise correlation.
"For quantum computing, the usual way to correct mistakes is to spread the mesh as much as possible to correct as much as possible," Layden said. "In sensing applications, you need to form the appropriate range of holes in the network very carefully. In order to pass the specific signal you are looking for. In fact, we are adapting existing signal processing techniques for quantum devices. Surprisingly, these seemingly unrelated concepts of quantum computing and signal processing can actually Seamlessly blend together."
As the core requirement of quantum sensor noise reduction technology, distinguishing between signal and noise can be done in a variety of ways. In addition to the geometric methods used in past ECQS techniques, the researchers took advantage of the fact that the noise in many quantum devices is not completely unpredictable, but full of correlation. For example, dynamic decoupling takes advantage of noise correlation at different times. Similarly, QEG researchers' new ECQS approach takes advantage of the noise correlation of quantum sensors at different locations. In this way, the new method can distinguish the signal from the noise, even in the common case where both are in the same direction (for example along the z-axis).
Layden and Cappellaro's approach complements existing DD and ECQS methods, which is useful because noise sources vary widely in different sensing applications. Diversifying the filtering tools to meet different needs, as well as the new method, opens up new applications for quantum sensors, which can correct the noise in the full three-dimensional space.
Although the developments to date have been largely theoretical, experimental research at QEG Laboratories is ongoing, including the assessment of noise challenges faced by different types of quantum systems. “We have been working on similar research,†Layden explains. “Small-scale implementations have only recently been realized; although there are many theoretical perspectives on how large-scale quantum devices work, it seems that any real progress can only be medium-sized in the near future. The newly developed QEG technology may prove to be particularly useful."
Layden and Cappellaro also collaborate with colleagues from Yale University to advance theoretical research on their projects. Project funding is provided by the US Army Research Office, the National Science Foundation, and the Canadian Natural Sciences and Engineering Research Council.
“We have not yet reached the stage of obtaining experimental results, but we are building hardware and continually simulating, and the iterations and interactions not only shape the project, but also affect several related projects,†adds Layden.
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