System Bits: Jan. 17

Overcoming quantum hurdles; illness-sensing biosensors.

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Turning quantum systems from novelties into useful technologies
In what is believed to be a major achievement that could help bring the strange and powerful world of quantum technology closer to reality, University of Sydney researchers have demonstrated the ability to “see” the future of quantum systems, and used that knowledge to preempt their demise.

The applications of quantum-enabled technologies are compelling and already demonstrating significant impacts – especially in the realm of sensing and metrology — and the potential to build exceptionally powerful quantum computers using quantum bits, or qubits, is driving investment from the world’s largest companies.

Trapped Ytterbium ions were used as one of the most advanced laboratory quantum systems for this study.  Professor Biercuk's research laboratories are now located in the Sydney Nanoscience Hub, after six years as a visiting scientist at the National Measurement Institute. (Source: University of Sydney)

Trapped Ytterbium ions were used as one of the most advanced laboratory quantum systems for this study.  Professor Biercuk’s research laboratories are now located in the Sydney Nanoscience Hub, after six years as a visiting scientist at the National Measurement Institute. (Source: University of Sydney)

A significant obstacle remains. To build reliable quantum technologies, quantum systems must be randomized by their environments, called decoherence, which effectively destroys the useful quantum character, the researchers said.

As such, the team has taken a technical quantum leap in addressing this, using techniques from big data to predict how quantum systems will change and then preventing the system’s breakdown from occurring.

“Much the way the individual components in mobile phones will eventually fail, so too do quantum systems, but in quantum technology the lifetime is generally measured in fractions of a second, rather than years,” said Professor Michael J.  Biercuk, from the University of Sydney’s School of Physics and a chief investigator at the Australian Research Council’s Centre of Excellence for Engineered Quantum Systems

He said his group had demonstrated it was possible to suppress decoherence in a preventive manner by developing a technique to predict how the system would disintegrate. “Humans routinely employ predictive techniques in our daily experience; for instance, when we play tennis we predict where the ball will end up based on observations of the airborne ball. This works because the rules that govern how the ball will move, like gravity, are regular and known. But what if the rules changed randomly while the ball was on its way to you? In that case it’s next to impossible to predict the future behavior of that ball. And yet this situation is exactly what we had to deal with because the disintegration of quantum systems is random. Moreover, in the quantum realm observation erases ‘quantumness,’ so our team needed to be able to guess how and when the system would randomly break. We effectively needed to swing at the randomly moving tennis ball while blindfolded.”

The team turned to machine learning for help in keeping their quantum systems – qubits realized in trapped atoms – from breaking. What might look like random behavior actually contained enough information for a computer program to guess how the system would change in the future. It could then predict the future without direct observation, which would otherwise erase the system’s useful characteristics. As the predictions were remarkably accurate, the team used their guesses to preemptively compensate for the anticipated changes.
Doing this in real time allowed the team to prevent the disintegration of the quantum character, extending the useful lifetime of the qubits.

“We know that building real quantum technologies will require major advances in our ability to control and stabilise qubits – to make them useful in applications. Our techniques apply to any qubit, built in any technology, including the special superconducting circuits being used by major corporations. We’re excited to be developing new capabilities that turn quantum systems from novelties into useful technologies. The quantum future is looking better all the time,” Biercuk added.

Wearable sensors suggest possible illness
According to a study by Stanford University School of Medicine researchers, wearable sensors that monitor heart rate, activity, skin temperature and other variables can reveal a lot about what is going on inside a person, including the onset of infection, inflammation and even insulin resistance.

Geneticist Michael Snyder was wearing seven biosensors collecting data about his health when he noticed changes in his heart rate and oxygen level during a flight. When he later developed a fever, he suspected he had been infected with Lyme disease. Subsequent tests confirmed his suspicion. (Source: Stanford University School of Medicine)

Geneticist Michael Snyder was wearing seven biosensors collecting data about his health when he noticed changes in his heart rate and oxygen level during a flight. When he later developed a fever, he suspected he had been infected with Lyme disease. Subsequent tests confirmed his suspicion.
(Source: Stanford University School of Medicine)

An important component of the ongoing study is to establish a range of normal, or baseline, values for each person in the study and when they are ill. “We want to study people at an individual level,” said Michael Snyder, PhD, professor and chair of genetics.

The team collected nearly 2 billion measurements from 60 people, including continuous data from each participant’s wearable biosensor devices and periodic data from laboratory tests of their blood chemistry, gene expression and other measures. Participants wore between one and seven commercially available activity monitors and other monitors that collected more than 250,000 measurements a day. The team collected data on weight; heart rate; oxygen in the blood; skin temperature; activity, including sleep, steps, walking, biking and running; calories expended; acceleration; and even exposure to gamma rays and X-rays.

The study demonstrated that, given a baseline range of values for each person, it is possible to monitor deviations from normal and associate those deviations with environmental conditions, illness or other factors that affect health. Distinctive patterns of deviation from normal seem to correlate with particular health problems. Algorithms designed to pick up on these patterns of change could potentially contribute to clinical diagnostics and research.

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