System Bits: June 5

The right squeeze for quantum computing
In an effort to bring quantum computers closer to development, Hokkaido University and Kyoto University researchers have developed a theoretical approach to quantum computing that is 10 billion times more tolerant to errors than current theoretical models.

The team said their method may lead to quantum computers that use the diverse properties of subatomic particles to transmit, process and store extremely large amounts of complex information. They reminded that quantum computing has the potential to solve problems involving vast amounts of information, such as modeling complex chemical processes, far better and faster than modern computers.

A new theoretical model involving squeezing light to just the right amount to accurately transmit information using subatomic particles is bringing us closer to a new era of computing.
Source: Hokkaido University

Scientists around the world have been investigating ways to employ subatomic particles — quantum bits — which can exist in more than just two separate states, for the storage and processing of much vaster amounts of information.

One such approach involves using the inherent properties in photons of light, such as encoding information as quantum bits into a light beam by digitizing patterns of the electromagnetic field. But the encoded information can be lost from light waves during quantum computation, leading to an accumulation of errors. To reduce information loss, scientists have been experimenting with “squeezing” light. Squeezing is a process that removes tiny quantum-level fluctuations, referred to as noise, from an electromagnetic field. Noise introduces a certain level of uncertainty into the amplitude and phase of the electromagnetic field. Squeezing is thus an efficient tool for the optical implementation of quantum computers, but the current usage is inadequate.

However, Akihisa Tomita, an applied physicist at Hokkaido University, and his colleagues have suggested a novel way to dramatically reduce errors when using this approach, and have developed a theoretical model that uses both the properties of quantum bits and the modes of the electromagnetic field in which they exist. The approach involves squeezing light by removing error-prone quantum bits, when quantum bits cluster together.

The researchers assert this model is ten billion times more tolerant to errors than current experimental methods, meaning that it tolerates up to one error every 10,000 calculations.

Defense against Android phone tracking
To detect and block audio tracking of Android-based mobile phones and tablets, St. Pölten University of Applied Sciences researchers have created a free app, which is essentially an ultrasound-firewall.

Audio tracking is used by means of ultrasound to unnoticeably track the behavior of their users: for example, viewing certain videos or staying in specific rooms and places. The SoniControl app exposes and blocks this spying.

The team explained that permanent networking of mobile devices can endanger the privacy of users and lead to new forms of monitoring, and new technologies such as Google Nearby and Silverpush use ultrasonic sounds to exchange information between devices via loudspeakers and microphones, also called data over audio.

As more and more devices communicate via this inaudible communication channel, this ultrasonic communication allows devices to be paired and information to be exchanged, but it also makes it possible to track users and their behavior over a number of devices, much like cookies on the Web. Almost every device with a microphone and a loudspeaker can send and receive ultrasonic sounds. Users are usually unaware of this inaudible and hidden data transmission.

The SoniControl project detects acoustic cookies, brings them to the attention of users and if desired, blocks the tracking. The app is thus, in a sense, the first available ultrasound-firewall for smartphones and tablets, they said. “The most challenging part of developing the app was to devise a method that can detect different existing ultrasound-transmission techniques reliably and in real time,” said Matthias Zeppelzauer, head of the project and senior researcher in the Media Computing research group of the Institute of Creative\Media/Technologies at St. Pölten UAS.

Such ultrasonic signals can be used for so-called cross-device tracking, which makes it possible to track the user’s behavior across multiple devices, and relevant user profiles can be merged with one other. In this way, more accurate user profiles can be created for targeted advertising and filtering of internet content.
Unlike their electronic counterparts when visiting web pages, up to now it has not been possible to block acoustic cookies. In order to accept voice commands, the mobile phone microphone is often permanently active. Every mobile application that has access to the microphone as well as the operating system itself can at any time without notice: activate the microphone of a mobile device, listen to it, detect acoustic cookies and synchronise it over the Internet, Zeppelzauer explained. Users are often not informed of this information transmission during ongoing operation. Only a permanent deactivation of the microphone would help, whereby the device as a telephone would become unusable.

In the project, the team developed a procedure to expose the cookies and inform device users. For masking and blocking the ultrasonic data transfer, interference signals are transmitted via the loudspeaker of the mobile device. Thus, acoustic cookies can be neutralized before operating systems or mobile applications can access them. Users can selectively block cookies without affecting the functionality of the smartphone.

SoniControl App in the Google Play Store

Applying ML to earthquake data to boost geothermal production 
Offering new insights into earthquake data, Columbia University researchers have shown that machine learning algorithms could pick out different types of earthquakes from three years of earthquake recordings at The Geysers in California, one of the world’s oldest and largest geothermal fields, and that the repeating patterns of earthquakes appear to match the seasonal rise and fall of water-injection flows into the hot rocks below, suggesting a link to the mechanical processes that cause rocks to slip or crack, triggering an earthquake.

Using machine-learning tools to analyze 46,000 earthquakes at The Geysers geothermal field in California, researchers discovered patterns that correspond to water-injection flows, suggesting a link to the mechanical processes that produce earthquakes.
Source: Columbia University

Benjamin Holtzman, a geophysicist at Columbia’s Lamont-Doherty Earth Observatory explained, “It’s a totally new way of studying earthquakes. These machine learning methods pick out very subtle differences in the raw data that we’re just learning to interpret.”

The machine-learning assist helped researchers make the link to the fluctuating amounts of water injected below ground at The Geysers during the energy-extraction process, giving the researchers a possible explanation for why the computer clustered the signals as it did.

“The work now is to examine these clusters with traditional methods and see if we can understand the physics behind them,” said Felix Waldhauser, a seismologist at Lamont-Doherty. “Usually you have a hypothesis and test it. Here you’re building a hypothesis from a pattern the machine has found.”

If the earthquakes in different clusters can be linked to the three mechanisms that typically generate earthquakes in a geothermal reservoir — shear fracture, thermal fracture and hydraulic cracking — it could be possible to boost power output there, the team said. Specifically, if engineers can understand what’s happening in the reservoir in near real-time, they can experiment with controlling water flows to create more small cracks, and thus, heated water to generate steam and eventually electricity. These methods could also help reduce the likelihood of triggering larger earthquakes — at The Geysers, and anywhere else fluid is pumped underground, including at fracking-fluid disposal sites.

Finally, the tools could help identify the warning signs of a big one on its way — one of the holy grails of seismology, the researchers added.