Quantum Resonance Interferometry (QRI)

Dr. Sandeep Gulati

Quantum Resonance Interferometry (QRI), an advanced, mathematics-based, active signal processing software technology, was discovered and developed by mathematician Sandeep Gulati, Ph.D., who was a senior manager in supercomputing and quantum computing at Caltech/NASA Jet Propulsion Laboratory.

Active Signal Processing
Prior to the discovery of QRI, active signal processing was discovered some twenty years ago and developed as a technology to amplify and otherwise improve detection and discrimination of weak signals. However, the development of active signal-processing has so far resulted in large, highly complex and expensive systems, such as Superconducting Quantum Interference Devices (SQUID) circuitry for medical imaging, military radar, etc. with few if any applications to the signal processing needs of scientific and electronic devices, communications and business applications.

The Signal to Noise Problem
Background noise is a term within active or passive signal processing that describes a phenomenon which masks or obscures signals of interest, making them difficult to detect and analyze. Background noise can occur in any kind of data, for example computing, optical or acoustic data or simply too much information. Background noise becomes a problem when the signal strength of the background noise level is high enough to make it difficult to discriminate the signal of interest. It is especially problematic where the desired signal of interest occurs at strength lower than the strength of the surrounding background noise. Imagine trying to hear a single person talking during a game on the opposite side of a crowded football stadium.

How QRI Works
QRI active signal processing software enables detection and analysis of signals above and far below the level of background noise. Rather than subtracting background signal, which inevitably also subtracts signal of interest hidden beneath background, QRI algorithms analyze the statistical disturbances to the background noise which are caused by the presence of the signal of interest. The level of disturbance is non-linearly correlated to level of the signal of interest. Unlike active signal processing in SQUID or radar, QRI software algorithms interrogate only data; uniquely QRI requires no interaction with physical phenomena. QRI does not require new devices or new data; information hidden within old files can be detected and once calibrated, results are stable and reproducible.

First, the typical noise characteristics captured in the data files of a system or instrument are modeled and encoded in mathematical algorithms. Second, a specifically designed mathematical signal is used to iteratively interrogate or interfere with the noise data set and then with the noise data set mixed with a data set containing calibrated signal of interest. The resulting new data sets from the mathematical interaction are then examined for evidence of disturbances in the noise state which are characteristic of the signal of interest. In short, disturbances in the noise are used to amplify the signal of interest. QRI is able to accurately and selectively analyze signals that would have gone undetected using conventional passive analysis methods that attempt to filter out background noise.

Various U.S. and foreign patents may cover QRI and its application to specific measurementation applications. For more information please contact Tomer D. Tamarkin at tomer@climatecite.com

Leave a Reply