Data makes the world go round….

And generally, its large complex datasets that organizations need to keep up with. This is particularly true in sectors such as finance where high-level data management and analysis is essential.  This is not always easy especially when trying to understand the subtle links between data features. 

The biggest obstacle in the analysis is that the data itself is quite complex with intrinsic salient features. For example, in High Frequency Trading (HFT), data is characterized by irregular time intervals between trading events, a high level of correlations between trades and significant intraday periodic fluctuations.  This  makes risk assessment difficult, leading to less than ideal trading strategies.  Another area where complex patterns need to be analyzed is in customer relations management. Here, data about customer behavior is used for identifying trends such as spending patterns, purchase intention and even the likelihood to default on loan repayments.  Although there are obvious advantages to improving our data management and analytic techniques, correctly taking into account data that is correlated in subtle and complex ways remains a formidable computational challenge.

Thankfully, the past decade has seen Machine Learning (ML) making significant progress in this area. However, as corporations extract larger, more complex, and intricate datasets it becomes important to develop and refine computational capabilities in data analysis that go beyond contemporary machine learning practices.  


Step in Quantum-Artificial-Intelligence…

Although a truly universal quantum computer is still few years away, the contemporary Noisy Intermediate Scale Quantum (NISQ) technologies that  currently exist are already demonstrating a quantum speed up and computational advantage in several areas. 

Perhaps one of the most prominent research topics in this field is in developing quantum enhanced algorithms for Kernel based ML techniques.  In machine learning, kernel machines are a class of algorithms for pattern analysis, examples include support-vector machines (SVM) and neural networks.  These methods require only a user-specified kernel, i.e., a similarity function over pairs of data points in raw representation.

Refining and improving the feature mapping and associated kernel optimization is possible with quantum computation and is expected to lead to significant advantages over classical data analysis. This is one of most widely studied area in Q-A.I. and the basis of our product development for AbaQus. 

Using a variety of NISQ QPUs allows mapping classical data onto quantum circuits, essentially producing a higher dimensional quantum feature space that encodes correlations not detected by classical methods.  A classical ML technique is then used to optimize this quantum kernel, resulting in a hybrid Quantum enhanced ML protocol for categorizing and analyzing data sets.  

These capabilities are built into our AbaQus API ensuring an easily deployed quantum-ML data management system tailored to cater to the individual needs of your enterprise.