With this white paper, BearingPoint offers an overview of quantum computing, its applications and describes a novel use case.

Quantum computing is a fundamentally different way of processing data. It does not implement strictly binary logic, does not process merely ones and zeros. Instead, these computers use quantum mechanical objects, called qubits, and gain a potentially exponential calculation speed-up using quantum mechanical effects, e.g., superposition and entanglement.

A current focus topic in digitalization is the analysis of complex, unstructured data. Many companies earn significant money by collecting, analyzing, and dealing with data. To process the growing amounts of data faster, a fundamental change in computation technology is necessary, which would potentially hit your business and interrupt your growth path.

Machine learning is mainly about classifying and recognizing patterns or clustering such data without imitating the processes that generated the data. So-called 'generative algorithms' have opened a new chapter for machine learning applications. A huge step has been taken to enable the machines to evaluate the data and generate new results, effectively becoming creative.

Generative adversarial networks (GANs), a specific method in this promising category of machine learning, address and solve a particular challenge of data analysis.

Possible application areas for GANs:

  1. Genomic analysis:  A deeper understanding of the genome’s behavior and being able to predict various types of cancer.
  2. Freight forecasting:  With GANs, predicting the exact amount of goods for specific scenarios is possible.
  3. Fraud detection:  Cybersecurity and fraud assessment are topics of great interest in analyzing unstructured data. GANs are predestined for this use case.
  4. Modelling of financial time series: The collection and enrichment of financial data sets is a major challenge for banks and the finance departments of large companies. GANs are particularly useful for backtesting high-dimensional objects and forecasts based on various scenarios.

While GANs are powerful, traditional computing limitations inhibit their application, such as creating a realistic multivariate financial time series, as these time series have underlying random processes, stationarity, heteroscedasticity, structural breaks, and outliers.

Financial data is high-dimensional, highly correlated, and it is an essential tool to measure business performance. Furthermore, it is crucial in various business applications, from risk management to backtesting strategies. Additionally, it could fulfill specific conditions that are not available in the (real) data, for example, market crashes.

Quantum generative adversarial networks (QuGANs) might have a significant advantage in generating realistic financial correlations. Besides the static dependence structure of multivariate financial time series, the algorithm might additionally capture the univariate time series features (e.g., autocorrelation) and the distributional properties of the margins altogether.

Modeling financial time series, namely, collecting and enriching financial datasets, is a huge challenge for banks and the treasury departments of large corporations. GANs are especially useful for the backtesting of high-dimensional objects and predictions based on different scenarios.

When do you need to look at quantum computations?

Better sooner than later. The quantum algorithm design is an entirely different area and not comparable with classical algorithm design. Obtaining the knowledge of designing and thinking for quantum computation can take years. Companies will have to become quantum-ready in the coming years and should consider steps to include quantum computers into their business processes. Quantum computers will provide game-changing new applications and upgrades for currently unsolvable problems and need to be on every major company’s 2030 strategy map.

BearingPoint recommends dealing with the subject of quantum computing and discussing the strategic considerations for your company. The focus is on developing quantum governance, including a strategic roadmap to integrate quantum computing into your business model and operations without disruption.

Learn more about the characteristics and functionalities of GAN, quantum computing, and QuGAN by downloading the full paper and speaking with our experts at BearingPoint.

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  • Stefan  Pechardscheck
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