Ionq and Ge research confirmed that quantum computing has great potential in risk aggregation

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lately , Quantum hardware Inc IonQ Announced its partnership with Ge innovation center （GE Research）, In the cooperative project of exploring the application of quantum computing to multivariate distribution modeling in risk management , Gratifying preliminary results have been achieved .

IonQ According to , Based on Standardization 、 Historical data indexed Quantum Circuit Born Machine（ QCBM） frame , They and GE Research The correlation between three to four indexes is effectively trained and learned with quantum circuits . meanwhile , In some cases , They found that the prediction results based on the quantum framework are better than the classical modeling methods . These results strongly confirm , quantum copulas It will make more intelligent data-driven analysis and decision-making in business applications .

IonQ CEO and President Peter Chapman Express ：“IonQ Positive and GE Research Together, we will promote the possible application goals of quantum computing . When modeling multiple variables with high accuracy , Although classical computing technology faces the problem of low efficiency , But our joint efforts have identified a new training strategy , Quantum computing results can be optimized even when the system is extended , And already in IonQ Aria Tested on the system .IonQ I am glad to be able to apply these new methods , To deal with real-world scenes that are too complex to be solved .”

Although mathematical approximation is used to form copula The classical technique of is a good method to build multivariable risk model , But they are limited in scaling . In dealing with this bottleneck ,IonQ and GE Research By using data from the four most representative stock indices that are easily accessible to changing market conditions , Successfully in IonQ A quantum with up to four variables has been trained on the ion trap system copula Model .

By studying the historical basis structure between the four index returns in this time range , The research team trained their models to understand the potential dynamics . Besides , The newly proposed methods include optimization techniques , By reducing the local minimum and eliminating the common gradient vanishing problem in the practice of quantum machine learning , This allows the model to be extended .

Based on these improvements , They showed a faster 、 Methods to perform multivariate analysis more accurately ,GE Researchers will use the updated 、 Better ways to evaluate major manufacturing processes （ Such as product design 、 Plant operations and supply chain management ） The risk of .

Senior executive officer and head of digital technology of General Electric David Vernooy say ：“ As we have seen from recent fluctuations in the global supply chain , The world needs more effective methods and tools to manage high levels of change , And risk related factors . We are in contact with IonQ The early results obtained in the financial use cases of cooperative research show that , Risk aspects that are highly relevant to such variable scenarios , Quantum computing has great potential to better understand and reduce risk .”

Link to the original text ：

https://www.hpcwire.com/off-the-wire/ionq-and-ge-research-demonstrate-high-potential-of-quantum-computing-for-risk-aggregation/

writing ：Jonathan Spencer Jones

compile ： Li every

edit ： Mu Yi

notes ： Ben Translated from “HPCWire”, Do not represent Quantum outpost view .