International Business Machines Corporation

E.on Digital Technology Gmbh

Gururaja Hebbar

Kavitha Hassan Yogaraj

Aaron K Baughman

Micah Forster

Yoshika Chhabra

Antonio Mezzacapo

Mohit Trivedi

Sudeep Ghosh

Gabriele Francesco Maria Agliardi

Herman Colquhoun

Arjun Kashyap

Rukhsan Ul Haq

Francesco Tacchino

Kumar Jang Bahadur Ghosh

Daniel Joseph Fry

Omar Shehab

Corey O'Meara

Piergiacomo Sabino

Souvik Mazumder

Cortiana Giorgio

Providing a hybrid neural network architecture by training a plurality of models using a set of training data, the plurality comprising quantum models and classical models, evaluating each model using a common test data set, assigning one or more evaluation metrics to each model according to the evaluation, generating a plurality of networks, each network comprising a combination of the models, evaluating a flow of each network, selecting a network according to the flow, and providing the selected network to a user.

Generating hybrid quantum-classical neural network architectures

Using a classical data model executing on a classical processor, a set of classical features is scored. A classical feature comprises a first attribute of a resource, and a score of the classical feature comprises an evaluation of a utility of the classical feature in predicting a result involving the resource. Using a quantum data model executing on a quantum processor and the scored set of classical features, a set of quantum features is scored. The scored set of classical features and the scored set of quantum features are correlated, forming a combined set of scored features. Using the combined set of scored features and a first set of input data of a resource, a valuation of the resource is calculated.

Combined classical/quantum predictor evaluation

Using a model executing on a classical processor, a set of classical features is scored. The scored set of classical features is divided into a set of feature groups, a number of classical features in a group determined according to a qubit capability of a quantum processor. Using a model executing on the quantum processor and a group of the scored set of classical features, a set of quantum features is scored. The score of a quantum feature is adjusted according to an accuracy of the quantum data model. The scored set of classical features and the scored set of quantum features are combined according to a measure of differences between the scored set of classical features and the scored set of quantum features. Using the combined set of scored features and a first set of input data of a resource, a valuation of a resource is calculated.

Combined classical/quantum predictor evaluation with model accuracy adjustment

A system can comprise a memory that stores computer executable components, and a processor, operably coupled to the memory, that executes the computer executable components comprising: an evaluating component that employs a quantum register to output a processed vector based on a first variable, of set of variables indexed over a selected dimension, and a valuation component that approximates a value function and that, based on the approximating, outputs a valuation amount representing provision of an aspect over the selected dimension, wherein the value function is a function of an inner product of a set of processing functions that are based on the set of variables, including the first variable, wherein a first processing function of the set of processing functions and of the inner product is based on the processed vector, and wherein the set of variables correspond to conditions defining the provision of the aspect.

Portfolio value evaluation through quantum polynomial approximation

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Gururaja Hebbar