Accenture Global Solutions Limited

Parag Rane

Gopali Raval Contractor

Prasanna Srinivasa Rao

Subhashini Lakshminarayanan

Amar Ratanlal Bafna

Arati Deo

Maria Lopes

Harsh Dhiraj Vira

Vaibhav Kumar Daga

Adnan Khan

Mukesh Kumar Chaudhary

Ratan Yashwant Panjwani

Ankit Laxmichand Dedhia

Payal Argarwal

Mayank Sudhir Singh

Prasanna S R

Payal Agarwal

Azimullah Shahzad

Saurabh Juneja

Vallinath Sriramula

Susan Patricia McNAMARA

In some implementations, an olfaction system may receive partition coefficients associated with one or more molecules detected in a headspace of a sample captured from an environment. The olfaction system may generate a quantum-ready dataset based on the partition coefficients using a partial quantum autoencoder that includes one or more quantum gate layers. The olfaction system may use a quantum approximate optimization algorithm to identify, within a spectrum of potential smells simulated by a quantum circuit, a set of smells emitted by the sample based on the quantum-ready dataset. The olfaction system may map a set of objects to the set of smells emitted by the sample. The olfaction system may predict a future state associated with the set of smells emitted by the sample using one or more hybrid quantum machine learning models.

Virtual nose using quantum machine learning and quantum simulation

A device may receive logs and files associated with a system to be migrated to a cloud computing environment, and may determine workload data of the system. The device may derive a data lineage for source data and target data, and may assess a utilization pattern of the system. The device may process the workload data, the data lineage, and data identifying utilization of a distributed computing feature of the system, with a model, to label utilization features and to recommend a cloud architecture. The device may process the workload data, the data lineage, and the data identifying utilization, with a natural language processing model, to determine a cost of migrating the system. The device may process the labelled utilization features, the cloud architecture, and the cost, with a Q-matrix model, to determine migration actions for migrating the system, and may perform actions based on the migration actions.

Utilizing a machine learning model to migrate a system to a cloud computing environment

A device may receive transaction data associated with transactions. The transaction data may be associated with transaction entries that are associated with the transactions. The device may process, using a matching model, the transaction entries to classify the transaction entries into a set of matched transaction entries and a set of unmatched transaction entries. The device may update a transaction grouping model based on the set of matched transaction entries to create an updated transaction grouping model. The device may determine, using the updated transaction grouping model, that a subset of the set of unmatched transaction entries are associated with a same transaction. The device may classify the subset of the set of unmatched transaction entries as grouped transaction entries. The device may provide an indication that the grouped transaction entries and the set of matched transaction entries are reconciled transactions.

Machine learning system for transaction reconciliation

Aspects of the present disclosure provide systems, methods, and computer-readable storage media facilitating automated apparel design using deep learning techniques. For example, user instructions may be received as text data (or converted to text data from audio data representing user speech), and natural language processing (NLP) may be performed on the text data to interpret the user instructions. An apparel design may be generated in real-time/substantially real-time based on the user instructions. For example, the interpreted user instructions may be provided as input to at least one machine learning (ML) model that is configured to determine one or more visual apparel elements based on the user instructions and to generate the apparel design based on the visual apparel elements. One or more operations may be initiated based on the apparel design.

Automated apparel design using machine learning

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Thane, India

Parag Rane