Cisco Technology, Inc.

Ramana Rao V R Kompella

Hugo Latapie

Ozkan Kilic

Gaowen Liu

Myungjin Lee

Alireza Shabani

Pallavi Kalapatapu

Ashish Kundu

Simon Matthew Young

Harshit Daga

In one implementation, a device obtains a base machine learning model trained to label input data using a plurality of classes. The device receives a deployment task from a user interface indicative of a subset of one or more of the plurality of classes to be identified by a new model for deployment. The device selects a quantization level based on a difficulty associated with the deployment task. The device generates the new model for deployment that is quantized from the base machine learning model and specialized to label its input data using only the subset of one or more of the plurality of classes.

Specialized, data-free model quantization

Presented herein are techniques through which modular architectures and systems may be implemented for superconducting (SC) quantum processing elements or chips utilizing photonic interconnects. In one instance, an SC processing element is provided that includes a plurality of interconnected qubits, wherein a first qubit of the plurality of interconnected qubits is interconnected with a first microwave-optical transducer. In one instance, a system is provided that includes a first SC processing element comprising a first plurality of interconnected qubits, wherein a first microwave-optical transducer is interconnected with a first qubit of the first plurality of interconnected qubits; a second SC processing element comprising a second plurality of interconnected qubits, wherein a second microwave-optical transducer is interconnected with a first qubit of the second plurality of interconnected qubits; and an optical network interconnecting the first microwave-optical transducer and the second microwave-optical transducer.

Superconducting quantum architectures

In one embodiment, a first device in a network receives a quantum computing power metric indicative of a maximum available compute power of quantum computers. The first device receives, from a second device in the network, a listing of cryptographic suites available on the second device. The first device selects, based on the quantum computing power metric, a particular cryptographic suite from among the listing of cryptographic suites available on the second device. The first device sends, to the second device via the network, an indication that the particular cryptographic suite is to be used to encrypt and decrypt traffic exchanged between the first device and the second device.

Adaptive qubit-based threshold handshaking for quantum safe protocols

In one embodiment, a device maintains a metamodel that describes a monitored system. The metamodel comprises a plurality of layers ranging from a sub-symbolic space to a symbolic space. The device tracks updates to the metamodel over time. The device updates the metamodel based in part on sub-symbolic time series data generated by the monitored system. The device receives, from a learning agent, a request for the updates to a particular layer of the metamodel associated with a specified time period. The device provides, to the learning agent, data indicative of one or more updates to the particular layer of the metamodel associated with the specified time period.

Cognitive automation for networking, security, IoT, and collaboration

In one embodiment, a device presents information regarding an upstream machine learning workload and a downstream machine learning workload via a user interface. The device receives, via the user interface, a request to form a combined machine learning workload by connecting the upstream machine learning workload and the downstream machine learning workload. The device identifies, after receiving the request, a node associated with the upstream machine learning workload and a node associated with the downstream machine learning workload. The device forms the combined machine learning workload by configuring the node associated with the upstream machine learning workload to use one or more connector application programming interfaces to send data from the upstream machine learning workload to the node associated with the downstream machine learning workload for consumption.

Extending machine learning workloads

In one embodiment, a device obtains data models and workflow logic for a visual programming environment. The device constructs, based on the data models and workflow logic for the visual programming environment, a metamodel that comprises a knowledge graph. The device makes, using the metamodel, an evaluation of an interaction between a user and the visual programming environment. The device provides, based on the evaluation, visualization data to a user interface of the visual programming environment.

Growing community of inventors

Cupertino, CA, United States of America

Ramana Rao V R Kompella