Abstract: A method, system, and product for performance analysis of quantum programs. A quantum program comprises plurality of code artifacts and is compilable into a quantum circuit. A representation of the quantum circuit that implements the quantum program is obtained. The quantum circuit manipulates a plurality of qubits over a plurality of cycles using a plurality of quantum gates. The representation of the quantum circuit includes circuit components. A performance measurement of a code artifact of the quantum program is automatically computed based on one or more circuit components that are mapped to the code artifact by a component to code mapping. The component to code mapping maps circuit components of the representation to the quantum circuit to respective code artifacts of the quantum program.

Abstract: Implementations are directed to causing a voice bot to utilize a plurality of ML layers in resolving unique personal identifier(s) for a human while the voice bot is engaged in a corresponding conversation with the human. The unique personal identifier(s) can include a unique sequence of alphanumeric characters that is personal to the human. In some implementations, ASR speech hypothes(es) corresponding to spoken utterance(s) that include the unique personal identifier(s) can be processed to generate candidate unique personal identifier(s), given alphanumeric character(s) of the candidate unique personal identifier(s) can be selected, and the voice bot can prompt the human with clarification request(s) to clarify the given alphanumeric character(s) until it is predicted to correspond to the an actual unique personal identifier(s) for the human(s). The unique personal identifier(s) can then be utilized in performance of further action(s) by the voice bot and/or other systems.

Abstract: Implementations described herein relate to reducing latency in automated assistant interactions. In some implementations, a client device can receive audio data that captures a spoken utterance of a user. The audio data can be processed to determine an assistant command to be performed by an automated assistant. The assistant command can be processed, using a latency prediction model, to generate a predicted latency to fulfill the assistant command. Further, the client device (or the automated assistant) can determine, based on the predicted latency, whether to audibly render pre-cached content for presentation to the user prior to audibly rendering content that is responsive to the spoken utterance. The pre-cached content can be tailored to the assistant command and audibly rendered for presentation to the user while the content is being obtained, and the content can be audibly rendered for presentation to the user subsequent to the pre-cached content.

Abstract: Implementations are directed to causing a voice bot to utilize a plurality of ML layers in resolving unique personal identifier(s) for a human while the voice bot is engaged in a corresponding conversation with the human. The unique personal identifier(s) can include a unique sequence of alphanumeric characters that is personal to the human. In some implementations, ASR speech hypothes(es) corresponding to spoken utterance(s) that include the unique personal identifier(s) can be processed to generate candidate unique personal identifier(s), given alphanumeric character(s) of the candidate unique personal identifier(s) can be selected, and the voice bot can prompt the human with clarification request(s) to clarify the given alphanumeric character(s) until it is predicted to correspond to the an actual unique personal identifier(s) for the human(s). The unique personal identifier(s) can then be utilized in performance of further action(s) by the voice bot and/or other systems.

Abstract: Implementations described herein relate to reducing latency in automated assistant interactions. In some implementations, a client device can receive audio data that captures a spoken utterance of a user. The audio data can be processed to determine an assistant command to be performed by an automated assistant. The assistant command can be processed, using a latency prediction model, to generate a predicted latency to fulfill the assistant command. Further, the client device (or the automated assistant) can determine, based on the predicted latency, whether to audibly render pre-cached content for presentation to the user prior to audibly rendering content that is responsive to the spoken utterance. The pre-cached content can be tailored to the assistant command and audibly rendered for presentation to the user while the content is being obtained, and the content can be audibly rendered for presentation to the user subsequent to the pre-cached content.

Abstract: Implementations described herein relate to reducing latency in automated assistant interactions. In some implementations, a client device can receive audio data that captures a spoken utterance of a user. The audio data can be processed to determine an assistant command to be performed by an automated assistant. The assistant command can be processed, using a latency prediction model, to generate a predicted latency to fulfill the assistant command. Further, the client device (or the automated assistant) can determine, based on the predicted latency, whether to audibly render pre-cached content for presentation to the user prior to audibly rendering content that is responsive to the spoken utterance. The pre-cached content can be tailored to the assistant command and audibly rendered for presentation to the user while the content is being obtained, and the content can be audibly rendered for presentation to the user subsequent to the pre-cached content.

Abstract: Implementations are directed to causing a voice bot to utilize a plurality of ML layers in resolving unique personal identifier(s) for a human while the voice bot is engaged in a corresponding conversation with the human. The unique personal identifier(s) can include a unique sequence of alphanumeric characters that is personal to the human. In some implementations, ASR speech hypothes(es) corresponding to spoken utterance(s) that include the unique personal identifier(s) can be processed to generate candidate unique personal identifier(s), given alphanumeric character(s) of the candidate unique personal identifier(s) can be selected, and the voice bot can prompt the human with clarification request(s) to clarify the given alphanumeric character(s) until it is predicted to correspond to the an actual unique personal identifier(s) for the human(s). The unique personal identifier(s) can then be utilized in performance of further action(s) by the voice bot and/or other systems.

Abstract: A method of load balancing data packets at an array is disclosed. The method includes receiving a data packet encoded in a first format at an input of the array. The received data packet is assigned to an assigned element of the array, and the data packet is routed to a device. A message encoded in a second format is received from the device at the array. Information is extracted from a payload portion of the message, and the message is assigned to the assigned element of the array based on the information extracted from the payload portion of the message.

Abstract: Verification of Internet connectivity using multiple prior connection attempts to Internet destination(s). The Internet destinations may be destinations that have high reliability and that do not easily have intermediating systems that might deny a connection request. Such an Internet destination might be, for example, root Domain Name Server (DNS) servers. Connection attempt results are obtained by for at least some of the connection attempts, tracking which resulted in success and failure. Internet connectivity is then verified based on the collective results, rather than relying on any one single connection attempt. In one embodiment, the frequency of the connection attempts may depend on a current state of the Internet connection.

Abstract: A method of load balancing data packets at an array is disclosed. The method includes receiving a data packet encoded in a first format at an input of the array. The received data packet is assigned to an assigned element of the array, and the data packet is routed to a device. A message encoded in a second format is received from the device at the array. Information is extracted from a payload portion of the message, and the message is assigned to the assigned element of the array based on the information extracted from the payload portion of the message.

Abstract: Verification of Internet connectivity using multiple prior connection attempts to Internet destination(s). The Internet destinations may be destinations that have high reliability and that do not easily have intermediating systems that might deny a connection request. Such an Internet destination might be, for example, root Domain Name Server (DNS) servers. Connection attempt results are obtained by for at least some of the connection attempts, tracking which resulted in success and failure. Internet connectivity is then verified based on the collective results, rather than relying on any one single connection attempt. In one embodiment, the frequency of the connection attempts may depend on a current state of the Internet connection.