Abstract: Systems and techniques that facilitate Clifford circuit synthesis are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise a receiver component that receives a quantum circuit design comprising a Clifford circuit representation and one or more circuit restrictions, and a machine learning component that generates, using a machine learning model, a replacement circuit based on the one or more circuit restrictions and the Clifford circuit representation, and generates a modified quantum circuit design by replacing the Clifford circuit representation with the replacement circuit.

Abstract: A computer-implemented process for generating a policy for design of quantum devices using a quantum hardware design kit including instructions and parameters associated with the instructions includes the following operations. An environment for a reinforcement learning architecture that includes a neural network as at least part of an agent is defined. A policy is generated by training the neural network using the environment. The defining the environment includes: defining actions of the neural network from a set of the instructions and parameters combinations associated with the quantum hardware design kit; and defining a reward function for generation of the policy.

Abstract: A compatibility is ascertained between a configuration of a quantum processor (q-processor) of a quantum cloud compute node (QCCN) in a quantum cloud environment (QCE) and an operation requested in a first instruction in a portion (q-portion) of a job submitted to the QCE, the QCE including the QCCN and a conventional compute node (CCN), the CCN including a conventional processor configured for binary computations. In response to the ascertaining, a quantum instruction (q-instruction) is constructed corresponding to the first instruction. The q-instruction is executed using the q-processor of the QCCN to produce a quantum output signal (q-signal). The q-signal is transformed into a corresponding quantum computing result (q-result). A final result is returned to a submitting system that submitted the job, wherein the final result comprises the q-result.

Abstract: Techniques for managing and compressing quantum output data (QOD) associated with quantum computing are presented. In response to receiving QOD from a quantum computer, a compressor component can compress QOD at first compression level to generate first compressed QOD, and can compress QOD at second compression level to generate second compressed QOD, the second compressed QOD can be less compressed than the first compressed QOD. Compressor management component (CMC) can determine whether first QOD includes sufficient data to enable it to be suitably processed by quantum logic. If so, CMC can allow first compressed QOD to continue to be sent to quantum logic and can discard second compressed QOD. If not sufficient, CMC can determine that second compressed QOD is to be processed by quantum logic. If CMC determines second compressed QOD does not include sufficient data, CMC can determine that the QOD is to be processed by quantum logic.

Abstract: A method for validation and runtime estimation of a quantum algorithm includes receiving a quantum algorithm and simulating the quantum algorithm, the quantum algorithm forming a set of quantum gates. The method further includes analyzing a first set of parameters of the set of quantum gates and analyzing a second set of parameters of a set of qubits performing the set of quantum gates. The method further includes transforming, in response to determining at least one of the first set of parameters or the second set of parameters meets an acceptability criterion, the quantum algorithm into a second set of quantum gates.

Abstract: Quantum code-related entities are obtained and one or more of a set of one or more trained neural network models are selected for inferencing based on the quantum code-related entities. The inferencing is performed using the submitted quantum code-related entities and the selected one or more trained neural network models, and a result of the inferencing operation is returned.

Abstract: Methods and systems use one or more machine learning models to automatically generate three-dimensional sound. A multimodal content item is accessed by a computing device. Three-dimensional sound is automatically generated by the computing device using the one or more machine learning models based on the multimodal content item.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to port code while adapting the code for a configuration of a target system. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an identification component that identifies a configuration of a target system on which a translated source code is to be executed, and a translation component that translates, based on a constraint defining the configuration, an original source code into the translated source code. In one or more embodiments, the translated source code can comprise a code language not comprised by the original source code and/or can be modified to align with the constraint, which can be of a hardware and/or software configuration.

Abstract: One or more computer processors receive a domain name system (DNS) request in response to a client connecting to a compute resource. The one or more computer processors decoding the DNS request into one or more provision parameters. The one or more computer processors determining that the compute resource is unavailable for a connection with the client utilizing the identified IP address. The one or more computer processors, responsive to determining that the compute resource is not available or not ready, provisioning and deploying a new compute resource according to the one or more decoded provision parameters, wherein the new compute resource is available to the client under the identified IP address.

Abstract: A method and system determine network based access to restricted systems. The method includes receiving a request for a permission access status of a party seeking access to one of the restricted systems. A database of periodically updated lists of entities is accessed. A name of the party is extracted from the request. A determination is made whether the name does not match one of the entities. The name is decomposed into parts if the name not matching one of the entities. A determination is made whether any of the parts of the name matches one of the entities. A denial of access status is forwarded from the computer server to an external computing device if any of the parts of the name matches one of the entities.

Abstract: Systems and techniques that facilitate backend quantum runtimes are provided. In various embodiments, a system can comprise a backend receiver component that can access a computer program provided by a client device, wherein the computer program is configured to indicate a quantum computation. In various aspects, the system can further comprise a backend runtime manager component that can host the computer program by instantiating a backend classical computing resource. In various instances, the backend classical computing resource can orchestrate both classical execution of the computer program and quantum execution of the quantum computation indicated by the computer program.

Abstract: A method for validation and runtime estimation of a quantum algorithm includes receiving a quantum algorithm and simulating the quantum algorithm, the quantum algorithm forming a set of quantum gates. The method further includes analyzing a first set of parameters of the set of quantum gates and analyzing a second set of parameters of a set of qubits performing the set of quantum gates. The method further includes transforming, in response to determining at least one of the first set of parameters or the second set of parameters meets an acceptability criterion, the quantum algorithm into a second set of quantum gates.

Abstract: Techniques for facilitating quantum pulse optimization using machine learning are provided. In one example, a system includes a classical processor and a quantum processor. The classical processor employs a quantum pulse optimizer to generate a quantum pulse based on a machine learning technique associated with one or more quantum computing processes. The quantum processor executes a quantum computing process based on the quantum pulse.

Abstract: A method and system determine network based access to restricted systems. The method includes receiving a request for a permission access status of a party seeking access to one of the restricted systems. A database of periodically updated lists of entities is accessed. A name of the party is extracted from the request. A determination is made whether the name does not match one of the entities. The name is decomposed into parts if the name not matching one of the entities. A determination is made whether any of the parts of the name matches one of the entities. A denial of access status is forwarded from the computer server to an external computing device if any of the parts of the name matches one of the entities.

Abstract: Systems and techniques that facilitate backend quantum runtimes are provided. In various embodiments, a system can comprise a memory that can store computer-executable components. The system can further comprise a processor that can be operably coupled to the memory and that can execute the computer-executable components stored in the memory. In various embodiments, the computer-executable components can comprise an execution orchestration engine component that can parse a computer program into classical and quantum portions and that can host the computer program by instantiating a classical computing resource.

Abstract: The illustrative embodiments provide a method, system, and computer program product for validating quantum algorithms using a machine learning model. In an embodiment, a method includes receiving a training data set. In an embodiment, a method includes training, by a first processor, a machine learning model with the training data set for validation of quantum circuits. In an embodiment, a method includes generating, by the machine learning model, a set of rules for validation of quantum circuits.

Abstract: A method for validation and runtime estimation of a quantum algorithm includes receiving a quantum algorithm and simulating the quantum algorithm, the quantum algorithm forming a set of quantum gates. The method further includes analyzing a first set of parameters of the set of quantum gates and analyzing a second set of parameters of a set of qubits performing the set of quantum gates. The method further includes transforming, in response to determining at least one of the first set of parameters or the second set of parameters meets an acceptability criterion, the quantum algorithm into a second set of quantum gates.

Abstract: Techniques for quantum data post-processing are provided. In one example, a system includes a quantum programming component and a post-processing component. The quantum programming component receives quantum output data that includes a set of quantum results for a quantum circuit in response to simulation of the quantum circuit. The post-processing component adjusts the quantum output data associated with the quantum circuit based on client system data indicative of information for a client system that consumes the quantum output data.

Abstract: Techniques facilitating quantum software developer kit and framework as a service are provided. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an execution component that executes, on a quantum device located within a cloud computing environment, a code based on an identification of the code received from a communication device. A quantum software development kit can execute on the communication device.

Abstract: Systems and techniques that facilitate backend quantum runtimes are provided. In various embodiments, a system can comprise a backend receiver component that can access a computer program provided by a client device, wherein the computer program is configured to indicate a quantum computation. In various aspects, the system can further comprise a backend runtime manager component that can host the computer program by instantiating a backend classical computing resource. In various instances, the backend classical computing resource can orchestrate both classical execution of the computer program and quantum execution of the quantum computation indicated by the computer program.