ABSTRACT
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.
Description
BACKGROUND
The subject disclosure relates to quantum computing and, more specifically, to optimizing quantum pulses provided to a quantum computer.
SUMMARY
The following presents a summary to provide a basic understanding of one or more embodiments of the invention. This summary is not intended to identify key or critical elements, or delineate any scope of the particular embodiments or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, devices, systems, computer-implemented methods, apparatus and/or computer program products for facilitating quantum pulse optimization using machine learning are described.
According to an embodiment, a system can comprise a classical processor and a quantum processor. The classical processor can employ 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 can execute a quantum computing process based on the quantum pulse.
According to another embodiment, a computer-implemented method is provided. The computer-implemented method can comprise optimizing, by a system operatively coupled to a processor, a quantum pulse based on a machine learning technique associated with one or more quantum computing processes to generate an optimized quantum pulse. The computer-implemented method can also comprise transmitting, by the system, the optimized quantum pulse to a quantum processor.
According to yet another embodiment, a computer program product for facilitating quantum pulse optimization using machine learning can comprise a computer readable storage medium having program instructions embodied therewith. The program instructions can be executable by a processor and cause the processor to optimize, by the processor, a quantum pulse based on a machine learning technique and historical data associated with one or more quantum computing processes to generate an optimized quantum pulse. The program instructions can also cause the processor to transmit, by the processor, the optimized quantum pulse to a quantum processor.
DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an example, non-limiting system that facilitates quantum pulse optimization using machine learning in accordance with one or more embodiments described herein.
FIG. 2 illustrates a block diagram of an example, non-limiting system that includes a quantum pulse optimizer in accordance with one or more embodiments described herein.
FIG. 3 illustrates another example, non-limiting system that facilitates quantum pulse optimization using machine learning in accordance with one or more embodiments described herein.
FIG. 4 illustrates an example, non-limiting system that includes a machine learning process for facilitating quantum pulse optimization in accordance with one or more embodiments described herein.
FIG. 5 illustrates an example, non-limiting system that includes quantum processor that employs an optimized quantum pulse in accordance with one or more embodiments described herein.
FIG. 6 illustrates a flow diagram of an example, non-limiting computer-implemented method for facilitating quantum pulse optimization using machine learning in accordance with one or more embodiments described herein.
FIG. 7 illustrates a flow diagram of another example, non-limiting computer-implemented method for facilitating quantum pulse optimization using machine learning in accordance with one or more embodiments described herein.
FIG. 8 illustrates a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein can be facilitated.
FIG. 9 illustrates a block diagram of an example, non-limiting cloud computing environment in accordance with one or more embodiments of the present invention.
FIG. 10 illustrates a block diagram of example, non-limiting abstraction model layers in accordance with one or more embodiments of the present invention.
DETAILED DESCRIPTION
The following detailed description is merely illustrative and is not intended to limit embodiments and/or application or uses of embodiments. Furthermore, there is no intention to be bound by any expressed or implied information presented in the preceding Background or Summary sections, or in the Detailed Description section.
One or more embodiments are now described with reference to the drawings, wherein like referenced numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of the one or more embodiments. It is evident, however, in various cases, that the one or more embodiments can be practiced without these specific details.
Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.
Quantum computing employs quantum physics to encode information rather than binary digital techniques based on transistors. For example, a quantum computer is a device that employs quantum mechanical phenomena for computations and/or operations on data. In an aspect, a quantum computer can employ quantum bits (e.g., qubits) that operate according to a superposition principle of quantum physics and an entanglement principle of quantum physics. The superposition principle of quantum physics allows each qubit to represent both a value of â1â and a value of â0â at the same time. The entanglement principle of quantum physics states allows qubits in a superposition to be correlated with each other. For instance, a state of a first value (e.g., a value of â1â or a value of â0â) can depend on a state of a second value. As such, a quantum computer can employ qubits to encode information rather than binary digital techniques based on transistors. Furthermore, a quantum computation by a quantum computer can employ quantum properties to represent and/or structure data. Quantum mechanisms can also be devices and/or built to perform operation with the data. To facilitate a quantum computation by a quantum computer, a quantum program (e.g., code that runs on a quantum computer) can be compiled into one or more quantum pulses to run in a quantum computer. A quantum pulse can encode data for interpretation by a quantum computer. A quantum pulse can also be provided as input to a quantum computer. However, quality of a quantum pulse provided to a quantum computer can directly impact execution of a quantum computer. Therefore, it is generally desirable to optimize a quantum pulse provided to a quantum computer.
To address these and/or other issues, embodiments described herein include systems, computer-implemented methods, and/or computer program products for facilitating quantum pulse optimization using machine learning. For example, a quantum pulse optimizer can be employed to improve inputs transmitted to a quantum computer. In an embodiment, one or more machine learning techniques can be employed to optimize a pulse generator that provides quantum pulses to a quantum computer. As such, quantum pulses can be optimized before being transmitted to a quantum computer. Performance of a quantum computer can also be maximized by optimizing quantum pulses for the quantum computer. In an aspect, the one or more machine learning techniques associated with the quantum pulse optimizer can predict an optimal pulse for a quantum program to run a quantum computer based on previous executions and/or knowledge associated with previous quantum programs. In certain embodiments, the one or more machine learning techniques associated with the quantum pulse optimizer can employ one or more classification techniques to detect patterns related to a quantum pulse and/or a quantum program. Additionally or alternatively, the one or more machine learning techniques associated with the quantum pulse optimizer can predict an optimal arrangement of quantum pulses for a quantum computer. Additionally or alternatively, the one or more machine learning techniques associated with the quantum pulse optimizer can employ reinforcement learning to provide repeated improvement of quality of quantum pulses for future optimizations of the quantum pulses. As such, a quantum pulse provided to a quantum computer can be improved and/or optimized. Furthermore, performance of a quantum computer can be improved. For instance, execution of a quantum program by a quantum computer can be improved. Moreover, processing performance of a quantum computer can be improved, processing efficiency of a quantum computer can be improved, processing characteristics a quantum computer can be improved, timing characteristics of a quantum computer can be improved and/or power efficiency of a quantum computer can be improved.
As disclosed herein, a classical processor (e.g., a classical computer, a classical circuit, etc.) can be a machine that processes data based on binary digits and/or transistors. Furthermore, a quantum processor (e.g., a quantum computer, a quantum circuit, etc.) as disclosed herein can be a machine that processes data based on quantum bits and/or quantum mechanical phenomena associated with superposition and/or entanglement.
FIG. 1 illustrates an example, non-limiting system 100 for facilitating quantum pulse optimization using machine learning in accordance with one or more embodiments described herein. In various embodiments, the system 100 can be a quantum computing system associated with technologies such as, but not limited to, quantum pulse technologies, quantum pulse generator technologies, quantum computing technologies, quantum programming technologies, quantum computer technologies, quantum chip technologies, quantum circuit technologies, quantum processor technologies, quantum device technologies, quantum simulation technologies, artificial intelligence technologies, machine learning technologies, network technologies, and/or other digital technologies. The system 100 can employ hardware and/or software to solve problems that are highly technical in nature, that are not abstract and that cannot be performed as a set of mental acts by a human. Further, in certain embodiments, some of the processes performed may be performed by one or more specialized computers (e.g., a qu
BACKGROUND
The subject disclosure relates to quantum computing and, more specifically, to optimizing quantum pulses provided to a quantum computer.
SUMMARY
The following presents a summary to provide a basic understanding of one or more embodiments of the invention. This summary is not intended to identify key or critical elements, or delineate any scope of the particular embodiments or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description that is presented later. In one or more embodiments described herein, devices, systems, computer-implemented methods, apparatus and/or computer program products for facilitating quantum pulse optimization using machine learning are described.
According to an embodiment, a system can comprise a classical processor and a quantum processor. The classical processor can employ 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 can execute a quantum computing process based on the quantum pulse.
According to another embodiment, a computer-implemented method is provided. The computer-implemented method can comprise optimizing, by a system operatively coupled to a processor, a quantum pulse based on a machine learning technique associated with one or more quantum computing processes to generate an optimized quantum pulse. The computer-implemented method can also comprise transmitting, by the system, the optimized quantum pulse to a quantum processor.
According to yet another embodiment, a computer program product for facilitating quantum pulse optimization using machine learning can comprise a computer readable storage medium having program instructions embodied therewith. The program instructions can be executable by a processor and cause the processor to optimize, by the processor, a quantum pulse based on a machine learning technique and historical data associated with one or more quantum computing processes to generate an optimized quantum pulse. The program instructions can also cause the processor to transmit, by the processor, the optimized quantum pulse to a quantum processor.
DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an example, non-limiting system that facilitates quantum pulse optimization using machine learning in accordance with one or more embodiments described herein.
FIG. 2 illustrates a block diagram of an example, non-limiting system that includes a quantum pulse optimizer in accordance with one or more embodiments described herein.
FIG. 3 illustrates another example, non-limiting system that facilitates quantum pulse optimization using machine learning in accordance with one or more embodiments described herein.
FIG. 4 illustrates an example, non-limiting system that includes a machine learning process for facilitating quantum pulse optimization in accordance with one or more embodiments described herein.
FIG. 5 illustrates an example, non-limiting system that includes quantum processor that employs an optimized quantum pulse in accordance with one or more embodiments described herein.
FIG. 6 illustrates a flow diagram of an example, non-limiting computer-implemented method for facilitating quantum pulse optimization using machine learning in accordance with one or more embodiments described herein.
FIG. 7 illustrates a flow diagram of another example, non-limiting computer-implemented method for facilitating quantum pulse optimization using machine learning in accordance with one or more embodiments described herein.
FIG. 8 illustrates a block diagram of an example, non-limiting operating environment in which one or more embodiments described herein can be facilitated.
FIG. 9 illustrates a block diagram of an example, non-limiting cloud computing environment in accordance with one or more embodiments of the present invention.
FIG. 10 illustrates a block diagram of example, non-limiting abstraction model layers in accordance with one or more embodiments of the present invention.
DETAILED DESCRIPTION
The following detailed description is merely illustrative and is not intended to limit embodiments and/or application or uses of embodiments. Furthermore, there is no intention to be bound by any expressed or implied information presented in the preceding Background or Summary sections, or in the Detailed Description section.
One or more embodiments are now described with reference to the drawings, wherein like referenced numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of the one or more embodiments. It is evident, however, in various cases, that the one or more embodiments can be practiced without these specific details.
Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.
Quantum computing employs quantum physics to encode information rather than binary digital techniques based on transistors. For example, a quantum computer is a device that employs quantum mechanical phenomena for computations and/or operations on data. In an aspect, a quantum computer can employ quantum bits (e.g., qubits) that operate according to a superposition principle of quantum physics and an entanglement principle of quantum physics. The superposition principle of quantum physics allows each qubit to represent both a value of â1â and a value of â0â at the same time. The entanglement principle of quantum physics states allows qubits in a superposition to be correlated with each other. For instance, a state of a first value (e.g., a value of â1â or a value of â0â) can depend on a state of a second value. As such, a quantum computer can employ qubits to encode information rather than binary digital techniques based on transistors. Furthermore, a quantum computation by a quantum computer can employ quantum properties to represent and/or structure data. Quantum mechanisms can also be devices and/or built to perform operation with the data. To facilitate a quantum computation by a quantum computer, a quantum program (e.g., code that runs on a quantum computer) can be compiled into one or more quantum pulses to run in a quantum computer. A quantum pulse can encode data for interpretation by a quantum computer. A quantum pulse can also be provided as input to a quantum computer. However, quality of a quantum pulse provided to a quantum computer can directly impact execution of a quantum computer. Therefore, it is generally desirable to optimize a quantum pulse provided to a quantum computer.
To address these and/or other issues, embodiments described herein include systems, computer-implemented methods, and/or computer program products for facilitating quantum pulse optimization using machine learning. For example, a quantum pulse optimizer can be employed to improve inputs transmitted to a quantum computer. In an embodiment, one or more machine learning techniques can be employed to optimize a pulse generator that provides quantum pulses to a quantum computer. As such, quantum pulses can be optimized before being transmitted to a quantum computer. Performance of a quantum computer can also be maximized by optimizing quantum pulses for the quantum computer. In an aspect, the one or more machine learning techniques associated with the quantum pulse optimizer can predict an optimal pulse for a quantum program to run a quantum computer based on previous executions and/or knowledge associated with previous quantum programs. In certain embodiments, the one or more machine learning techniques associated with the quantum pulse optimizer can employ one or more classification techniques to detect patterns related to a quantum pulse and/or a quantum program. Additionally or alternatively, the one or more machine learning techniques associated with the quantum pulse optimizer can predict an optimal arrangement of quantum pulses for a quantum computer. Additionally or alternatively, the one or more machine learning techniques associated with the quantum pulse optimizer can employ reinforcement learning to provide repeated improvement of quality of quantum pulses for future optimizations of the quantum pulses. As such, a quantum pulse provided to a quantum computer can be improved and/or optimized. Furthermore, performance of a quantum computer can be improved. For instance, execution of a quantum program by a quantum computer can be improved. Moreover, processing performance of a quantum computer can be improved, processing efficiency of a quantum computer can be improved, processing characteristics a quantum computer can be improved, timing characteristics of a quantum computer can be improved and/or power efficiency of a quantum computer can be improved.
As disclosed herein, a classical processor (e.g., a classical computer, a classical circuit, etc.) can be a machine that processes data based on binary digits and/or transistors. Furthermore, a quantum processor (e.g., a quantum computer, a quantum circuit, etc.) as disclosed herein can be a machine that processes data based on quantum bits and/or quantum mechanical phenomena associated with superposition and/or entanglement.
FIG. 1 illustrates an example, non-limiting system 100 for facilitating quantum pulse optimization using machine learning in accordance with one or more embodiments described herein. In various embodiments, the system 100 can be a quantum computing system associated with technologies such as, but not limited to, quantum pulse technologies, quantum pulse generator technologies, quantum computing technologies, quantum programming technologies, quantum computer technologies, quantum chip technologies, quantum circuit technologies, quantum processor technologies, quantum device technologies, quantum simulation technologies, artificial intelligence technologies, machine learning technologies, network technologies, and/or other digital technologies. The system 100 can employ hardware and/or software to solve problems that are highly technical in nature, that are not abstract and that cannot be performed as a set of mental acts by a human. Further, in certain embodiments, some of the processes performed may be performed by one or more specialized computers (e.g., a quantum computer, one or more specialized processing units, a specialized computer with a quantum pulse optimizer, etc.) for carrying out defined tasks related to quantum computing and/or optimizing a quantum pulse. The system 100 and/or components of the system 100 can be employed to solve new problems that arise through advancements in technologies mentioned above, computer architecture, and/or the like. One or more embodiments of the system 100 can provide technical improvements to quantum pulse systems, quantum pulse generator systems, quantum computing systems, quantum programming systems, quantum computer systems, quantum chip systems, quantum circuit systems, quantum processor systems, quantum device systems, quantum simulation systems, artificial intelligence systems, machine learning systems, network systems, and/or other digital systems. One or more embodiments of the system 100 can also provide technical improvements to a quantum device (e.g., a quantum circuit, a quantum processor, a quantum computer, etc.) by improving processing performance of the quantum device, improving processing efficiency of the quantum device, improving processing characteristics of the quantum device, improving timing characteristics of the quantum device and/or improving power efficiency of the quantum device.
In the embodiment shown in FIG. 1 , the system 100 can include a classical processor 102 and a quantum processor 104 . Furthermore, the classical processor 102 can include a quantum pulse optimizer 106 . The classical processor 102 can be communicatively coupled to the quantum processor 104 . For example, the quantum pulse optimizer 106 of the classical processor 102 can be communicatively coupled to the quantum processor 104 . The classical processor 102 can be a machine that performs a set of calculations based on binary digits and/or transistors. In an aspect, the classical processor 102 can employ one or more classical computation techniques via the quantum pulse optimizer 106 to facilitate optimization of a quantum pulse 108 provided to the quantum processor 104 . The quantum processor 104 can be a machine that performs a set of calculations based on principle of quantum physics. For instance, the quantum processor 104 can perform one or more quantum computations associated with a set of quantum gates. Furthermore, the quantum processor 104 can encode information using qubits. In certain embodiments, the quantum processor 104 can perform one or more quantum computations associated with a sequence of quantum gates. In one embodiment, the quantum processor 104 can be a hardware quantum processor (e.g., a hardware superconducting quantum processor) that can encode and/or process information using qubits. For instance, the quantum processor 104 can be a hardware quantum processor that executes a set of instruction threads associated with qubits. In another embodiment, the quantum processor 104 can be associated with a quantum simulator that can simulate execution of a set of processing threads on the quantum processor 104 . In certain embodiments, the quantum processor 104 can perform one or more quantum computations based on a machine-readable description of the quantum processor 104 . For instance, the machine-readable description of the quantum processor 104 can textually describe one or more qubit gates of the quantum processor 104 associated with one or more qubits. In an aspect, the quantum processor 104 can perform one or more quantum computations based on the quantum pulse 108 . For example, the quantum processor 104 can execute a quantum program associated with the quantum pulse 108 . The quantum pulse 108 can be a microwave pulse that controls one or more quantum gates and/or one or more qubits of the quantum processor 104 . For example, the quantum pulse 108 can be an electromagnetic pulse transmitted at a microwave frequency. The quantum pulse 108 can comprise, for example, a particular frequency and/or a particular phase to facilitate determination of an angle of rotation of a qubit state associated with the quantum processor 104 . In certain embodiments, the classical processor 102 (e.g., the quantum pulse optimizer 106 of the classical processor 102 ) can convert a quantum program into the quantum pulse 108 . For example, the classical processor 102 (e.g., the quantum pulse optimizer 106 of the classical processor 102 ) can convert quantum assembly language data into the quantum pulse 108 .
In an embodiment, the quantum pulse optimizer 106 of the classical processor 102 can employ one or more machine learning techniques to optimize the quantum pulse 108 provided to the quantum processor 104 . In an aspect, the quantum pulse optimizer 106 can employ machine learning and/or principles of artificial intelligence (e.g., one or more machine learning processes) to detect one or more patterns related to a quantum pulse and/or a quantum program associated with a quantum pulse. For example, the quantum pulse optimizer 106 can employ machine learning and/or principles of artificial intelligence (e.g., one or more machine learning processes) to detect one or more patterns related to the quantum pulse 108 . The quantum pulse optimizer 106 can additionally or alternatively employ machine learning and/or principles of artificial intelligence (e.g., one or more machine learning processes) to detect one or more patterns related to a quantum program associated with the quantum pulse 108 . Additionally or alternatively, the quantum pulse optimizer 106 can employ machine learning and/or principles of artificial intelligence (e.g., one or more machine learning processes) to detect one or more patterns related to one or more quantum pulses generated prior to the quantum pulse 108 . Additionally or alternatively, the quantum pulse optimizer 106 can employ machine learning and/or principles of artificial intelligence (e.g., one or more machine learning processes) to detect one or more patterns related to one or more quantum programs associated with one or more quantum pulses generated prior to the quantum pulse 108 . In certain embodiments, the quantum pulse optimizer 106 can employ machine learning and/or principles of artificial intelligence (e.g., one or more machine learning processes) to predict an optimal arrangement of quantum pulses for the quantum processor 104 . For example, in certain embodiments, the quantum pulse 108 can be two or more quantum pulses transmitted to the quantum processor 104 in an optimal manner based on machine learning and/or principles of artificial intelligence (e.g., one or more machine learning processes) associated with the quantum pulse optimizer 106 .
In another embodiment, the quantum pulse optimizer 106 can employ machine learning and/or principles of artificial intelligence (e.g., one or more machine learning processes) to generate the quantum pulse 108 . The quantum pulse optimizer 106 can perform learning explicitly or implicitly with respect to learning one or more patterns, one or more inferences, one or more correlations, one or more features and/or information related to one or more quantum pulses and/or one or more quantum programs. In an aspect, the quantum pulse optimizer 106 can learn one or more patterns, one or more inferences, one or more correlations, one or more features and/or information related to one or more quantum pulses and/or one or more quantum programs based on classifications, correlations, inferences and/or expressions associated with principles of artificial intelligence. For instance, the quantum pulse optimizer 106 can employ an automatic classification system and/or an automatic classification process to learn one or more patterns, one or more inferences, one or more correlations, one or more features and/or information related to one or more quantum pulses and/or one or more quantum programs. In one example, the quantum pulse optimizer 106 can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to learn and/or generate inferences with respect to one or more quantum pulses and/or one or more quantum programs. In an aspect, the quantum pulse optimizer 106 can include an inference component (not shown) that can further enhance automated aspects of the quantum pulse optimizer 106 utilizing in part inference-based schemes to learn one or more patterns, one or more inferences, one or more correlations, one or more features and/or information related to one or more quantum pulses and/or one or more quantum programs.
The quantum pulse optimizer 106 can employ any suitable machine-learning based techniques, statistical-based techniques and/or probabilistic-based techniques. For example, the quantum pulse optimizer 106 can employ deep learning, expert systems, fuzzy logic, SVMs, Hidden Markov Models (HMMs), greedy search algorithms, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other non-linear training techniques, data fusion, utility-based analytical systems, systems employing Bayesian models, etc. In another aspect, the quantum pulse optimizer 106 can perform a set of machine learning computations associated with learning one or more patterns, one or more inferences, one or more correlations, one or more features and/or information related to one or more quantum pulses and/or one or more quantum programs. For example, the quantum pulse optimizer 106 can perform a set of clustering machine learning computations, a set of logistic regression machine learning computations, a set of decision tree machine learning computations, a set of random forest machine learning computations, a set of regression tree machine learning computations, a set of least square machine learning computations, a set of instance-based machine learning computations, a set of regression machine learning computations, a set of support vector regression machine learning computations, a set of k-means machine learning computations, a set of spectral clustering machine learning computations, a set of rule learning machine learning computations, a set of Bayesian machine learning computations, a set of deep Boltzmann machine computations, a set of deep belief network computations, and/or a set of different machine learning computations to learn one or more patterns, one or more inferences, one or more correlations, one or more features and/or information related to one or more quantum pulses and/or one or more quantum programs.
It is to be appreciated that the quantum pulse optimizer 106 performs a quantum computing optimization process that cannot be performed by a human (e.g., is greater than the capability of a single human mind). For example, an amount of data processed, a speed of data processed and/or data types of data processed by the quantum pulse optimizer 106 over a certain period of time can be greater, faster and different than an amount, a speed and data types that can be processed by a single human mind over the same period of time. The quantum pulse optimizer 106 can also be fully operational towards performing one or more other functions (e.g., fully powered on, fully executed, etc.) while also performing the above-referenced quantum computing process and/or quantum post-process process. Additionally, the quantum pulse 108 generated by the quantum pulse optimizer 106 can include information that is impossible to obtain manually by a user. For example, a type of information included in the quantum pulse 108 , a variety of information included in the quantum pulse 108 , and/or an amount of information included in the quantum pulse 108 can be more complex than information obtained manually by a user. Moreover, it is to be appreciated that the quantum processor 104 performs a quantum computing process that cannot be performed by a human (e.g., is greater than the capability of a single human mind). For example, an amount of data processed, a speed of data processed and/or a type of data processed by the quantum processor 104 over a certain period of time can be greater, faster and different than an amount, a speed and data types that can be processed by a single human mind over the same period of time. Additionally, it is to be appreciated that the system 100 can provide various advantages as compared to conventional quantum computing systems. For instance, quality of the quantum pulse 108 can be improved by employing the system 100 . Performance, efficiency and/or efficacy of the quantum processor 104 can also be improved by employing the system 100 . Furthermore, accuracy of a quantum computing process, efficiency of a quantum computing process, efficacy of a quantum computing process, an amount of time to perform a quantum computing process, an amount of processing performed by a quantum computing process, and/or an amount of storage utilized by a quantum computing process can be reduced by employing the system 100 .
FIG. 2 illustrates a block diagram of an example, non-limiting system 200 in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity.
In the embodiment shown in FIG. 2 , the system 200 can include the quantum pulse optimizer 106 . As shown in FIG. 2 , in an embodiment, the quantum pulse optimizer 106 can include a machine learning component 204 and a pulse generator component 206 . Aspects of the quantum pulse optimizer 106 can constitute machine-executable component(s) embodied within machine(s), e.g., embodied in one or more computer readable mediums (or media) associated with one or more machines. Such component(s), when executed by the one or more machines, e.g., computer(s), computing device(s), virtual machine(s), etc. can cause the machine(s) to perform the operations described. In an aspect, the quantum pulse optimizer 106 can also include memory 208 that stores computer executable components and instructions. Furthermore, in certain embodiments, the quantum pulse optimizer 106 can include a processor 210 to facilitate execution of the instructions (e.g., computer executable components and corresponding instructions) by the quantum pulse optimizer 106 . As shown, the machine learning component 204 , the pulse generator component 206 , the memory 208 and/or the processor 210 can be electrically and/or communicatively coupled to one another in one or more embodiments.
The machine learning component 204 can perform one or more machine learning techniques to facilitate optimization of the quantum pulse 108 generated by the pulse generator component 206 . In an embodiment, the machine learning component 204 can perform one or more machine learning techniques based on historical data associated with one or more quantum computing processes (e.g., one or more previously performed quantum computing processes). For example, the historical data can include information related to one or more previously generated quantum pulses provided to the quantum processor 104 . Additionally or alternatively, the historical data can include information related to one or more quantum programs previously executed by the quantum processor 104 . In an aspect, the machine learning component 204 can detect one or more patterns in the one or more previously generated quantum pulses provided to the quantum processor 104 . Additionally or alternatively, the machine learning component 204 can detect one or more patterns in the one or more quantum programs previously executed by the quantum processor 104 .
In certain embodiments, the machine learning component 204 can compare a pattern for a quantum program to one or more previously generated quantum programs. In response to a determination by the machine learning component 204 that the quantum program matches a previously generated quantum program from the one or more previously generated quantum programs, the pulse generator component 206 can employ at least a portion of a compilation associated with the previously generated quantum program for the quantum pulse 108 . For example, in response to a determination by the machine learning component 204 that the quantum program matches a previously generated quantum program from the one or more previously generated quantum programs, the pulse generator component 206 can employ at least a portion of a previous quantum pulse for the quantum pulse 108 . In another example, in response to a determination by the machine learning component 204 that the quantum program matches a previously generated quantum program from the one or more previously generated quantum programs, the pulse generator component 206 can modify one or more portions of the quantum program based on results of the machine learning. In certain embodiments, the machine learning component 204 can perform two or more different machine learning techniques to facilitate generation of the quantum pulse 108 by the pulse generator component 206 . The machine learning component 204 can also rank results from the two or more different machine learning techniques to facilitate generation of the quantum pulse 108 by the pulse generator component 206 . In certain embodiments, the machine learning component 204 can determine an arrangement of quantum pulses generated by the pulse generator component 206 . For example, the machine learning component 204 can determine an order of transmission of the quantum pulse 108 with respect to one or more other quantum pulses generated by the pulse generator component 206 . The pulse generator component 206 can generate one or more quantum pulses such as, for example, the quantum pulse 108 . Furthermore, the pulse generator component 206 can transmit, for example, one or more quantum pulses to the quantum processor 104 . In an example, the pulse generator component 206 can transmit the quantum pulse 108 to the quantum processor 104 .
In certain embodiments, the machine learning component 204 can employ one or more machine learning techniques to optimize the quantum pulse 108 generated by the pulse generator component 206 . In an aspect, the machine learning component 204 can employ machine learning and/or principles of artificial intelligence (e.g., one or more machine learning processes) to detect one or more patterns related to a quantum pulse and/or a quantum program associated with a quantum pulse. For example, the machine learning component 204 can employ machine learning and/or principles of artificial intellige
CLAIMS
Claims ( 20 )
What is claimed is:
1. A system, comprising:
a classical processor that employs a quantum pulse optimizer to generate a first quantum pulse for execution of a quantum program by employing one or more machine learning techniques based on historical data related to at least one second quantum pulse previously used in at least one previous execution of the quantum program, and further based on learned data associated with one or more quantum computing processes; and
a quantum processor that executes the quantum program based on the first quantum pulse received from the classical processor, wherein:
the historical data comprises data related to historical qubit values of the quantum processor, and data related to one or more inputs provided to the quantum processor for the one or more quantum computing processes;
the learned data is generated based on knowledge related to the historical data;
the quantum pulse optimizer optimizes the first quantum pulse according to at least one of a performance requirement for improving operations of the quantum processor including at least one of accuracy, efficiency, or efficacy of the quantum program, an amount of time to perform one or more quantum computations, an amount of processing performed by the one or more quantum computations, or an amount of storage utilized by the one or more quantum computations; and
wherein employing the first quantum pulse causes the amount of time required to perform the one or more quantum computations by the quantum processor to fall below a first defined threshold.
2. The system of claim 1 , wherein the classical processor employs the quantum pulse optimizer to generate the first quantum pulse based on the learned data generated by the one or more machine learning techniques.
3. The system of claim 1 , wherein the first quantum pulse is a microwave pulse that controls one or more quantum gates of the quantum processor.
4. The system of claim 1 , wherein the quantum pulse optimizer generates the first quantum pulse based on one or more patterns associated with the one or more quantum computing processes.
5. The system of claim 1 , wherein the quantum pulse optimizer generates the first quantum pulse based on one or more patterns related to the quantum program.
6. The system of claim 1 , wherein the quantum pulse optimizer generates the first quantum pulse based on an arrangement of quantum pulses associated with the one or more quantum computing processes.
7. The system of claim 1 , wherein employing the first quantum pulse further causes accuracy of the one or more quantum computations performed by the quantum processor to fall above a second defined threshold.
8. A computer-implemented method, comprising:
generating, by a system operatively coupled to a processor, a first quantum pulse for execution of a quantum program, wherein the generating comprises employing, one or more machine learning techniques based on historical data related to at least one second quantum pulse previously used in at least one previous execution of the quantum program, and further based on learned data associated with one or more quantum computing processes; and
transmitting, by the system, the first quantum pulse to a quantum processor, wherein:
the historical data comprises data related to historical qubit values of the quantum processor, and data related to one or more inputs provided to the quantum processor for the one or more quantum computing processes;
the learned data is generated based on knowledge related to the historical data;
the first quantum pulse is optimized according to at least one of a performance requirement for improving operations of the quantum processor including at least one of accuracy, efficiency, or efficacy of the quantum program, an amount of time to perform one or more quantum computations, an amount of processing performed by the one or more quantum computations, or an amount of storage utilized by the one or more quantum computations; and
the quantum processor employs the first quantum pulse to execute the quantum program such that the amount of time required to perform the one or more quantum computations by the quantum processor falls below a first defined threshold.
9. The computer-implemented method of claim 8 , wherein the generating further comprises generating the first quantum pulse based on one or more patterns associated with the one or more quantum computing processes.
10. The computer-implemented method of claim 8 , wherein the generating further comprises generating the first quantum pulse based on one or more patterns related to the quantum program.
11. The computer-implemented method of claim 8 , wherein the generating further comprises generating the first quantum pulse based on an arrangement of quantum pulses associated with the one or more quantum computing processes.
12. The computer-implemented method of claim 8 , wherein employing the first quantum pulse further causes accuracy of the one or more quantum computations performed by one the quantum processor to fall above a second defined threshold.
13. The computer-implemented method of claim 8 , wherein the first quantum pulse is a microwave pulse that controls one or more quantum gates of the quantum processor.
14. The computer-implemented method of claim 8 , wherein the first quantum pulse is generated using a quantum pulse optimizer.
15. A computer program product for facilitating quantum pulse optimization using machine learning, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
generate, by the processor, a first quantum pulse for execution of a quantum program by employing one or more machine learning techniques based on historical data related to at least one second quantum pulse previously used in at least one previous execution of the quantum program, and further based on learned data associated with one or more quantum computing processes; and
transmit, by the processor, the first quantum pulse to a quantum processor, wherein:
the historical data comprises data related to historical qubit values of the quantum processor, and data related to one or more inputs provided to the quantum processor for the one or more quantum computing processes;
the learned data is generated based on knowledge related to the historical data;
the first quantum pulse is optimized according to at least one of a performance requirement for improving operations of the quantum processor including at least one of accuracy, efficiency, or efficacy of the quantum program, an amount of time to perform one or more quantum computations, an amount of processing performed by the one or more quantum computations, or an amount of storage utilized by the one or more quantum computations; and
the quantum processor employs the first quantum pulse to execute the quantum program such that the amount of time required to perform the one or more quantum computations by the quantum processor falls below a first defined threshold.
16. The computer program product of claim 15 , wherein the program instructions are further executable by the processor to cause the processor to:
generate, by the processor, the first quantum pulse based on one or more patterns associated with the one or more quantum computing processes.
17. The computer program product of claim 15 , wherein the program instructions are further executable by the processor to cause the processor to:
generate, by the processor, the first quantum pulse based on an arrangement of quantum pulses associated with the one or more quantum computing processes.
18. The computer program product of claim 15 , wherein the program instructions are further executable by the processor to cause the processor to:
generate, by the processor, the first quantum pulse based on one or more patterns associated with the quantum program related to the first quantum pulse.
19. The computer program product of claim 15 , wherein the first quantum pulse is a microwave pulse that controls one or more quantum gates of the quantum processor.
20. The computer program product of claim 15 , wherein the first quantum pulse is generated using a quantum pulse optimizer.
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Cited By (2)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US20220129411A1
( en )
*
2020-10-28
2022-04-28
International Business Machines Corporation
Partitioned template matching and symbolic peephole optimization
US20240070513A1
( en )
*
2021-07-28
2024-02-29
Origin Quantum Computing Technology (Hefei) Co., Ltd
Method for determining crosstalk of quantum bits, quantum control system, and quantum computer
Families Citing this family (28)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US10333503B1
( en )
2018-11-26
2019-06-25
Quantum Machines
Quantum controller with modular and dynamic pulse generation and routing
US10454459B1
( en )
2019-01-14
2019-10-22
Quantum Machines
Quantum controller with multiple pulse modes
US10505524B1
( en )
2019-03-06
2019-12-10
Quantum Machines
Synchronization in a quantum controller with modular and dynamic pulse generation and routing
US11164100B2
( en )
2019-05-02
2021-11-02
Quantum Machines
Modular and dynamic digital control in a quantum controller
US10931267B1
( en )
2019-07-31
2021-02-23
Quantum Machines
Frequency generation in a quantum controller
US12579462B1
( en )
2019-08-14
2026-03-17
Q.M Technologies Ltd.
Controlling a quantum processor via quantum programming field payloads
US10862465B1
( en )
2019-09-02
2020-12-08
Quantum Machines
Quantum controller architecture
US11245390B2
( en )
2019-09-02
2022-02-08
Quantum Machines
Software-defined pulse orchestration platform
US12431879B2
( en )
2019-09-25
2025-09-30
Q.M Technologies Ltd.
Classical processor for quantum control
US11507873B1
( en )
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Quantum Machines
Highly scalable quantum control
US11126926B1
( en )
2020-03-09
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Quantum Machines
Concurrent results processing in a quantum control system
US11043939B1
( en )
2020-08-05
2021-06-22
Quantum Machines
Frequency management for quantum control
US11790260B2
( en )
*
2021-01-28
2023-10-17
Red Hat, Inc.
Quantum process termination
US12450509B2
( en )
*
2021-02-24
2025-10-21
Red Hat, Inc.
Access protection for shared qubits
US12132486B2
( en )
*
2021-04-08
2024-10-29
Quantum Machines
System and method for pulse generation during quantum operations
US11671180B2
( en )
2021-04-28
2023-06-06
Quantum Machines
System and method for communication between quantum controller modules
US12242406B2
( en )
2021-05-10
2025-03-04
Q.M Technologies Ltd.
System and method for processing between a plurality of quantum controllers
US11915325B2
( en )
*
2021-06-09
2024-02-27
Bank Of America Corporation
Quantum enabled resource activity investigation and response tool
US12165011B2
( en )
2021-06-19
2024-12-10
Q.M Technologies Ltd.
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( en )
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2025-10-21
United Services Automobile Association (Usaa)
Property analysis using sensors
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( en )
2021-07-21
2025-06-17
Q.M Technologies Ltd.
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( en )
2022-01-24
2024-10-08
Quantum Machines
Machine learning for syncing multiple FPGA ports in a quantum system
US12314815B2
( en )
2022-02-28
2025-05-27
Q.M Technologies Ltd.
Auto-calibrating mixers in a quantum orchestration platform
CN114757225B
( en )
*
2022-03-31
2023-05-30
å京ç¾åº¦ç½è®¯ç§ææéå ¬å¸
Method, device, equipment and storage medium for determining signal sampling quality
US12493810B2
( en )
2022-05-09
2025-12-09
Q.M Technologies Ltd.
Pulse generation in a quantum device operator
US12488275B1
( en )
2022-05-10
2025-12-02
Q.M Technologies Ltd.
Buffering the control of a quantum device
US12450513B2
( en )
*
2022-05-31
2025-10-21
Q.M Technologies Ltd.
Quantum controller validation
US12549161B2
( en )
2023-11-29
2026-02-10
Q.M Technologies Ltd.
High resolution, direct synthesis of qubit control signals
Citations (21)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US20110173696A1
( en )
*
2010-01-08
2011-07-14
Kabushiki Kaisha Toshiba
Quantum communication system and method
US20130215421A1
( en )
*
2012-02-16
2013-08-22
Richard E. Stoner
Methods and apparatus for controlled generation of hyperfine polarizations and coherences
US20180062838A1
( en )
*
2015-03-10
2018-03-01
The University Of Bristol
Chip-based quantum key distribution
CN108415206A
( en )
2018-03-21
2018-08-17
èå·å¤§å¦
The light pulse generation method of the arbitrary superposition state of three-lever system quantum bit can be created
US20180260732A1
( en )
*
2017-03-10
2018-09-13
Rigetti & Co, Inc.
Performing a Calibration Process in a Quantum Computing System
CN109165744A
( en )
2018-08-15
2019-01-08
åè¥æ¬æºéå计ç®ç§ææéè´£ä»»å ¬å¸
A method for optimizing the operation of quantum logic gates
US10176433B2
( en )
*
2016-05-13
2019-01-08
Microsoft Technology Licensing, Llc
Training a quantum optimizer
US20190042973A1
( en )
*
2018-09-27
2019-02-07
Xiang Zou
Apparatus and method for arbitrary qubit rotation
US10223643B1
( en )
*
2017-09-29
2019-03-05
International Business Machines Corporation
Reduction and/or mitigation of crosstalk in quantum bit gates
US20190095811A1
( en )
*
2017-09-22
2019-03-28
International Business Machines Corporation
Hardware-efficient variational quantum eigenvalue solver for quantum computing machines
US10483980B2
( en )
*
2017-06-19
2019-11-19
Rigetti & Co, Inc.
Parametrically activated quantum logic gates
US10622978B1
( en )
*
2019-04-05
2020-04-14
IonQ, Inc.
Quantum logic gate design and optimization
US20200125402A1
( en )
*
2018-10-22
2020-04-23
Red Hat, Inc.
Scheduling services for quantum computing
US10797684B1
( en )
*
2019-05-09
2020-10-06
Government Of The United States Of America, As Represented By The Secretary Of Commerce
Superconducting waveform synthesizer
US20200369517A1
( en )
*
2019-05-22
2020-11-26
IonQ, Inc.
Amplitude, frequency, and phase modulated entangling gates for trapped-ion quantum computers
US20210279631A1
( en )
*
2018-08-31
2021-09-09
President And Fellows Of Harvard College
Quantum computing for combinatorial optimization problems using programmable atom arrays
US20210334081A1
( en )
*
2018-09-13
2021-10-28
University Of Chicago
System and method of optimizing instructions for quantum computers
US20220084085A1
( en )
*
2018-10-03
2022-03-17
Rigetti & Co, Inc.
Parcelled Quantum Resources
US20220164693A1
( en )
*
2018-08-09
2022-05-26
Rigetti & Co, Inc.
Quantum Streaming Kernel
US20220321616A1
( en )
*
2021-04-06
2022-10-06
Avaya Management L.P.
Intelligent screen and resource sharing during a meeting
US20220358391A1
( en )
*
2021-05-07
2022-11-10
International Business Machines Corporation
Backend quantum runtimes
2019
2019-07-01
US
US16/458,586
patent/US11748648B2/en
active
Active
Patent Citations (23)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US20110173696A1
( en )
*
2010-01-08
2011-07-14
Kabushiki Kaisha Toshiba
Quantum communication system and method
US20130215421A1
( en )
*
2012-02-16
2013-08-22
Richard E. Stoner
Methods and apparatus for controlled generation of hyperfine polarizations and coherences
US20180062838A1
( en )
*
2015-03-10
2018-03-01
The University Of Bristol
Chip-based quantum key distribution
US10176433B2
( en )
*
2016-05-13
2019-01-08
Microsoft Technology Licensing, Llc
Training a quantum optimizer
US20180260732A1
( en )
*
2017-03-10
2018-09-13
Rigetti & Co, Inc.
Performing a Calibration Process in a Quantum Computing System
US10483980B2
( en )
*
2017-06-19
2019-11-19
Rigetti & Co, Inc.
Parametrically activated quantum logic gates
US20190095811A1
( en )
*
2017-09-22
2019-03-28
International Business Machines Corporation
Hardware-efficient variational quantum eigenvalue solver for quantum computing machines
US10332023B2
( en )
*
2017-09-22
2019-06-25
International Business Machines Corporation
Hardware-efficient variational quantum eigenvalue solver for quantum computing machines
US10223643B1
( en )
*
2017-09-29
2019-03-05
International Business Machines Corporation
Reduction and/or mitigation of crosstalk in quantum bit gates
CN108415206A
( en )
2018-03-21
2018-08-17
èå·å¤§å¦
The light pulse generation method of the arbitrary superposition state of three-lever system quantum bit can be created
US20220164693A1
( en )
*
2018-08-09
2022-05-26
Rigetti & Co, Inc.
Quantum Streaming Kernel
CN109165744A
( en )
2018-08-15
2019-01-08
åè¥æ¬æºéå计ç®ç§ææéè´£ä»»å ¬å¸
A method for optimizing the operation of quantum logic gates
US20210279631A1
( en )
*
2018-08-31
2021-09-09
President And Fellows Of Harvard College
Quantum computing for combinatorial optimization problems using programmable atom arrays
US20210334081A1
( en )
*
2018-09-13
2021-10-28
University Of Chicago
System and method of optimizing instructions for quantum computers
US20190042973A1
( en )
*
2018-09-27
2019-02-07
Xiang Zou
Apparatus and method for arbitrary qubit rotation
US20220084085A1
( en )
*
2018-10-03
2022-03-17
Rigetti & Co, Inc.
Parcelled Quantum Resources
US20200125402A1
( en )
*
2018-10-22
2020-04-23
Red Hat, Inc.
Scheduling services for quantum computing
US11086665B2
( en )
*
2018-10-22
2021-08-10
Red Hat, Inc.
Scheduling services for quantum computing
US10622978B1
( en )
*
2019-04-05
2020-04-14
IonQ, Inc.
Quantum logic gate design and optimization
US10797684B1
( en )
*
2019-05-09
2020-10-06
Government Of The United States Of America, As Represented By The Secretary Of Commerce
Superconducting waveform synthesizer
US20200369517A1
( en )
*
2019-05-22
2020-11-26
IonQ, Inc.
Amplitude, frequency, and phase modulated entangling gates for trapped-ion quantum computers
US20220321616A1
( en )
*
2021-04-06
2022-10-06
Avaya Management L.P.
Intelligent screen and resource sharing during a meeting
US20220358391A1
( en )
*
2021-05-07
2022-11-10
International Business Machines Corporation
Backend quantum runtimes
Non-Patent Citations (3)
* Cited by examiner, â Cited by third party
Title
Liu, et al. " Integrating machine learning to achieve an automatic parameter prediction for practical continuous-variable quantum key distribution ", Physical Review A 97(2), 022316 DOI: 10.1103/PhysRevA.97.022316 (2018).
Mel, et al., " The NIST Definition of Cloud Computing, " Special Publication 800-145, Sep. 2011, 7 pages.
Palittapongarnpim, et al. " Learning in quantum control: High-dimensional global optimization for noisy quantum dynamics ", Neurocomputing 268 pp. 116-126 (2017).
Cited By (3)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US20220129411A1
( en )
*
2020-10-28
2022-04-28
International Business Machines Corporation
Partitioned template matching and symbolic peephole optimization
US11983605B2
( en )
*
2020-10-28
2024-05-14
International Business Machines Corporation
Partitioned template matching and symbolic peephole optimization
US20240070513A1
( en )
*
2021-07-28
2024-02-29
Origin Quantum Computing Technology (Hefei) Co., Ltd
Method for determining crosstalk of quantum bits, quantum control system, and quantum computer
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