Resource management mechanism for distributed multi-quantum equipment
By virtualizing quantum computing resources and implementing a distributed circuit scheduling mechanism, the challenges of resource management in distributed quantum computing are addressed, achieving efficient and stable allocation of quantum computing resources and execution of circuits, thereby improving computational efficiency and fidelity.
Patent Information
- Application Number
- CN202510911740.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-02
- Publication Date
- 2025-11-14
AI Technical Summary
In distributed quantum computing environments, traditional resource management strategies cannot effectively cope with noise, qubit interference, and non-cloning of quantum computing resources, resulting in low computational efficiency and unstable performance. Existing technologies struggle to reasonably allocate tasks and optimize performance among multiple quantum computing units.
By employing quantum computing resource virtualization and distributed line scheduling mechanisms, and managing the state of QPU devices through graph data structures, combined with community discovery algorithms and hot/weak connectivity mapping optimization methods, high-fidelity execution of quantum lines is achieved.
It improves resource utilization and the fidelity of quantum circuit execution in distributed quantum computing environments, thereby enhancing computational efficiency and stability.
Smart Images
Figure CN120952192A_ABST
Abstract
Description
Technical fields:
[0001] This invention relates to the field of quantum computing, and more specifically to a resource management mechanism for distributed multi-quantum devices. Background technology:
[0002] With the continuous development of quantum computing technology, quantum computers are gradually demonstrating potential speedup advantages in some classical computational problems, such as large integer prime factorization and unordered data search. Superconducting quantum computers are among the most promising quantum computers today. They utilize the zero-resistance property of superconducting materials at extremely low temperatures to realize qubits (such as Transmon qubits) through structures like Josephson junctions, exhibiting long coherence times and high manipulation precision. The operation of superconducting quantum computers relies on a near-absolute-zero cryogenic environment, typically requiring the use of dilution refrigerators for cooling, and the manipulation and readout of quantum states via microwave pulses.
[0003] Current quantum computers remain medium-scale and noise-sensitive, and the number of qubits that a single superconducting quantum computer can integrate is still limited, insufficient to support large-scale quantum computing. Distributed quantum computing technology is considered a feasible way to achieve large-scale quantum computing. It overcomes the resource limitations of a single quantum computer by distributing quantum computing tasks across multiple quantum processing units (QPUs) to achieve collaborative computing across multiple physical locations. This technology relies on the principles of quantum communication and quantum entanglement, using the long-range entanglement between qubits to coordinate cooperation between different quantum computers, improving the scale and computing power of quantum computing, and providing a new solution to the scale bottleneck of traditional quantum computers. Distributed quantum computing often relies on quantum circuit cutting algorithms. Quantum circuit cutting introduces a large amount of communication between quantum nodes. In a distributed quantum computing environment, communication between multiple quantum processing units is more error-prone and time-consuming than communication between data qubits within a quantum processing unit (QPU).
[0004] In summary, quantum computing resources are often expensive. Therefore, effective management of quantum computing resources in a distributed multi-quantum device environment is an important method to reduce the number of node communications and improve the efficiency of distributed computing. However, resource management in a distributed quantum computing environment differs significantly from the management of traditional computing resources (such as GPUs, FPGAs, TPUs, etc.). Traditional computing resources are generally more stable and do not experience errors due to noise during computation, unlike quantum computing resources. This means that traditional computing resources can be optimized using standard scheduling and load balancing strategies, while quantum processing units (QPUs) face more complex challenges. QPUs have limited computing power and are inherently noisy; even QPUs with the same architecture and model may have different levels of noise and computational performance, making it difficult to rationally allocate tasks and optimize performance among multiple QPUs. Furthermore, qubits on QPUs interfere with each other, preventing efficiency improvements through parallel computation like with traditional computing resources, as multiple quantum programs may influence each other, leading to performance degradation. Simultaneously, according to the quantum no-cloning theorem, quantum programs cannot be copied or saved during execution, rendering traditional scheduling mechanisms such as resource preemption, program migration, and resource sharing inapplicable. Therefore, resource management in distributed quantum computing needs to consider special factors such as noise, qubit interference, and no-cloning, and adopt novel virtualization and management methods to ensure the efficiency and stability of computing. Summary of the Invention:
[0005] To this end, this invention proposes a resource management mechanism for distributed multi-quantum devices, used to manage multiple QPU devices in a distributed quantum computing environment, and to allocate resources and divide tasks based on the real-time status of the devices, so as to improve the utilization rate of quantum devices and the fidelity of quantum circuit execution. This invention consists of two parts: (1) a quantum computing resource virtualization mechanism, and (2) a circuit scheduling mechanism in a distributed quantum computing environment. The specific contents of each part are as follows:
[0006] (I) Virtualization Mechanism of Quantum Computing Resources
[0007] Step 1: Quantum Processor (QPU) Virtualization Mechanism. Figure 1This paper presents a topology diagram of the IBM London quantum processor. This invention uses a graph data structure to virtualize the quantum processor. Each graph data structure represents a quantum processor. A vertex in the graph data structure includes the following attributes: physical qubit index, physical qubit type (communication qubit or data qubit; logical qubits can only be mapped to data qubits), physical qubit readout error rate, physical qubit connectivity, whether the physical qubit is used, and the quantum task index occupying that physical qubit. Edges in the graph data structure represent connections between physical qubits. The weight of an edge represents the error rate of performing a CNOT gate operation between two physical qubits. This invention updates the graph data structure based on the real-time state of the QPU, including computing power and noise level, to manage quantum processor resources. The physical qubit readout error rate and the error rate of connections between physical qubits can serve as evaluation criteria for building robust qubit sets.
[0008] Step 2: Global virtualization mechanism for distributed quantum computing resources. Figure 2 A distributed quantum computing (DQC) model is presented. In a distributed quantum computing environment, not every two QPUs can perform remote gate operations. This invention uses a graph data structure to represent the connections between QPUs. The nodes in the graph data structure are the graph data structure of the virtualized QPUs in step 1, with each node representing each QPU in the distributed quantum computing environment. The edges in the graph represent the connections between QPUs. If there is an edge between different QPUs, it means that two QPUs can be connected through EPR pairs and perform remote gate operations.
[0009] (II) Line scheduling mechanism in a distributed multi-quantum device environment
[0010] Step 3: Line scheduling mechanism based on the number of qubits. Figure 3 This invention demonstrates a resource management and circuit scheduling mechanism for distributed multi-quantum devices. Based on the quantum computing resource virtualization mechanism proposed in step 1, when a new quantum circuit needs to be executed, this invention searches for an independent quantum processor with sufficient physical qubits according to the state information of the quantum computing resources in the system and the number of logical bits required for the circuit. If no suitable quantum processor exists in the current system, this invention will divide the quantum circuit into multiple sub-circuits using a circuit segmentation algorithm based on the distributed quantum computing topology established in step 3, and execute them independently on different QPUs. Finally, the execution results of each sub-circuit are reconstructed to obtain the execution result of the overall quantum circuit.
[0011] Step 4: Line Mapping Mechanism in a Distributed Quantum Computing Environment. Based on the line scheduling results in Step 4, when a quantum line is mapped to a single independent QPU, this invention employs a community discovery algorithm to construct and allocate robust qubit regions for that line, ensuring high fidelity during its execution. If the quantum line is divided into multiple sub-lines and mapped to different QPUs using a line segmentation algorithm, this invention uses a hot / weak connectivity mapping optimization method to map qubits frequently involved in two-qubit gate operations to the same QPU, thereby improving the cooperative efficiency between qubits and allocating more robust qubit resources. Simultaneously, this invention maps qubits with weaker connectivity and fewer two-qubit gate operations to different QPUs, thereby reducing the frequency of long-range gate operations and significantly improving the fidelity of line execution in a distributed quantum computing environment.
[0012] This invention provides a resource management mechanism for distributed multi-quantum devices, including: (1) quantum computing resource virtualization, where the topology of independent QPU devices and the connection relationships between QPUs in a distributed quantum computing environment are abstracted into a graph data structure and updated according to the real-time state of the quantum devices. When the actual quantum computing resources in the system are insufficient, a supercomputer will be used to simulate the execution of quantum circuits; (2) a circuit scheduling mechanism in a distributed quantum computing environment, which determines whether a quantum circuit needs to be cut based on the state of the quantum devices in the system and the resource requirements for executing the quantum circuit. When the quantum circuit can be executed on an independent QPU, a community discovery algorithm will be used to allocate a robust qubit region for it. When distributed quantum computing needs to be executed, a heat / weak connectivity mapping optimization algorithm will be used to improve the fidelity of the quantum program. This invention provides a novel solution for managing quantum computing resources in a distributed computing environment, which helps to improve the utilization rate of quantum computing resources and improve the fidelity of quantum circuit execution. Attached image description:
[0013] Figure 1 This is a topology diagram of the IBM London quantum processor architecture.
[0014] Figure 2 This is a schematic diagram of a distributed quantum computing model.
[0015] Figure 3 This is a schematic diagram of a resource management and line scheduling mechanism for distributed multi-quantum devices.
[0016] Figure 4 This is a flowchart illustrating an embodiment of the resource management and line scheduling method for distributed multi-quantum devices according to the present invention. Specific implementation method:
[0017] To make the above features and effects of the present invention clearer and easier to understand, specific embodiments are described below, and detailed descriptions are provided in conjunction with the accompanying drawings.
[0018] 1. Operating Environment. This invention assumes that each QPU in this embodiment is a quantum processing unit with 3 data qubits and 2 communication qubits and identical topology.
[0019] 2. Specific steps. Figure 3 This is a schematic diagram of a resource management and line scheduling mechanism for distributed multi-quantum devices proposed in this invention. Figure 4 This is a flowchart illustrating one embodiment of the present invention. The present invention mainly includes the following steps:
[0020] Step 1: Quantum Processor (QPU) Virtualization Mechanism. In Figure 4 In the flowchart shown, a single quantum processor is abstracted as a graph data structure for management. Although each QPU has the same topology, its noise level may not be the same. This invention updates the graph data structure in real time according to the state of the QPU to manage multiple quantum processing units in a distributed quantum computing environment.
[0021] Step 2: Global virtualization mechanism for distributed quantum computing resources. Figure 4 As shown in the flowchart, this invention establishes a global topology graph of distributed quantum computing resources. This invention uses a graph data structure to record the connections between QPUs and implements a circuit cutting algorithm based on the topology of the distributed quantum computing system to support the execution of large circuits.
[0022] Step 3: Circuit scheduling mechanism based on the number of qubits. Figure 4 In the flowchart shown, the 4-qubit QFT circuit to be executed exceeds the maximum number of qubits in a single QPU in this embodiment. Therefore, a circuit splitting algorithm is required to execute the split sub-circuits on two QPUs and reconstruct the circuit execution results.
[0023] Step 4: Circuit mapping mechanism in a distributed quantum computing environment. Figure 4 In the flowchart shown, the quantum circuit is executed in a distributed manner using a circuit cutting algorithm. This invention employs a hotness / weak connectivity mapping optimization method to allocate the most robust physical qubit to the q0 logical qubit with the highest connectivity, and to map frequently interacting logical qubits to the same QPU as much as possible, thereby reducing remote gate operations.
Claims
1. A resource management mechanism for distributed multi-quantum devices, characterized in that, include: (1) Quantum computing resource virtualization mechanism: using common graph data structures to abstract the topology of the QPU and the connection relationship between distributed quantum devices. Based on the real-time status update of the quantum devices, when the quantum computing resources are insufficient to execute the quantum circuit, a supercomputer is used for simulation. (2) Circuit scheduling mechanism in a distributed multi-quantum device environment: based on the number of qubits required for the quantum circuit and the available quantum computing resources in the system, the execution of the quantum circuit is scheduled. Based on whether distributed quantum computing needs to be executed, the mapping optimization method of the quantum circuit is determined.
2. The resource management mechanism for distributed multi-quantum devices as described in claim 1, characterized in that, Quantum computing resource virtualization includes: Step 1, Quantum Processor (QPU) Virtualization Mechanism: This includes using a graph data structure to virtualize the quantum processor. The vertex in the graph data structure includes the following attributes: physical qubit index, physical qubit type, physical qubit readout error rate, physical qubit connectivity, whether the physical qubit is being used, and the quantum task index occupying that physical qubit. Edges in the graph data structure represent connections between physical qubits, and the edge weights represent the error rate of CNOT gate operations between two physical qubits. The graph data structure is updated based on the real-time state of the QPU, including computing power and noise level, to manage quantum processor resources. Step 2, Global Virtualization Mechanism for Distributed Quantum Computing Resources: This includes using a graph data structure to represent the connections between QPUs; the nodes in the graph data structure are the graph data structures of the virtualized QPUs in Step 1, with each node representing each QPU in the distributed quantum computing environment; the edges in the graph data structure represent the connections between QPUs. If there are edges between different QPUs, it means that two QPUs can be connected through EPR pairs to perform remote gate operations.
3. The resource management mechanism for distributed multi-quantum devices as described in claim 1, characterized in that, Line scheduling in a distributed multi-quantum device environment includes: Step 3, Line scheduling mechanism based on the number of qubits: When a new quantum line needs to be executed, according to the state information of the quantum computing resources in the system and the number of logical bits required by the line, an independent quantum processor with enough physical qubits is searched. If there is no quantum processor that meets the requirements in the current system, the quantum line is divided into multiple sub-lines according to the distributed quantum computing topology graph and executed independently on different QPUs. Finally, the execution results of each sub-line are reconstructed to obtain the execution result of the overall quantum line. Step 4, Line Mapping Mechanism in Distributed Quantum Computing Environment: Based on the execution result of the overall quantum line, when a quantum line is mapped to a single independent QPU, a community discovery algorithm is used to construct and allocate robust qubit regions for the line to ensure high fidelity during the execution of the quantum line. If the quantum line is divided into multiple sub-lines and mapped to different QPUs through a line segmentation algorithm, a hotness / weak connectivity mapping optimization method is used to map qubits that frequently involve two-qubit gate operations to the same QPU to improve the cooperative efficiency between qubits and allocate more robust qubit resources. At the same time, qubits with weak connectivity or fewer two-qubit gate operations are mapped to different QPUs, thereby reducing the frequency of remote gate operations and significantly improving the fidelity of line execution in the distributed quantum computing environment.