Abstract Currently, accessing quantum chips through the quantum cloud is the conventional way for most researchers to use quantum computers. In the case of limited quantum chip resources, improving the overall utilization of quantum chips can provide advantages from using quantum computer systems. At present, the main method to improve resource utilization is through multiprogramming on one quantum chip by putting multiple programs on one quantum chip for execution. A scheduling framework of multitasking mapping on a multichip is proposed in this paper. This framework is deployed in the later stage of the quantum computing compiler. Moreover, this framework can unify all candidate chips in the cloud platform into a computing resource pool and automatically allocate computing resources for users, which improves the overall efficiency of quantum computers and further simplifies the steps for users to use quantum computers.
Abstract Currently, accessing quantum chips through the quantum cloud is the conventional way for most researchers to use quantum computers. In the case of limited quantum chip resources, improving the overall utilization of quantum chips can provide advantages from using quantum computer systems. At present, the main method to improve resource utilization is through multiprogramming on one quantum chip by putting multiple programs on one quantum chip for execution. A scheduling framework of multitasking mapping on a multichip is proposed in this paper. This framework is deployed in the later stage of the quantum computing compiler. Moreover, this framework can unify all candidate chips in the cloud platform into a computing resource pool and automatically allocate computing resources for users, which improves the overall efficiency of quantum computers and further simplifies the steps for users to use quantum computers.
For a specific quantum chip, multi-programming helps to improve overall throughput and resource utilization. However, the previous solutions for mapping multiple programs onto a quantum chip often lead to resource under-utilization, high error rate and low fidelity. In this paper, we propose a new approach to map concurrent quantum programs. Our approach has three critical components. The first one is the Community Detection Assisted Partition (CDAP) algorithm, which partitions physical qubits for concurrent quantum programs by considering both physical typology and the error rates, avoiding the waste of robust resources. The second one is the X-SWAP scheme that enables inter-program SWAP operations to reduce the SWAP overheads. Finally, we propose a compilation task scheduling framework, which dynamically selects concurrent quantum programs to be executed based on estimated fidelity, increasing the throughput of the quantum computer. We evaluate our work on publicly available quantum computer IBMQ16 and a simulated quantum chip IBMQ20. Our work outperforms the previous solution on multi-programming in both fidelity and SWAP overheads by 12.0% and 11.1%, respectively.
For a specific quantum chip, multi-programming improves overall throughput and resource utilization. Previous studies on mapping multiple programs often lead to resource under-utilization, high error rate, and low fidelity. This paper proposes QuCloud, a new approach for mapping quantum programs in the cloud environment. We have three new designs in QuCloud. (1) We leverage the community detection technique to partition physical qubits among concurrent quantum programs, avoiding the waste of robust resources. (2) We design X-SWAP scheme that enables inter-program SWAPs and prioritizes SWAPs associated with critical gates to reduce the SWAP overheads. (3) We propose a compilation task scheduler that schedules concurrent quantum programs to be compiled and executed based on estimated fidelity for the best practice. We evaluate our work on publicly available quantum computer IBMQ16 and a simulated quantum chip IBMQ50. Our work outperforms the state-of-the-art work for multi-programming on fidelity and compilation overheads by 9.7% and 11.6%, respectively.
For a specific quantum chip, multi-programming helps to improve the overall throughput and resource utilization. However, previous solutions for mapping multiple programs often lead to resource under-utilization, high error rate, and low fidelity. In this paper, we propose a new approach to map concurrent quantum programs. Our approach has three critical components. The first one is the Community Detection Assisted Partition (CDAP) algorithm, which partitions physical qubits for concurrent quantum programs by considering physical typology and the error rate, avoiding the waste of robust resources. The second one is the X-SWAP scheme that enables inter-program SWAPs and prioritizes SWAPs associated with critical gates to reduce the SWAP overheads. Finally, we propose a compilation task scheduler, which dynamically selects concurrent quantum programs to be compiled and executed together based on estimated fidelity for the best practice. We evaluate our work on publicly available quantum computer IBMQ16 and a simulated quantum chip IBMQ50. Our work outperforms the state-of-the-art work for multi-programming on fidelity and compilation overheads by 12.0% and 11.6%, respectively.
As the leading candidate for quantum error correction, the surface code faces substantial overhead, such as redundant physical qubits and prolonged execution time. Reducing the space-time cost of circuit execution can significantly improve the throughput of modern quantum cloud platforms. While utilizing more physical qubits can reduce execution time, different quantum circuits vary in their ability to leverage chip resources. Therefore, optimizing the compilation of surface code circuits on quantum chips becomes critical. In this work, we address the mapping and scheduling problem in compiling surface code to reduce the cost. First, we introduce a novel metric Circuit Parallelism Degree to characterize circuit properties in detail and select the most suitable chip from a list of available options. Next, we will quantitatively assess the resources to determine if they are sufficient for the circuit. We then propose a resource-adaptive mapping and scheduling method called Ecmas+ , which customizes the initialization of chip resources for each circuit. Ecmas+ significantly reduces execution time in the double defect and lattice surgery models. Extensive numerical tests on practical datasets demonstrate that Ecmas+ outperforms state-of-the-art methods, reducing execution time by an average of 46% for the double defect model and 29.7% for the lattice surgery model.
Quantum computing holds significant potential across various fields such as cybersecurity, finance, and health-care, among many others. However, we currently face multiple limitations such as the low availability of computers, the underutilization of available qubits, and the ever-increasing number of users, which results in prolonged waiting times. This, combined with the cost of executions, limits access to quantum computing. To address these challenges, we propose a scheduling process to optimize task execution on quantum computers. The proposed scheduling process unifies multiple circuits to enhance the efficiency of resource utilization in quantum machines. This approach allows for better use of available qubits and reduces the associated execution costs. We validate the proposed method employing various quantum algorithms by systematically examining the scheduling process through a comparative case study between two quantum service providers: IBM Quantum and Amazon Braket. This validation has shown that the scheduling process does not generate a significant impact on the results, ensuring similar results to single circuit executions with a fraction of the cost.
This paper investigates a novel approach to parallel computing resource scheduling leveraging the principles of quantum state superposition. Traditional parallel computing methods face limitations in efficiently utilizing available resources due to inherent sequential execution constraints. We propose a framework that maps computational tasks onto quantum state superposition, enabling parallel execution through quantum control techniques. Furthermore, we explore the potential of quantum entanglement for optimizing resource allocation and scheduling. This research addresses the core claim of enhancing computational efficiency by exploiting the unique properties of quantum mechanics. The methodology presented offers a fundamentally different paradigm for resource management, potentially unlocking significant performance improvements in computationally intensive applications. This work introduces a new method for parallel computation resource scheduling by utilizing quantum state superposition. The key mechanism involves mapping tasks to superposition states, parallel execution via quantum control, and entanglement-based resource scheduling.