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On Reducing the Execution Latency of Superconducting Quantum Processors via Quantum Job Scheduling

Wenjie Wu, Yiquan Wang, Ge Yan, Yuming Zhao, Bo Zhang, Junchi Yan

TL;DR

This work addresses long queue delays in public superconducting quantum processors by formulating the Quantum Job Scheduling Problem (QJSP) and introducing a noise-aware scheduler (NAQJS). The method combines queue rearranging, qubit partitioning, and qubit mapping, using a priority score $S_p^{(i)}$ and an EPST$^*$-based fidelity assessment to balance latency and fidelity. Empirical results on both a Guadalupe noise model and the Xiaohong quantum processor show that NAQJS markedly reduces QPU time and turnaround time while maintaining fairness, with only modest fidelity trade-offs. The approach demonstrates strong potential to enable quantum multi-programming and lower-access barriers in the NISQ era, with scalability to larger qubit counts and applicability to non-superconducting platforms in future work.

Abstract

Quantum computing has gained considerable attention, especially after the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum processors and cloud services have been made world-wide increasingly available. Unfortunately, jobs on existing quantum processors are often executed in series, and the workload could be heavy to the processor. Typically, one has to wait for hours or even longer to obtain the result of a single quantum job on public quantum cloud due to long queue time. In fact, as the scale grows, the qubit utilization rate of the serial execution mode will further diminish, causing the waste of quantum resources. In this paper, to our best knowledge for the first time, the Quantum Job Scheduling Problem (QJSP) is formulated and introduced, and we accordingly aim to improve the utility efficiency of quantum resources. Specifically, a noise-aware quantum job scheduler (NAQJS) concerning the circuit width, number of measurement shots, and submission time of quantum jobs is proposed to reduce the execution latency. We conduct extensive experiments on a simulated Qiskit noise model, as well as on the Xiaohong (from QuantumCTek) superconducting quantum processor. Numerical results show the effectiveness in both the QPU time and turnaround time.

On Reducing the Execution Latency of Superconducting Quantum Processors via Quantum Job Scheduling

TL;DR

This work addresses long queue delays in public superconducting quantum processors by formulating the Quantum Job Scheduling Problem (QJSP) and introducing a noise-aware scheduler (NAQJS). The method combines queue rearranging, qubit partitioning, and qubit mapping, using a priority score and an EPST-based fidelity assessment to balance latency and fidelity. Empirical results on both a Guadalupe noise model and the Xiaohong quantum processor show that NAQJS markedly reduces QPU time and turnaround time while maintaining fairness, with only modest fidelity trade-offs. The approach demonstrates strong potential to enable quantum multi-programming and lower-access barriers in the NISQ era, with scalability to larger qubit counts and applicability to non-superconducting platforms in future work.

Abstract

Quantum computing has gained considerable attention, especially after the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum processors and cloud services have been made world-wide increasingly available. Unfortunately, jobs on existing quantum processors are often executed in series, and the workload could be heavy to the processor. Typically, one has to wait for hours or even longer to obtain the result of a single quantum job on public quantum cloud due to long queue time. In fact, as the scale grows, the qubit utilization rate of the serial execution mode will further diminish, causing the waste of quantum resources. In this paper, to our best knowledge for the first time, the Quantum Job Scheduling Problem (QJSP) is formulated and introduced, and we accordingly aim to improve the utility efficiency of quantum resources. Specifically, a noise-aware quantum job scheduler (NAQJS) concerning the circuit width, number of measurement shots, and submission time of quantum jobs is proposed to reduce the execution latency. We conduct extensive experiments on a simulated Qiskit noise model, as well as on the Xiaohong (from QuantumCTek) superconducting quantum processor. Numerical results show the effectiveness in both the QPU time and turnaround time.
Paper Structure (21 sections, 1 theorem, 3 equations, 7 figures, 2 tables, 2 algorithms)

This paper contains 21 sections, 1 theorem, 3 equations, 7 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Preliminaries and Related Works
  3. Methodology
  4. The Quantum Job Scheduling Problem
  5. Definition
  6. Metrics
  7. Proposed Method
  8. Queue Rearranging
  9. Qubit Partitioning
  10. Qubit Mapping
  11. Complexity Analysis
  12. Experiments
  13. Protocols
  14. Results on the Noise Model Guadalupe
  15. Results on Xiaohong Quantum Processor
  16. ...and 6 more sections

Key Result

theorem 1

With our aging strategy, all the quantum jobs can be executed in finite time in QJSP.

Figures (7)

  • Figure 1: (a) Coupling graph of Xiaohong quantum processor (from QuantumCTek as used in this paper for experiments). (b) Coupling graph of IBM Guadalupe. (c) SWAP gate. (d) BRIDGE gate. SWAP and BRIDGE gates can solve the connectivity constraints on coupling graphs.
  • Figure 2: An example of qubit mapping. (a) Subgraph derived by qubit partitioning. (b) Quantum circuit to be mapped. (The CNOT gate in red cannot be applied, because $Q_0$ and $Q_2$ are not connected.) (c) Mapped circuit through SWAP gates. (d) Mapped circuit through BRIDGE gates. The SWAP gate changes the mapping in (c) (marked in blue).
  • Figure 3: A quantum circuit (left) and its directed acyclic graph (right).
  • Figure 4: Overview of our noise-aware quantum job scheduler (NAQJS).
  • Figure 5: Chain-type topology.
  • ...and 2 more figures

Theorems & Definitions (2)

  • theorem 1
  • proof