QuMod: Parallel Quantum Job Scheduling on Modular QPUs using Circuit Cutting

• URL: http://arxiv.org/abs/2604.11013v1
• Date: Mon, 13 Apr 2026 05:27:30 GMT
• Title: QuMod: Parallel Quantum Job Scheduling on Modular QPUs using Circuit Cutting
• Abstract summary: Many real-world applications require scaling to realize logical qubits via error correction.<n> hardware vendors are moving beyond single, monolithic QPUs toward modular architectures connected via interconnects.<n>We develop a multi-programmable scheduler for modular quantum systems that jointly considers qubit mapping, parallel circuit execution, measurement synchronization across subcircuits.
• Score: 9.668642748122506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quantum computing community is increasingly positioning quantum processors as accelerators within classical HPC workflows, analogous to GPUs and TPUs. However, many real-world applications require scaling to hundreds or thousands of physical qubits to realize logical qubits via error correction. To reach these scales, hardware vendors employing diverse technologies -- such as trapped ions, photonics, neutral atoms, and superconducting circuits -- are moving beyond single, monolithic QPUs toward modular architectures connected via interconnects. For example, IonQ has proposed photonic links for scaling, while IBM has demonstrated a modular QPU architecture by classically linking two 127-qubit devices. Using dynamic circuits, Bell-pair-based teleportation, and circuit cutting, they have shown how to execute a large quantum circuit that cannot fit on a single QPU. As interest in quantum computing grows, cloud providers must ensure fair and efficient resource allocation for multiple users sharing such modular systems. Classical interconnection of QPUs introduces new scheduling challenges, particularly when multiple jobs execute in parallel. In this work, we develop a multi-programmable scheduler for modular quantum systems that jointly considers qubit mapping, parallel circuit execution, measurement synchronization across subcircuits, and teleportation operations between QPUs using dynamic circuits.

Related papers

• Evaluating Variational Quantum Circuit Architectures for Distributed Quantum Computing [3.1224202646855894]
  We evaluate the architectures, and in particular the entanglement patterns, of variational quantum circuits in a distributed quantum computing setting.<n>We simulate the execution of an eight qubit circuit using four QPUs each with two computational and two communication qubits.<n>We compare the performance of various circuits on a binary classification task where training is executed under ideal and testing under noisy conditions.
  arXiv  Detail & Related papers  (2025-09-15T14:50:30Z)
• Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
  We develop a reinforcement learning-based quantum compiler for a superconducting processor. We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
  arXiv  Detail & Related papers  (2024-06-18T01:49:48Z)
• Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
  We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
  arXiv  Detail & Related papers  (2024-06-05T17:16:07Z)
• Distributed Quantum Computing in Silicon [40.16556091789959]
  We present preliminary demonstrations of some key distributed quantum computing protocols on silicon T centres in isotopically-enriched silicon. We demonstrate the distribution of entanglement between modules and consume it to apply a teleported gate sequence.
  arXiv  Detail & Related papers  (2024-06-03T18:02:49Z)
• Combining quantum processors with real-time classical communication [0.6597195879147557]
  Quantum computers process information with the laws of quantum mechanics.<n>Current quantum hardware is noisy, can only store information for a short time, and is limited to a few quantum bits, i.e., qubits.<n>Here we overcome these limitations with error mitigated dynamic circuits and circuit-cutting to create quantum states requiring a periodic connectivity employing up to 142 qubits.
  arXiv  Detail & Related papers  (2024-02-27T19:00:07Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• A Hybrid Classical-Quantum HPC Workload [0.0]
  A strategy for the orchestration of hybrid classical-quantum workloads on supercomputers featuring quantum devices is proposed. An example application is investigated that offloads parts of computation to a quantum device. The present test bed serves as a basis for more advanced hybrid workloads eventually involving a real quantum device.
  arXiv  Detail & Related papers  (2023-12-08T09:54:51Z)
• TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [48.491303218786044]
  TeD-Q is an open-source software framework for quantum machine learning.<n>It seamlessly integrates classical machine learning libraries with quantum simulators.<n>It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
  arXiv  Detail & Related papers  (2023-01-13T09:35:05Z)
• Cutting Quantum Circuits to Run on Quantum and Classical Platforms [25.18520278107402]
  CutQC is a scalable hybrid computing approach that distributes a large quantum circuit onto quantum (QPU) and classical platforms ( CPU or GPU) for co-processing. It achieves much higher quantum circuit evaluation fidelity than the large NISQ devices achieve in real-system runs.
  arXiv  Detail & Related papers  (2022-05-12T02:09:38Z)
• An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian Simulation [55.41644538483948]
  Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in chip size and error rates. We derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions. The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond $mathcalO(103)$ spins.
  arXiv  Detail & Related papers  (2021-08-06T19:38:03Z)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.