QuSplit: Achieving Both High Fidelity and Throughput via Job Splitting on Noisy Quantum Computers

• URL: http://arxiv.org/abs/2501.12492v2
• Date: Tue, 11 Mar 2025 22:08:05 GMT
• Title: QuSplit: Achieving Both High Fidelity and Throughput via Job Splitting on Noisy Quantum Computers
• Authors: Jinyang Li, Yuhong Song, Yipei Liu, Jianli Pan, Lei Yang, Travis Humble, Weiwen Jiang,
• Abstract summary: We develop a Genetic Algorithm-based scheduling framework that incorporates job splitting to optimize fidelity and throughput.<n> Experimental results demonstrate that our approach consistently maintains high fidelity across all jobs while significantly enhancing system throughput.
• Score: 6.46676684248918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the progression into the quantum utility era, computing is shifting toward quantum-centric architectures, where multiple quantum processors collaborate with classical computing resources. Platforms such as IBM Quantum and Amazon Braket exemplify this trend, enabling access to diverse quantum backends. However, efficient resource management remains a challenge, as quantum processors are highly susceptible to noise, which significantly impacts computation fidelity. Additionally, the heterogeneous noise characteristics across different processors add further complexity to scheduling and resource allocation. Existing scheduling strategies typically focus on mapping and scheduling jobs to these heterogeneous backends, which leads to some jobs suffering extremely low fidelity. Targeting quantum optimization jobs (e.g., VQC, VQE, QAOA) - among the most promising quantum applications in the NISQ era - we hypothesize that executing the later stages of a job on a high-fidelity quantum processor can significantly improve overall fidelity. To verify this, we use VQE as a case study and develop a Genetic Algorithm-based scheduling framework that incorporates job splitting to optimize fidelity and throughput. Experimental results demonstrate that our approach consistently maintains high fidelity across all jobs while significantly enhancing system throughput. Furthermore, the proposed algorithm exhibits excellent scalability in handling an increasing number of quantum processors and larger workloads, making it a robust and practical solution for emerging quantum computing platforms. To further substantiate its effectiveness, we conduct experiments on a real quantum processor, IBM Strasbourg, which confirm that job splitting improves fidelity and reduces the number of iterations required for convergence.

Related papers

• Q-Fusion: Diffusing Quantum Circuits [2.348041867134616]
  We propose a diffusion-based algorithm leveraging the LayerDAG framework to generate new quantum circuits. Our results demonstrate that the proposed model consistently generates 100% valid quantum circuit outputs.
  arXiv  Detail & Related papers  (2025-04-29T14:10:10Z)
• Enhancing variational quantum algorithms by balancing training on classical and quantum hardware [1.8377902806196762]
  Variational quantum algorithms (VQAs) have the potential to provide a near-term route to quantum utility or advantage. VQAs have been proposed for a multitude of tasks such as ground-state estimation. There remain major challenges in its trainability and resource costs on quantum hardware.
  arXiv  Detail & Related papers  (2025-03-20T17:17:58Z)
• Noise-Aware Distributed Quantum Approximate Optimization Algorithm on Near-term Quantum Hardware [2.753858051267023]
  This paper introduces a noise-aware distributed Quantum Approximate Optimization Algorithm (QAOA) tailored for execution on near-term quantum hardware. We address the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, which are hindered by limited qubit counts and high error rates.
  arXiv  Detail & Related papers  (2024-07-24T14:50:01Z)
• Variational Quantum Algorithms for Combinatorial Optimization [0.571097144710995]
  Variational Algorithms (VQA) have emerged as one of the strongest candidates towards reaching practical applicability of NISQ systems. This paper explores the current state and recent developments of VQAs, emphasizing their applicability to Approximate optimization. We implement QAOA circuits with varying depths to solve the MaxCut problem on graphs with 10 and 20 nodes.
  arXiv  Detail & Related papers  (2024-07-08T22:02:39Z)
• Unlocking Quantum Optimization: A Use Case Study on NISQ Systems [0.0]
  This paper considers two industrial relevant use cases: one in the realm of optimizing charging schedules for electric vehicles, the other concerned with the optimization of truck routes. Our central contribution are systematic series of examples derived from these uses cases that we execute on different processors of the gate-based quantum computers of IBM as well as on the quantum annealer of D-Wave.
  arXiv  Detail & Related papers  (2024-04-10T17:08:07Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation [80.78352800340032]
  Quantum computing networks execute large-scale generative AI computation tasks and advanced quantum algorithms. efficient resource allocation in quantum computing networks is a critical challenge due to qubit variability and network complexity. We introduce state-of-the-art reinforcement learning (RL) algorithms, from generative learning to quantum machine learning for optimal quantum resource allocation.
  arXiv  Detail & Related papers  (2024-01-13T17:16:38Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Preparing random state for quantum financing with quantum walks [1.2074552857379273]
  We propose an efficient approach to load classical data into quantum states that can be executed by quantum computers or quantum simulators on classical hardware. A practical example of implementing SSQW using Qiskit has been released as open-source software. Showing its potential as a promising method for generating desired probability amplitude distributions highlights the potential application of SSQW in option pricing through quantum simulation.
  arXiv  Detail & Related papers  (2023-02-24T08:01:35Z)
• Iterative Qubits Management for Quantum Index Searching in a Hybrid System [56.39703478198019]
  IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
  arXiv  Detail & Related papers  (2022-09-22T21:54:28Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Quantum circuit architecture search on a superconducting processor [56.04169357427682]
  Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry. However, the ansatz exploited in modern VQAs is incapable of balancing the tradeoff between expressivity and trainability. We demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique to enhance VQAs on an 8-qubit superconducting quantum processor.
  arXiv  Detail & Related papers  (2022-01-04T01:53:42Z)
• Error mitigation in variational quantum eigensolvers using tailored probabilistic machine learning [5.630204194930539]
  We present a novel method that employs parametric Gaussian process regression (GPR) within an active learning framework to mitigate noise in quantum computations. We demonstrate the effectiveness of our method on a 2-site Anderson impurity model and a 8-site Heisenberg model, using the IBM open-source quantum computing framework, Qiskit.
  arXiv  Detail & Related papers  (2021-11-16T22:29:43Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• SQUARE: Strategic Quantum Ancilla Reuse for Modular Quantum Programs via Cost-Effective Uncomputation [7.92565122267857]
  We present a compilation infrastructure that tackles allocation and reclamation of scratch qubits (called ancilla) in quantum programs. At its core, SQUARE strategically performs uncomputation to create opportunities for qubit reuse. Our results show that SQUARE improves the average success rate of NISQ applications by 1.47X.
  arXiv  Detail & Related papers  (2020-04-18T06:34:37Z)

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.