A Closeness Centrality-based Circuit Partitioner for Quantum Simulations

• URL: http://arxiv.org/abs/2509.14098v2
• Date: Thu, 18 Sep 2025 02:53:34 GMT
• Title: A Closeness Centrality-based Circuit Partitioner for Quantum Simulations
• Authors: Doru Thom Popovici, Harlin Lee, Mauro Del Ben, Naoki Yoshioka, Nobuyasu Ito, Katherine Klymko, Daan Camps, Anastasiia Butko,
• Abstract summary: Simulating quantum circuits (QC) on high-performance computing systems has become an essential method to benchmark algorithms and probe the potential of large-scale quantum computation despite the limitations of current quantum hardware.<n>We introduce an end-to-end framework that provides an efficient partitioning scheme for large-scale QCs alongside a flexible code generator to offer a portable solution that minimizes data movement between compute nodes.
• Score: 1.8202149041424323
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Simulating quantum circuits (QC) on high-performance computing (HPC) systems has become an essential method to benchmark algorithms and probe the potential of large-scale quantum computation despite the limitations of current quantum hardware. However, these simulations often require large amounts of resources, necessitating the use of large clusters with thousands of compute nodes and large memory footprints. In this work, we introduce an end-to-end framework that provides an efficient partitioning scheme for large-scale QCs alongside a flexible code generator to offer a portable solution that minimizes data movement between compute nodes. By formulating the distribution of quantum states and circuits as a graph problem, we apply closeness centrality to assess gate importance and design a fast, scalable partitioning method. The resulting partitions are compiled into highly optimized codes that run seamlessly on a wide range of supercomputers, providing critical insights into the performance and scalability of quantum algorithm simulations.

Related papers

• Efficient Time-Aware Partitioning of Quantum Circuits for Distributed Quantum Computing [10.919776355400282]
  Distributed quantum computing (DQC) interconnects multiple smaller-scale quantum processing units (QPUs) to form a quantum network.<n>To minimize this communication overhead, DQC compilers must strategically partition quantum circuits by mapping logical qubits to distributed physical QPUs.<n>We propose a time-aware algorithm incrementally constructs a low-cost sequence of qubit assignments across successive time steps to minimize overall communication overhead.<n>Our proposed algorithm consistently achieves significantly lower communication costs than static baselines across varying circuit sizes, depths, and network topologies.
  arXiv  Detail & Related papers  (2026-03-04T14:43:10Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
  Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• 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)
• Scaling Up the Quantum Divide and Conquer Algorithm for Combinatorial Optimization [0.8121127831316319]
  We propose a method for constructing quantum circuits which greatly reduces inter-device communication costs. We show that we can construct tractable circuits nearly three times the size of previous QDCA methods while retaining a similar or greater level of quality.
  arXiv  Detail & Related papers  (2024-05-01T20:49:50Z)
• Revisiting the Mapping of Quantum Circuits: Entering the Multi-Core Era [2.465579331213113]
  We introduce the Hungarian Qubit Assignment (HQA) algorithm, a multi-core mapping algorithm designed to optimize qubit assignments to cores with the aim of reducing inter-core communications. Our evaluation of HQA against state-of-the-art circuit mapping algorithms for modular architectures reveals a $4.9times$ and $1.6times$ improvement in terms of execution time and non-local communications.
  arXiv  Detail & Related papers  (2024-03-25T21:31:39Z)
• End-to-end resource analysis for quantum interior point methods and portfolio optimization [63.4863637315163]
  We provide a complete quantum circuit-level description of the algorithm from problem input to problem output. We report the number of logical qubits and the quantity/depth of non-Clifford T-gates needed to run the algorithm.
  arXiv  Detail & Related papers  (2022-11-22T18:54:48Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• 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)
• Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
  arXiv  Detail & Related papers  (2022-04-18T21:45:13Z)
• Divide and Conquer for Combinatorial Optimization and Distributed Quantum Computation [3.8221353389253676]
  We introduce the Quantum Divide and Conquer Algorithm (QDCA), a hybrid variational approach to mapping large optimization problems onto distributed quantum architectures. This is achieved through the combined use of graph partitioning and quantum circuit cutting. We simulate the QDCA on instances of the Maximum Independent Set problem and find that it is able to outperform similar classical algorithms.
  arXiv  Detail & Related papers  (2021-07-15T18:00:32Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)

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.