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IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum

QuIDS: A Large-Scale Distributed Framework for Quantum Irregular Dynamics Simulations

Joseph Touzet, Oguz Kaya, Pablo J. Arrighi, Amélia Durbec

Published Jun 3, 2025• 0 citations
Publisher

Abstract

In traditional quantum computing, e.g. in the quantum circuit model, the size of the data structure describing basis elements is well known, because the dimensionality is fixed. General quantum systems, however, exhibit basis elements of variable size, and state spaces having dynamically unbounded, possibly infinite dimensionality, e.g. for quantum Turing machines or quantum field theories. When seeking to simulate them classically, this imposes an irregularity on both the memory representation of basis elements and the sparsity of the quantum transformations they undergo. Moreover, the high dimensionality of these problems often makes them memory intensive, potentially requiring truncation methods during the simulation. One prototypical example of this would be quantum causal graph dynamics (QCGD), which feature superpositions of colored graphs of different shapes and sizes, driven by the application of local quantum transformations. Numerical observations show that their reversible counterparts typically grow in size; understanding how this is affected in the quantum regime is an arduous computational challenge requiring a particular HPC expertise. In this work, we address this challenge by developing a computational framework for a scalable simulation of such general irregular quantum systems in distributed-memory parallel environments. We lay out the computational challenges arising from the nature of such simulations and then propose effective parallelization, load balancing, memory management, and parallel sampling strategies to accelerate them. We report parallel scalability and accuracy results for up to 1548 MPI processes on a parallel cluster using our framework for the QCGD simulation.

DOI
10.1109/ipdpsw66978.2025.00080

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In traditional quantum computing, e.g. in the quantum circuit model, the size of the data structure describing basis elements is well known, because the dimensionality is fixed. General quantum systems, however, exhibit basis elements of variable size, and state spaces having dynamically unbounded, possibly infinite dimensionality, e.g. for quantum Turing machines or quantum field theories. When seeking to simulate them classically, this imposes an irregularity on both the memory representation of basis elements and the sparsity of the quantum transformations they undergo. Moreover, the high dimensionality of these problems often makes them memory intensive, potentially requiring truncation methods during the simulation. One prototypical example of this would be quantum causal graph dynamics (QCGD), which feature superpositions of colored graphs of different shapes and sizes, driven by the application of local quantum transformations. Numerical observations show that their reversible counterparts typically grow in size; understanding how this is affected in the quantum regime is an arduous computational challenge requiring a particular HPC expertise. In this work, we address this challenge by developing a computational framework for a scalable simulation of such general irregular quantum systems in distributed-memory parallel environments. We lay out the computational challenges arising from the nature of such simulations and then propose effective parallelization, load balancing, memory management, and parallel sampling strategies to accelerate them. We report parallel scalability and accuracy results for up to 1548 MPI processes on a parallel cluster using our framework for the QCGD simulation.
Large-scale quantum circuit simulation on high-performance computing (HPC) systems is crucial for developing and verifying quantum algorithms to overcome the limitations of current noisy quantum computers. However, existing simulators face scalability bottlenecks due to memory limits and communication overhead. Many state-of-the-art approaches rely on static circuit partitioning, which makes it difficult to address the exponential growth in problem size as the number of qubits increases. To overcome these challenges, we present ScaleQsim, a highly scalable quantum circuit simulation framework for large-scale HPC systems. ScaleQsim focuses on providing a unified representation of the full qubit state, yet provides scalability through efficient synchronization and communication. ScaleQsim adopts a novel full state vector partitioning strategy that evenly distributes the full state vector across multiple nodes and GPUs. This distributed structure enables efficient parallel gate execution without costly synchronization, and ScaleQsim applies adaptive kernel configuration, which adjusts execution parameters based on GPU resources and task granularity to enhance simulation efficiency. Our evaluation on the Perlmutter supercomputer with up to 512 GPUs demonstrates that ScaleQsim simulates quantum circuits with up to 42 qubits and outperforms state-of-the-art simulators by up to 77.40× across various quantum circuits.
Quantum circuit simulation is crucial for algorithm development and cryptanalysis given the limited qubit counts and high noise in today’s quantum hardware. We present a Qiskit MPI framework that accelerates classical simulation of quantum circuits focusing on Shor’s integer factoring algorithm by distributing workloads across high performance computing (HPC) resources. A novel dynamic shot allocation strategy balances simulation tasks across MPI processes, preventing idle CPUs even when the total number of circuit "shots" (independent runs) is not evenly divisible. This load balancing approach, combined with support for hybrid CPU/GPU execution on each node, achieves near linear speedups (e.g. 7.5× on 8 processes and 14× on 16 processes) in simulating Shor’s algorithm circuits. We validate the correctness of our parallel simulator by showing that distributed execution produces identical quantum output distributions as a serial run, thus maintaining fidelity. All experiments are performed with fixed random seeds and provided in open source form for reproducibility. We also discuss compatibility with the emerging Quantum Message Passing Interface (QMPI), which extends MPI to quantum computing contexts. By enabling fast, scalable simulation of cryptographically relevant algorithms like Shor’s on classical clusters, our framework allows researchers to prototype quantum attacks and assess quantum algorithm performance ahead of large scale quantum hardware. We report extensive experiments, including factoring small composite numbers, that demonstrate significant wall clock speedups without loss of accuracy. These results position our approach as a practical tool for scalable quantum algorithm prototyping and cryptanalysis research in the pre-quantum era.
The Tight-Binding Propagation Method (TBPM) is a robust computational framework for large-scale quantum simulations with linear scaling in memory and computational time. Building upon shared-memory implementations, this study presents an MPI-accelerated batched TBPM algorithm to exploit distributed-memory high-performance computing systems through domain decomposition and cross-node parallel execution. The approach distributes both the Hamiltonian matrix and wave function states across multiple nodes, enabling memory aggregation and simultaneous utilization of computational resources throughout clusters. The maximum tractable system size is determined primarily by the number of available supercomputing nodes. The batched processing strategy transforms the computational characteristics by reducing matrix access frequency and enhancing computational intensity by a factor of 64, shifting the algorithm from memory-bound to compute-bound regimes. Comprehensive benchmarks on graphene and twisted bilayer graphene demonstrate substantial speedup and memory scalability improvements. Super-linear speedup is consistently observed across both systems, attributed to superior cache efficiency when local Hamiltonian partitions fit within the Last Level Cache, confirming the efficiency and broad applicability of this MPIbased distributed architecture for ultra-large-scale tight-binding calculations.
Large-scale simulations across various scientific domains – from molecular dynamics to computational fluid dynamics and climate modeling – are increasingly hampered by computational limitations. Traditional parallel processing approaches, while providing some degree of speedup, often struggle to effectively manage the inherent complexity and scale of these simulations. This paper proposes a novel framework leveraging quantum-inspired algorithms, specifically tensor network contractions, to address this challenge. The core idea is to decompose the simulation into smaller, independent subproblems that mirror the inherent parallelism observed in quantum systems. This decomposition allows for a truly parallel execution, significantly reducing the overall simulation time. We outline the theoretical underpinnings, detail the proposed methodology, and discuss the potential benefits and challenges associated with this approach. The framework is designed to be adaptable to various simulation types and scales, offering a promising avenue for accelerating computationally intensive scientific investigations. The efficiency gains are attributed to the ability to exploit the natural structure of the simulation problem, similar to how quantum algorithms are designed. Mathematical notation demonstrates key concepts and equations used within the framework.
Advances in quantum simulator technology are increasingly required because research on quantum algorithms is becoming more sophisticated and complex. State vector simulation utilizes CPU and memory resources in computing nodes exponentially with respect to the number of qubits; furthermore, in a variational quantum algorithm, the large number of repeated runs by classical optimization is also a heavy load. This problem has been addressed by preparing numerous computing nodes or simulation frameworks that operate effectively. This study aimed to accelerate quantum simulation using two newly proposed methods: to efficiently utilize limited computational resources by adjusting the ratio of the MPI and distributed processing parallelism corresponding to the target problem settings and to slim down the Hamiltonian by considering the effect of accuracy on the calculation result. Ground-state energy calculations of a fermionic model were performed using a variational quantum eigensolver (VQE) on an HPC cluster with up to 1024 FUJITSU Processor A64FX connected to each other by InfiniBand; the processor is also used on supercomputer Fugaku. We achieved 200 times higher speed over VQE simulations and demonstrated 32 qubits ground-state energy calculations in acceptable time. This result indicates that > 30 qubit state vector simulations can be realistically utilized for further research on variational quantum algorithms.
The article introduces Q-ClusterSim, a resource-efficient hybrid quantum-classical framework designed to accelerate clustering operations on large-scale and high-dimensional datasets. The proposed model combines quantum-enhanced k-means and density-based clustering within a unified quantum simulation environment to achieve improved computational efficiency and scalability. By leveraging the parallelism of quantum circuits and the adaptability of classical optimization, Q-ClusterSim significantly reduces execution time while maintaining clustering accuracy. Simulations are conducted using IBM Qiskit and TensorFlow Quantum on benchmark datasets, where the proposed model consistently outperforms classical clustering algorithms in terms of convergence speed, precision, and resource consumption. Results validate the model’s effectiveness in managing complex data structures under limited qubit resources, making it suitable for real-world large data applications. This study highlights the transformative potential of quantum-assisted clustering in reshaping future data analytics and computational intelligence paradigms..