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

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,...

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Published in:2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) pp. 491 - 500
Main Authors: Touzet, Joseph, Kaya, Oguz, Arrighi, Pablo, Durbec, Amelia
Format: Conference Proceeding
Language:English
Published: IEEE 03.06.2025
Subjects:
Computational efficiency
distributed-memory parallelism
irregular quantum simulation
Memory management
Parallel algorithms
quantum causal graph dynamics
Quantum circuit
quantum computing
Quantum simulation
Quantum system
Scalability
Shape
Software libraries
Turing machines
ISSN:2995-066X
Online Access:Get full text
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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.
AbstractList 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.
Author Touzet, Joseph
Arrighi, Pablo
Kaya, Oguz
Durbec, Amelia
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  givenname: Joseph
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  givenname: Oguz
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  givenname: Pablo
  surname: Arrighi
  fullname: Arrighi, Pablo
  email: pablo.arrighi@universite-paris-saclay.fr
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  givenname: Amelia
  surname: Durbec
  fullname: Durbec, Amelia
  email: amelia.durbec@protonmail.com
  organization: ICL, Junia, Université Catholique de Lille, LITL,Lille,France
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Snippet 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...
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StartPage 491
SubjectTerms Computational efficiency
distributed-memory parallelism
irregular quantum simulation
Memory management
Parallel algorithms
quantum causal graph dynamics
Quantum circuit
quantum computing
Quantum simulation
Quantum system
Scalability
Shape
Software libraries
Turing machines
Title QuIDS: A Large-Scale Distributed Framework for Quantum Irregular Dynamics Simulations
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