Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2408.09055

Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs (Extended Version)

Mingkuan Xu, Shiyi Cao, Xupeng Miao, Umut A. Acar, Zhihao Jia

Published Aug 17, 2024• 2 citations
arXivHTMLPDF

Abstract

This paper presents techniques for theoretically and practically efficient and scalable Schrödinger-style quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates the subcircuits on multi-node GPUs, exploiting available data parallelism while minimizing communication costs. To minimize communication costs, we formulate an Integer Linear Program that rewards simulation of "nearby" gates on "nearby" GPUs. To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU. We realize these techniques in Atlas, a distributed, multi-GPU quantum circuit simulator. Our evaluation on a variety of quantum circuits shows that Atlas outperforms state-of-the-art GPU-based simulators by more than 2$\times$ on average and is able to run larger circuits via offloading to DRAM, outperforming other large-circuit simulators by two orders of magnitude.

DOI
10.48550/arxiv.2408.09055

Related papers

Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs

Mingkuan Xu, Shiyi Cao, Xupeng Miao +2 more

This paper presents techniques for theoretically and practically efficient and scalable Schrödinger-style quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates the subcircuits on multinode GPUs, exploiting available data parallelism while minimizing communication costs. To minimize communication costs, we formulate an Integer Linear Program that rewards simulation of “nearby” gates on “nearby” GPUs. To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU. We realize these techniques in Atlas, a distributed, multi-GPU quantum circuit simulator. Our evaluation on a variety of quantum circuits shows that Atlas outperforms state-of-the-art GPU-based simulators by more than $2 \times$ on average and is able to run larger circuits via offloading to DRAM, outperforming other large-circuit simulators by two orders of magnitude.

International Conference for High Performance Computing, Networking, Storage and Analysis (SC)•Nov 17, 2024•9 citations

Optimization and Simulation of Quantum Circuits

Mingkuan Xu

Optimizing and simulating quantum circuits at scale are critical bottlenecks in realizing the full potential of quantum computing. This thesis delivers a comprehensive suite of systems to overcome these limitations, spanning both physical compilation and classical simulation. For quantum circuit optimization, we first automate the discovery and verification of transformation rules by introducing Equivalence Circuit Class (ECC) sets and an efficient generation algorithm for arbitrary gate sets. We deploy these rules within the superoptimizer Quartz, which utilizes a cost-based backtracking search to escape local minima that trap traditional rule-based methods. To eliminate the exponential runtime penalties inherent to pure search, we introduce QALM, a hybrid optimization framework. By unifying search-based exploration with greedy rule-based exploitation in an interleaved control loop, QALM dynamically escapes local minima without exhaustive backtracking. Evaluated across a large benchmark suite of 248 diverse circuits, QALM outperforms existing search-based optimizers in final circuit quality while operating with the computational efficiency of rule-based systems. While the prior two approaches aim for global optimization, this problem is intrinsically QMA-hard, creating a bottleneck for large programs. To circumvent this issue and scale up, we introduce OAC, a cut-and-meld circuit optimization algorithm. OAC cuts a circuit into subcircuits, applies an existing oracle optimizer independently, and seamlessly melds the results. This algorithm operates with a linear number of oracle calls while attaining local optimality. Empirical evaluation shows that OAC significantly improves the efficiency of state-of-the-art optimizers while enhancing overall quality. Beyond physical execution, the scalable simulation of quantum circuits on classical hardware presents another major challenge. To address this, we present Atlas, a distributed GPU-based simulator that hierarchically partitions circuits to exploit data parallelism while minimizing communication overhead. By leveraging integer linear programming to allocate structurally related gates to nearby GPUs and dynamic programming for kernel scheduling, Atlas runs significantly faster than prior state-of-the-art GPU simulators. Together, these frameworks provide a robust toolchain, improving both the execution of quantum circuits on physical devices and their scalable classical simulation.

•Jul 29, 2026•0 citations

Communication-Partition Co-Optimization for Quantum Circuit Simulation on CPU+GPU Clusters

Chenyang Jiao, Weihua Zhang, Li Shen

Simulating the behavior of quantum circuits on classical computers is one of the widely used approaches for current quantum computing device and quantum algorithm research. State- vector simulators keep all the quantum states in main memory, thus consuming a large amount of memory and resulting in significant memory access and communication overhead far longer than calculation time. Consequently, data communication time often becomes the primary bottleneck in quantum circuit simulation. Many classical simulators, such as QuEST, are designed to execute quantum gates serially without exploiting data locality, and all amplitudes need to be exchanged. Such a gate-unaware full-data communication scheme forces powerful computation such as GPUs to spend substantial time waiting for data transfers. In this paper, we propose a gate-aware on-demand communication quantum simulation framework to optimize communication overhead. We analyze the on-demand communication process and develop a communication cost model to elucidate the relationship between communication costs and circuit partition. Since greedy circuit partition method can only yield locally optimal solutions, we propose a choice-start-node quantum circuit partition method guided by the communication cost model to further reduce communication overhead in quantum circuit simulation. We evaluate on a 4-node cluster with 2 NVIDIA A100 GPUs on each node and our design achieves 59.32× speedup compared to the gate-unaware full-data communication scheme. Furthermore, compared to the greedy circuit partition method, our partition method can achieve simulation acceleration of 1.79×.

IEEE Transactions on Parallel and Distributed Systems•Mar 30, 2026•0 citations

ScaleQsim: Highly Scalable Quantum Circuit Simulation Framework for Exascale HPC Systems

Changjong Kim, Ehan Sohn, Seunghwan Kim +5 more

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.

Proceedings of the ACM on Measurement and Analysis of Computing Systems•Dec 1, 2025•1 citation

Circuit Partitioning and Full Circuit Execution: A Comparative Study of GPU-Based Quantum Circuit Simulation

Kartikey Sarode, Daniel Huang, E. Wes Bethel

Executing large quantum circuits is not feasible using the currently available NISQ (noisy intermediate-scale quantum) devices. The high costs of using real quantum devices make it further challenging to research and develop quantum algorithms. As a result, performing classical simulations is usually the preferred method for researching and validating large-scale quantum algorithms. However, these simulations require a huge amount of resources, as each additional qubit exponentially increases the computational space required. Distributed Quantum Computing (DQC) is a promising alternative to reduce the resources required for simulating large quantum algorithms at the cost of increased runtime. This study presents a comparative analysis of two simulation methods: circuit-splitting and full-circuit execution using distributed memory, each having a different type of overhead. The first method, using CutQC, cuts the circuit into smaller subcircuits and allows us to simulate a large quantum circuit on smaller machines. The second method, using Qiskit-Aer-GPU, distributes the computational space across a distributed memory system to simulate the entire quantum circuit. Results indicate that full-circuit executions are faster than circuit-splitting for simulations performed on a single node. However, circuit-splitting simulations show promising results in specific scenarios as the number of qubits is scaled.

arXiv:2502.11385•Feb 17, 2025•0 citations

Distributed Quantum Computing via Adaptive Circuit Knitting

K. Grace Johnson, Aniello Esposito, Gaurav Gyawali +5 more

Distributing quantum workloads over many Quantum Processing Units (QPUs) is a crucial step in scaling up quantum computers toward practical quantum advantage due to the limitations in size of a single QPU. In the absence of high-fidelity quantum interconnects, circuit knitting could provide a path to computing certain properties of large quantum systems on many QPUs of limited size in a distributed fashion using only classical communication. Circuit knitting partitions large quantum circuits into manageable sub-circuits, however, reconstructing observables in a straightforward manner comes at an exponential cost in sampling and classical post-processing. To mitigate the overhead this technique incurs, we introduce an Adaptive Circuit Knitting (ACK) method that finds efficient partitions of quantum circuits by discovering regions of minimal entanglement between subsystems. We simulate 1D and 2D disordered mixed-field Ising models up to 60 qubits and show that the ACK approach can reduce circuit knitting sampling overheads by up to four orders of magnitude for observables of interest. We highlight our parallel GPU-accelerated implementation and discuss the need for efficient classical simulators to enable distributed quantum algorithm development. Our techniques could enable efficient distribution of quantum simulation for both near-term and fault-tolerant architectures.

quant-pharXiv:2603.12411•Mar 16, 2026•0 citations
This paper presents techniques for theoretically and practically efficient and scalable Schrödinger-style quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates the subcircuits on multi-node GPUs, exploiting available data parallelism while minimizing communication costs. To minimize communication costs, we formulate an Integer Linear Program that rewards simulation of "nearby" gates on "nearby" GPUs. To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU. We realize these techniques in Atlas, a distributed, multi-GPU quantum circuit simulator. Our evaluation on a variety of quantum circuits shows that Atlas outperforms state-of-the-art GPU-based simulators by more than 2×\times× on average and is able to run larger circuits via offloading to DRAM, outperforming other large-circuit simulators by two orders of magnitude.
This paper presents techniques for theoretically and practically efficient and scalable Schrödinger-style quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates the subcircuits on multinode GPUs, exploiting available data parallelism while minimizing communication costs. To minimize communication costs, we formulate an Integer Linear Program that rewards simulation of “nearby” gates on “nearby” GPUs. To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU. We realize these techniques in Atlas, a distributed, multi-GPU quantum circuit simulator. Our evaluation on a variety of quantum circuits shows that Atlas outperforms state-of-the-art GPU-based simulators by more than 2×2 \times2× on average and is able to run larger circuits via offloading to DRAM, outperforming other large-circuit simulators by two orders of magnitude.
Optimizing and simulating quantum circuits at scale are critical bottlenecks in realizing the full potential of quantum computing. This thesis delivers a comprehensive suite of systems to overcome these limitations, spanning both physical compilation and classical simulation. For quantum circuit optimization, we first automate the discovery and verification of transformation rules by introducing Equivalence Circuit Class (ECC) sets and an efficient generation algorithm for arbitrary gate sets. We deploy these rules within the superoptimizer Quartz, which utilizes a cost-based backtracking search to escape local minima that trap traditional rule-based methods. To eliminate the exponential runtime penalties inherent to pure search, we introduce QALM, a hybrid optimization framework. By unifying search-based exploration with greedy rule-based exploitation in an interleaved control loop, QALM dynamically escapes local minima without exhaustive backtracking. Evaluated across a large benchmark suite of 248 diverse circuits, QALM outperforms existing search-based optimizers in final circuit quality while operating with the computational efficiency of rule-based systems. While the prior two approaches aim for global optimization, this problem is intrinsically QMA-hard, creating a bottleneck for large programs. To circumvent this issue and scale up, we introduce OAC, a cut-and-meld circuit optimization algorithm. OAC cuts a circuit into subcircuits, applies an existing oracle optimizer independently, and seamlessly melds the results. This algorithm operates with a linear number of oracle calls while attaining local optimality. Empirical evaluation shows that OAC significantly improves the efficiency of state-of-the-art optimizers while enhancing overall quality. Beyond physical execution, the scalable simulation of quantum circuits on classical hardware presents another major challenge. To address this, we present Atlas, a distributed GPU-based simulator that hierarchically partitions circuits to exploit data parallelism while minimizing communication overhead. By leveraging integer linear programming to allocate structurally related gates to nearby GPUs and dynamic programming for kernel scheduling, Atlas runs significantly faster than prior state-of-the-art GPU simulators. Together, these frameworks provide a robust toolchain, improving both the execution of quantum circuits on physical devices and their scalable classical simulation.
Simulating the behavior of quantum circuits on classical computers is one of the widely used approaches for current quantum computing device and quantum algorithm research. State- vector simulators keep all the quantum states in main memory, thus consuming a large amount of memory and resulting in significant memory access and communication overhead far longer than calculation time. Consequently, data communication time often becomes the primary bottleneck in quantum circuit simulation. Many classical simulators, such as QuEST, are designed to execute quantum gates serially without exploiting data locality, and all amplitudes need to be exchanged. Such a gate-unaware full-data communication scheme forces powerful computation such as GPUs to spend substantial time waiting for data transfers. In this paper, we propose a gate-aware on-demand communication quantum simulation framework to optimize communication overhead. We analyze the on-demand communication process and develop a communication cost model to elucidate the relationship between communication costs and circuit partition. Since greedy circuit partition method can only yield locally optimal solutions, we propose a choice-start-node quantum circuit partition method guided by the communication cost model to further reduce communication overhead in quantum circuit simulation. We evaluate on a 4-node cluster with 2 NVIDIA A100 GPUs on each node and our design achieves 59.32× speedup compared to the gate-unaware full-data communication scheme. Furthermore, compared to the greedy circuit partition method, our partition method can achieve simulation acceleration of 1.79×.
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.
Executing large quantum circuits is not feasible using the currently available NISQ (noisy intermediate-scale quantum) devices. The high costs of using real quantum devices make it further challenging to research and develop quantum algorithms. As a result, performing classical simulations is usually the preferred method for researching and validating large-scale quantum algorithms. However, these simulations require a huge amount of resources, as each additional qubit exponentially increases the computational space required. Distributed Quantum Computing (DQC) is a promising alternative to reduce the resources required for simulating large quantum algorithms at the cost of increased runtime. This study presents a comparative analysis of two simulation methods: circuit-splitting and full-circuit execution using distributed memory, each having a different type of overhead. The first method, using CutQC, cuts the circuit into smaller subcircuits and allows us to simulate a large quantum circuit on smaller machines. The second method, using Qiskit-Aer-GPU, distributes the computational space across a distributed memory system to simulate the entire quantum circuit. Results indicate that full-circuit executions are faster than circuit-splitting for simulations performed on a single node. However, circuit-splitting simulations show promising results in specific scenarios as the number of qubits is scaled.
Distributing quantum workloads over many Quantum Processing Units (QPUs) is a crucial step in scaling up quantum computers toward practical quantum advantage due to the limitations in size of a single QPU. In the absence of high-fidelity quantum interconnects, circuit knitting could provide a path to computing certain properties of large quantum systems on many QPUs of limited size in a distributed fashion using only classical communication. Circuit knitting partitions large quantum circuits into manageable sub-circuits, however, reconstructing observables in a straightforward manner comes at an exponential cost in sampling and classical post-processing. To mitigate the overhead this technique incurs, we introduce an Adaptive Circuit Knitting (ACK) method that finds efficient partitions of quantum circuits by discovering regions of minimal entanglement between subsystems. We simulate 1D and 2D disordered mixed-field Ising models up to 60 qubits and show that the ACK approach can reduce circuit knitting sampling overheads by up to four orders of magnitude for observables of interest. We highlight our parallel GPU-accelerated implementation and discuss the need for efficient classical simulators to enable distributed quantum algorithm development. Our techniques could enable efficient distribution of quantum simulation for both near-term and fault-tolerant architectures.