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IEEE Transactions on Quantum Engineering

Improving Decision Diagram-Based Quantum Circuit Simulation Using Static Variable Ordering and Multinode Ring Communication

Yusuke Kimura, Shaowen Li, Hiroyuki Satō, Masahiro Fujita, Robert Wille

Published Jan 1, 2026• 0 citations
Publisher

Abstract

Currently the development of quantum computers is active; however, large-scale machines remain limited and noisy. Furthermore, such quantum computers do not allow direct access to state vectors, posing challenges for quantum algorithm development. Quantum circuit simulators on classical computers offer a solution, with decision diagram (DD)-based simulators being particularly memory-efficient for representing quantum states. However, DD-based simulation still requires optimization, especially concerning variable ordering and multi-node parallelization. Processing time for DDs heavily depends on variable order, but existing static variable ordering methods did not have general applicability. The prior multi-threaded DD simulations showed slowdowns. This study proposes two techniques to address these issues. The first is a scoring-based heuristic static variable ordering method that analyzes an input quantum circuit, such as the distribution of parameterized rotations and multi-bit gates, to suppress the growth of graph nodes and amount of computation. The second is a ring communication approach that reduces communication overhead when performing parallel simulations across multiple computing nodes. Parallel computation requires data exchange between nodes, which results in additional communication time. The proposed ring communication method can eliminate broadcast communication, thereby reducing the waiting time until communication is completed. Evaluations using various benchmark circuits demonstrate that the proposed ordering achieves up to a 4.5 speedup in a single-node environment. Furthermore, the ring communication exhibits superior scalability, achieving up to an 11x speedup with 16 computing nodes. The proposed variable ordering method generally reduces the overall simulation time in multi-node environments.

DOI
10.1109/tqe.2026.3654543

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Currently the development of quantum computers is active; however, large-scale machines remain limited and noisy. Furthermore, such quantum computers do not allow direct access to state vectors, posing challenges for quantum algorithm development. Quantum circuit simulators on classical computers offer a solution, with decision diagram (DD)-based simulators being particularly memory-efficient for representing quantum states. However, DD-based simulation still requires optimization, especially concerning variable ordering and multi-node parallelization. Processing time for DDs heavily depends on variable order, but existing static variable ordering methods did not have general applicability. The prior multi-threaded DD simulations showed slowdowns. This study proposes two techniques to address these issues. The first is a scoring-based heuristic static variable ordering method that analyzes an input quantum circuit, such as the distribution of parameterized rotations and multi-bit gates, to suppress the growth of graph nodes and amount of computation. The second is a ring communication approach that reduces communication overhead when performing parallel simulations across multiple computing nodes. Parallel computation requires data exchange between nodes, which results in additional communication time. The proposed ring communication method can eliminate broadcast communication, thereby reducing the waiting time until communication is completed. Evaluations using various benchmark circuits demonstrate that the proposed ordering achieves up to a 4.5 speedup in a single-node environment. Furthermore, the ring communication exhibits superior scalability, achieving up to an 11x speedup with 16 computing nodes. The proposed variable ordering method generally reduces the overall simulation time in multi-node environments.
Decision diagram (DD)-based quantum circuit simulators represent quantum states and gates using DDs, enabling memory-efficient and fast simulations for some quantum circuits like Shor. Although it is known that DD size and processing time vary depending on the variable order in classical circuits, there has not been much research on the variable order under quantum circuit simulation. One existing study pointed out that dynamic reordering worsens the simulation time and numerical accuracy, and there is no comprehensive research on static orders in the context of quantum circuit simulation. Therefore, this paper proposes a scoring-based heuristic method for determining a static variable order that enables efficient DD-based quantum circuit simulation. When applied to benchmark circuits, the default original variable orders resulted in slow simulations, whereas the proposed method achieved speedups of up to 150x. Furthermore, the proposed order completed the simulation of Shor's 1011 factorization in 5 hours on a single-core laptop, although it was not completed within two days previously.
An N-bit quantum state requires a vector of length 2N, leading to an exponential increase in the required memory with N in conventional statevector-based quantum simulators. A proposed solution to this issue is the decision diagram-based quantum simulator, which can significantly decrease the necessary memory and is expected to operate faster for specific quantum circuits. However, decision diagram-based quantum simulators are not easily parallelizable because data must be manipulated dynamically, and most implementations run on one thread. This paper introduces ring communication-based optimal parallelization and automatic swap insertion techniques for multi-node implementation of decision diagram-based quantum simulators. The ring communication approach is designed so that each node communicates with its neighboring nodes, which can facilitate faster and more parallel communication than broadcasting where one node needs to communicate with all nodes simultaneously. The automatic swap insertion method, an approach to minimize inter-node communication, has been employed in existing multi-node state vector-based simulators, but this paper proposes two methods specifically designed for decision diagram-based quantum simulators. These techniques were implemented and evaluated using the Shor algorithm and random circuits with up to 38 qubits using a maximum of 256 nodes. The experimental results have revealed that multi-node implementation can reduce run-time by up to 26 times. For example, Shor circuits that need 38 qubits can finish simulation in 147 seconds. Additionally, it was shown that ring communication has a higher speed-up effect than broadcast communication, and the importance of selecting the appropriate automatic swap insertion method was revealed.
Quantum computing is a hotspot technology for its potential to accelerate specific applications by exploiting quantum parallelism. However, current physical quantum computers are limited to a relatively small scale, simulators based on conventional machines are significantly relied on to perform quantum computing research. The straightforward array-based simulators require a tremendous amount of memory that increases exponentially with respect to the number of qubits. To mitigate such computing resource concerns, decision diagram based simulators were proposed that can efficiently exploit data redundancies in quantum states and operations. In this paper, we study two classes of quantum circuits on which the state-of-the-art decision diagram based simulators failed to perform well in terms of simulation time. We also propose a simple and powerful reorder trick to boost the simulation of such quantum circuits. Preliminary evaluation results demonstrate the usefulness of the proposed trick. Especially, for the Quantum Phase Estimation circuits, the proposed trick achieved speedups up to 313.6x compared to a state-of-the-art approach that relies on an auxiliary tool to optimize simulation order.
Quantum circuit simulator (QCS) is essential for designing quantum algorithms because it assists researchers in understanding how quantum operations work without access to expensive quantum computers. Traditional array-based QCSs suffer from exponential time and memory complexities. To address this problem, Decision Diagram (DD) was introduced to compress simulation data by exploring the circuit regularity. However, for irregular circuit structures, DD-based simulation incurs significant runtime and memory overhead. To overcome this challenge, we present FlatDD, a parallel QCS that capitalizes on the strength of both DD- and array-based approaches. FlatDD parallelizes the simulation workload at multiple levels and leverages caching to reuse historical results. To further enhance the simulation performance for deep circuits, FlatDD introduces a gate-fusion algorithm to reduce the computational cost. Compared to state-of-the-art QCSs on commonly used quantum circuits, FlatDD achieves 54.35 × speed-up and 1.91 × memory reduction.
Since people became aware of the power of quantum phenomena in the domain of traditional computation, a great number of complex problems that were once considered intractable in the classical world have been tackled. The downsides of quantum supremacy are its high cost and unpredictability. In order to obtain a stronger control over quantum interactions(by reducing its noise) and reduce its cost, numerous researchers are relying on quantum simulators running on classical computers. The critical obstacle facing classical computers in the task of quantum simulation is its limited memory space. Quantum simulation intrinsically models the state evolution of quantum subsystems, i.e., qubits. Qubits are mathematically constructed in Hilbert space whose size grows exponentially with its number. Consequently, the scalability of the straightforward statevector approach is limited. It has been proven effective to adopt decision diagrams to mitigate the memory cost issue in various fields. In recent years, researchers have adapted decision diagrams into different forms for representing quantum states and performing quantum calculations efficiently. This leads to the study of decision diagram based quantum simulation. However, their advantage of memory efficiency does not let it replace the mainstream statevector and tensor network based approaches. We argue the reason is the lack of effective parallelization strategies in performing calculations on decision diagrams. In this work, we explore several strategies for parallelizing decision diagram operations with the focus on leveraging them for quantum simulations. The target is to find the optimal parallelization strategies and improve the performance of decision diagram based quantum simulation. Based on the experiment results, our proposed strategy achieves a 2-3 times faster simulation of Grover's algorithm and random circuits than the state-of-the-art single-thread DD-based simulator DDSIM.
Quantum computers promise to solve important problems faster than conventional computers ever could. Underneath is a fundamentally different computational primitive that introduces new challenges for the development of software tools that aid designers of corresponding quantum algorithms. The different computational primitives render classical simulation of quantum circuits particularly challenging. While the logic simulation of conventional circuits is comparatively simple with linear complexity with respect to the number of gates, quantum circuit simulation has to deal with the exponential memory requirements to represent quantum states on non-quantum hardware with respect to the number of qubits. Decision Diagrams (DDs) address this challenge through exploitation of redundancies in matrices and vectors to provide significantly more compact representations in many cases. Moreover, the probabilistic nature of quantum computations enables another angle to tackle the complexity: Quantum algorithms are resistant to some degree against small inaccuracies in the quantum state as these only lead to small changes in the outcome probabilities. We propose to exploit this resistance against (small) errors to gain even more compact decision diagrams. In this work, we investigate the potential of approximation in quantum circuit simulation in detail. To this end, we first present four dedicated schemes that exploit the error resistance and efficiently approximate quantum states represented by decision diagrams. Subsequently, we propose two simulation strategies that utilize those approximations schemes in order to improve the efficiency of DD-based quantum circuit simulation, while, at the same time, allowing the user to control the resulting degradation in accuracy. We empirically show that the proposed approximation schemes reduce the size of decision diagrams substantially and also analytically prove the effect of multiple approximations on the attained accuracy. Eventually, this enables speed-ups of the resulting approximate quantum circuit simulation of up to several orders of magnitudes—again, while controlling the fidelity of the result.