As quantum circuits grow in complexity, traditional simulation methods face significant challenges, particularly regarding memory usage and computational time. This paper investigates the performance of decision diagram-based quantum circuit simulations when scaled across high-performance computing (HPC) environments. Specifically, we benchmark a multi-core implementation of the Quantum Multi-Valued Decision Diagram (QMDD) circuit simulator using the COARE HPC facility at the Department of Science and Technology-Advanced Science and Technology Institute. Our results demonstrate that leveraging decision diagrams in a multi-core setup enhances simulation scalability and efficiency in several circuits, although not all, compared to single-core implementations and other classical simulation techniques, such as Qiskit Aer and Munich Quantum Toolkit’s DDSim. This work provides valuable insights into the practical scalability of quantum circuit simulators and suggests potential directions for future advancements in quantum computing simulation technologies.
As quantum circuits grow in complexity, traditional simulation methods face significant challenges, particularly regarding memory usage and computational time. This paper investigates the performance of decision diagram-based quantum circuit simulations when scaled across high-performance computing (HPC) environments. Specifically, we benchmark a multi-core implementation of the Quantum Multi-Valued Decision Diagram (QMDD) circuit simulator using the COARE HPC facility at the Department of Science and Technology-Advanced Science and Technology Institute. Our results demonstrate that leveraging decision diagrams in a multi-core setup enhances simulation scalability and efficiency in several circuits, although not all, compared to single-core implementations and other classical simulation techniques, such as Qiskit Aer and Munich Quantum Toolkit’s DDSim. This work provides valuable insights into the practical scalability of quantum circuit simulators and suggests potential directions for future advancements in quantum computing simulation technologies.
The increasing complexity of quantum algorithms and the limitations of current Noisy Intermediate-Scale Quantum (NISQ) hardware underscore the importance of efficient classical simulators. To support informed decision-making by users of quantum circuit simulators, we benchmark seven statevector-based quantum circuit simulators (Qiskit, Qulacs, Qibo, Qsimov, Cirq, Pennylane and the Intel Quantum Simulator (IQS)) on a multicore node of the Lusitania high-performance computing (HPC) system. We evaluate their performance in terms of execution time, memory usage and core scalability using Grover’s algorithm, the quantum Fourier transform (QFT), and quantum volume (QV) circuits, across qubit counts ranging from 3 to 30. Our results reveal that Qulacs offers the best performance for circuits below 22 qubits, while Qiskit becomes the fastest for larger and more complex circuits. Qiskit and Qulacs achieve the most efficient parallel performance across multiple cores, while others display limited scaling benefits. IQS shows the lowest memory consumption in QFT and QV benchmarks for systems under 24 qubits; however, it suffers from higher execution times, particularly for Grover’s algorithm. The experimental implementation of Qsimov consistently underperforms in both runtime and scalability; for this reason, it is employed as a baseline in our measurements, serving to highlight the importance of performance optimizations in statevector-based quantum circuit simulators. Previous findings provide a comprehensive performance landscape to guide researchers in selecting appropriate simulators for both standard and large-scale quantum workloads on HPC infrastructures.
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.
Recent technological advancements show promise in leveraging quantum mechanical phenomena for computation. This brings substantial speed-ups to problems that are once considered to be intractable in the classical world. However, the physical realization of quantum computers is still far away from us, and a majority of research work is done using quantum simulators running on classical computers. Classical computers face a critical obstacle in simulating quantum algorithms. Quantum states reside in a Hilbert space whose size grows exponentially to the number of subsystems, i.e., qubits. As a result, the straightforward statevector approach does not scale due to the exponential growth of the memory requirement. Decision diagrams have gained attention in recent years for representing quantum states and operations in quantum simulations. The main advantage of this approach is its ability to exploit redundancy. However, mainstream quantum simulators still rely on statevectors or tensor networks. We consider the absence of decision diagrams due to the lack of parallelization strategies. This work explores several strategies for parallelizing decision diagram operations, specifically for quantum simulations. We propose optimal parallelization strategies. Based on the experiment results, our parallelization 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.
The classical simulation of quantum circuits is constrained by the exponential growth of the quantum state. Decision-diagram methods, such as the Quantum Multiple-valued Decision Diagram (QMDD), mitigate this by exploiting regularities in the tensor descriptions of quantum operators and states. This paper presents QOLE, a browser-native quantum circuit simulator built on QMDDs. It pairs a simple, high-level programming interface with an internal compiler that maps gates and state vectors into compact graphs for efficient simulation. Extending the conference version of this work, we add an empirical evaluation of the open implementation across four families of circuits (GHZ states, linear cluster states, the Bernstein–Vazirani algorithm and random Clifford + T circuits) of up to 29 qubits. We measure diagram size, the wall-clock cost of construction and sampling, and the memory behaviour of the lazy output interface, contrasting diagram sizes against a dense state-vector baseline. For structured circuits, QOLE represents the full state in a number of nodes that grows linearly with the qubit count, whereas a dense representation grows exponentially, and the lazy interface keeps the working set bounded independently of state size. Diagram-based simulation is thus both accessible and effective within modern web execution environments.
Classical simulation remains essential for the development of quantum algorithms; however, traditional matrix-based methods struggle with scalability. While the Fast Tensor Decision Diagram (FTDD) framework offers a hybrid approach by combining the strengths of Tensor Networks and Decision Diagrams (DDs) for memory-efficient simulation, its core operations are often bottlenecked by memory management structures. Our primary motivation is to address the high computational and spatial cost associated with the hash tables, as well as to simplify the complexity of the simulation workflow. This work introduces systematic, low-level optimizations to the framework. Our main contribution is the enhancement of the memory management layer for the internal tables. We implemented a node reuse mechanism and designed a controlled strategy for hash table growth. These improvements minimize the memory footprint and reduce the costly hashing overhead inherent in DD-based simulators. We performed a thorough experimental analysis using a diverse set of quantum circuits, which were characterized by well-established metrics. Experimental results demonstrate that our enhancements improve the tool's performance and scalability. Notably, these advancements enable the simulation of complex circuits, such as Quantum Fourier Transform, with hundreds of qubits, on a standard server with 2-Tb of memory.
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 high-performance QCS that capitalizes on the strength of both DD- and array-based approaches. FlatDD parallelizes the simulation workload at multiple levels and leverages an efficient caching technique 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 34.81× speed-up and 1.93× memory reduction.