This paper compares quantum computing simulators running on a single CPU or GPU-based HPC node using the Quantum Volume benchmark commonly proposed for comparing NISQ systems. As simulators do not suffer from noise, the metric used in the comparison is the time required to simulate a set Quantum Volume. The results are important to estimate the feasibility of proof of concept studies and debugging of quantum algorithms on HPC systems. Besides benchmarks of some commonly used simulators, this paper also offers an overview of their main features, a review of the state of quantum computing simulation and quantum computing benchmarking, and some insight into the theory of Quantum Volume.
This paper compares quantum computing simulators running on a single CPU or GPU-based HPC node using the Quantum Volume benchmark commonly proposed for comparing NISQ systems. As simulators do not suffer from noise, the metric used in the comparison is the time required to simulate a set Quantum Volume. The results are important to estimate the feasibility of proof of concept studies and debugging of quantum algorithms on HPC systems. Besides benchmarks of some commonly used simulators, this paper also offers an overview of their main features, a review of the state of quantum computing simulation and quantum computing benchmarking, and some insight into the theory of Quantum Volume.
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.
Variational quantum algorithms are of special importance in the research on quantum computing applications because of their applicability to current Noisy Intermediate-Scale Quantum (NISQ) devices. The main building blocks of these algorithms define a relatively large parameter space, making the comparison of results and performance between different approaches and software simulators cumbersome and prone to errors. In this paper, we employ a generic description of the problem, in terms of both Hamiltonian and ansatz, to consistently port a problem definition across different simulators. A use case relevant to current quantum hardware has been run on a set of HPC systems and software simulators to study the dependence of performance on the runtime environment, the scalability of the simulation codes, and the agreement between the physical results, respectively. The results show that our toolchain can successfully translate a problem definition between different simulators. On the other hand, variational algorithms are limited in their scaling by the long runtimes with respect to their memory footprint, so they expose limited parallelism to computation. This shortcoming is partially mitigated by using techniques like job arrays. The potential of the parser tool within the toolchain for exploring HPC performance and comparisons of results of variational algorithm simulations is highlighted.
Quantum computing in the NISQ era requires advanced emulation tools to facilitate the development and validation of complex quantum algorithms, as current quantum devices remain too noisy and resource-limited. This work presents a comprehensive review of quantum emulation software available for HPC centers using various comparison metrics to evaluate parallelization, precision, acceleration via Graphics Processing Unit (GPU), emulation with noise, among others. The comparative analysis evaluates four quantum emulation frameworks (QuEST, Qaptiva HPC, CUDA-Q, and PennyLane) across three hardware platforms with distinct characteristics: two HPC clusters (Spartan-Eviden and Joliot-Curie TGCC) and one quantum emulation appliance (Eviden Qaptiva 804). The scope of this analysis is fundamentally tied to the state-vector emulation paradigm. The experimental results of our evaluation reveal that performance depends on classical hardware configuration and circuit characteristics, that is, CUDA-Q excels in single-GPU environments, while Qaptiva HPC shows advantages in distributed multi-node configurations (both CPU and GPU).
Rapid advances in quantum computing technology lead to an increasing need for software simulators that enable both algorithm design and the validation of results obtained from quantum hardware. This includes calculations that aim at probing regimes of quantum advantage, where a quantum computer outperforms a classical computer in the same task. High performance computing (HPC) platforms play a crucial role as today’s quantum devices already reach beyond the limits of what powerful workstations can model, but a systematic evaluation of the individual performance of the many offered simulation packages is lacking so far. In this Technical Review, we benchmark several software packages capable of simulating quantum dynamics with a special focus on HPC capabilities. We develop a containerized toolchain for benchmarking a large set of simulation packages on a local HPC cluster using different parallelisation capabilities, and compare the performance and system size-scaling for three paradigmatic quantum computing tasks. Our results can help finding the right package for a given simulation task and lay the foundation for a systematic community effort to benchmark and validate upcoming versions of existing and also newly developed simulation packages.
The superiority of Quantum algorithms over classical algorithms, wherein the quantum algorithms can provide an exponential speedup, is a well-proven phenomenon. Quantum computing simulations on classical computers are of utmost importance in preparing the research community for the quantum computing revolution, designing new algorithms, and validating the results obtained from the quantum hardware. This paper demonstrates the performance benchmarking results of the indigenously developed density-matrix-based quantum computing simulator QSim (qctoolkit.in). We have adopted a framework called “Application-Oriented Performance Benchmarks for Quantum Computing” for the benchmarking procedure and modified it for our cause. We evaluated the performance of the simulator on CPU, GPU, and the Vector Accelerator by running the quantum algorithms with varying numbers of qubits in each cycle, with and without the insertion of noise parameters, to evaluate various metrics. The results obtained will provide the performance aspects of the simulator on the described platforms.
Digital quantum computing promises new pathways for problem solving. However, currently available gate-based quantum computers still suffer from short decoherence timings and noisy results. In the meantime, quantum emulators can offer a way to test and refine quantum algorithms. They reproduce classically the behavior of quantum computers at the cost of large memory requirements: the memory needed doubles with each additional qubits emulated. Emulators can be set-up on a large range of machines, from laptops to supercomputers. In this paper, we compare the capabilities of quantum emulators built on the EuroHPC Karolina server using one CPU, one GPU or multiple GPUs in a single node. In terms of the number of qubits emulated, we show that while only 20+ qubits can be emulated using a single CPU, a single GPU brings this number to 30+. We also compare the timing performances, both for randomly generated quantum circuits and for Shor’s factorization algorithm. For randomly generated circuits with same depth and width, the emulators using a GPU and eight GPUs respectively are up to 20 and 180 times faster than the CPU-based emulator. For Shor’s algorithm which can have more than a million gates, the emulators using a GPU and eight GPUs respectively are up to 11 and 20 times faster than the CPU-based emulator. This benchmark of different types of quantum emulators on the Karolina server helps to choose the right parameters and make the most of the Karolina resources for future scientific work involving quantum emulation.