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.
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.
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).
The rapid expansion of quantum computing research has created a growing need for effective and scalable tools to simulate quantum algorithms. In this study, we present a performance analysis of several quantum circuit simulators by using the Deutsch-Jozsa (DJ) and Quantum Fourier Transform (QFT). A gate-based QASM implementation serves as the baseline and is converted to code compatible with each simulator. Our evaluation covers multi-CPU simulators like Qiskit, Qsim-Cirq, and QuEST, as well as their GPU-accelerated variants along with CUDA-Q, for qubit sizes ranging from 21 to 35. By profiling these algorithms, we compare key performance indicators and reveal notable efficiency differences between platforms. In particular, the multi-node version of QuEST shows strong CPU utilization, high speedup, and efficient memory scalability. whereas GPUbased implementations provide clear performance advantages at higher qubit counts. These findings offer practical guidance for choosing simulation platforms according to problem size and available computing resources, thereby supporting more efficient quantum-algorithm workflows.
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.
If classical algorithms have been successful in reproducing the estimation of expectation values of observables of some quantum circuits using off-the-shelf computing resources, matching the performance of the most advanced quantum devices on sampling problems usually requires extreme cost in terms of memory and computing operations, making them accessible to only a handful of supercomputers around the world. In this work, we demonstrate for the first time a classical simulation outperforming Gaussian boson sampling experiments of one hundred modes on established benchmark tests using a single CPU or GPU. Being embarrassingly parallelizable, a small number of CPUs or GPUs allows us to match previous sampling rates that required more than one hundred GPUs. We believe algorithmic and implementation improvements will generalize our tools to photo-counting, single-photon inputs, and pseudo-photon-number-resolving scenarios beyond one thousand modes. Finally, most of the innovations in our tools remain valid for generic probability distributions over binary variables, rendering it potentially applicable to the simulation of qubit-based sampling problems and creating classical surrogates for classical-quantum algorithms.
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.
Quantum computing is regarded as the future of computing that hopefully provides exponentially large processing power compared to the conventional digital computing. However, current quantum computers do not have the capability to correct errors caused by environmental noise, so that it is difficult to run useful algorithms that require deep quantum circuits. Therefore, emulation of quantum circuits in digital computers is essential. However, emulation of large quantum circuits requires enormous amount of computations, and leads to a very large processing time. To reduce the processing time, we propose an FPGA emulator with high-bandwidth-memory to emulate quantum Fourier transform (QFT), which is a major part of many quantum algorithms. The proposed FPGA emulator is scalable in terms of both processing speed and the number of qubits, and extendable to multiple FPGAs. We performed QFT emulations up to 30 qubits using two FPGAs. According to the measured results, we have achieved 23.6 ~ 24.5 times speed-up compared to a fully optimized 24-core CPU emulator.