Classical simulations are essential for the development of quantum computing, and their exponential scaling can easily fill any modern supercomputer. In this paper we consider the performance and energy consumption of large Quantum Fourier Transform (QFT) simulations run on ARCHER2, the UK’s National Supercomputing Service, with QuEST toolkit. We take into account CPU clock frequency and node memory size, and use cache-blocking to rearrange the circuit, which minimises communications. We find that using 2.00 GHz instead of 2.25 GHz can save as much as 25% of energy at 5% increase in runtime. Higher node memory also has the potential to be more efficient, and cost the user fewer CUs, but at higher runtime penalty. Finally, we present a cache-blocking QFT circuit, which halves the required communication. All our optimisations combined result in 40% faster simulations and 35% energy savings in 44 qubit simulations on 4,096 ARCHER2 nodes.
Classical simulations are essential for the development of quantum computing, and their exponential scaling can easily fill any modern supercomputer. In this paper we consider the performance and energy consumption of large Quantum Fourier Transform (QFT) simulations run on ARCHER2, the UK’s National Supercomputing Service, with QuEST toolkit. We take into account CPU clock frequency and node memory size, and use cache-blocking to rearrange the circuit, which minimises communications. We find that using 2.00 GHz instead of 2.25 GHz can save as much as 25% of energy at 5% increase in runtime. Higher node memory also has the potential to be more efficient, and cost the user fewer CUs, but at higher runtime penalty. Finally, we present a cache-blocking QFT circuit, which halves the required communication. All our optimisations combined result in 40% faster simulations and 35% energy savings in 44 qubit simulations on 4,096 ARCHER2 nodes.
Quantum circuit simulation at scale demands high-performance computing (HPC) frameworks capable of distributing exponentially growing state vectors and tensor networks across heterogeneous compute clusters. As quantum algorithm development accelerates and circuits approach 50-100 qubits in practical workloads, simulation bottlenecks shift from single-node memory constraints to inter-node communication overhead, GPU memory bandwidth, and framework-level scheduling inefficiencies. This paper proposes the HPC Quantum Simulation Framework Benchmark (HQSFB), a systematic evaluation of five leading quantum simulation frameworks -- Qiskit Aer, Cirq on GCP, cuQuantum (NVIDIA), QuEST, and Intel Quantum Simulator (IQS) -- across six HPC deployment configurations spanning single-GPU workstations to 512-GPU supercomputer partitions. HQSFB introduces the Framework Efficiency Score (FES) combining simulation throughput, GPU utilisation, inter-node communication overhead, and memory scaling efficiency. Key results: cuQuantum achieves the highest FES (0.924) with 94.2% GPU utilisation and 42x speedup over CPU baseline at 512 GPUs; QuEST achieves the best memory scaling efficiency (0.962) for distributed statevector simulation; Qiskit Aer provides the broadest circuit compatibility with FES = 0.842. The framework provides HPC configuration guidance and an open benchmark suite for quantum simulation infrastructure planning.
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.
Classical computers require large memory resources and computational power to simulate quantum circuits with a large number of qubits. Even supercomputers that can store huge amounts of data face a scalability issue in regard to parallel quantum computing simulations because of the latency of data movements between distributed memory spaces. Here, we apply a cache blocking technique by inserting swap gates in quantum circuits to decrease data movements. We implemented this technique in the open source simulation framework Qiskit Aer. We evaluated our simulator on GPU clusters and observed good scalability.
While extensive research over the past decades has been dedicated to developing scalable quantum computers, the question of their energetic performance has only gained attention more recently, but its importance is now recognized. In fact, quantum computers can only be a viable alternative if their energy cost scales favorably, and some research has shown that there is even a potential quantum energy advantage. In parallel, Rydberg atoms have recently emerged as one of the most promising platforms to implement a large-scale quantum computer. This work aims at contributing first steps to understand the energy efficiency of this platform, by investigating the energy consumption of the different elements of a Rydberg atom quantum computer. First, an experimental implementation of the Quantum Phase Estimation algorithm is analyzed, and an estimation of the energetic cost of executing it is calculated. Then, we derive an estimate of how the energy cost of performing the Quantum Fourier Transform scales with the number of qubits in the Rydberg platform. This analysis facilitates a comparison of the energy consumption of different elements within a Rydberg atom quantum computer, from the preparation of the atoms to the execution of the algorithm, and the measurement of the final state, enabling the evaluation of the energy expenditure of the Rydberg platform and the identification of potential improvements. Finally, we use the Quantum Fourier Transform as an energetic benchmark, comparing the scaling we obtained to that of the execution of the Discrete Fourier Transform in two state-of-the-art classical supercomputers. This comparison indicates that, in an ideal error-free scenario, a quantum energy advantage is achieved in the Rydberg platform for the Fourier Transform, in a regime where classical algorithms are still faster.
Advances in quantum simulator technology are increasingly required because research on quantum algorithms is becoming more sophisticated and complex. State vector simulation utilizes CPU and memory resources in computing nodes exponentially with respect to the number of qubits; furthermore, in a variational quantum algorithm, the large number of repeated runs by classical optimization is also a heavy load. This problem has been addressed by preparing numerous computing nodes or simulation frameworks that operate effectively. This study aimed to accelerate quantum simulation using two newly proposed methods: to efficiently utilize limited computational resources by adjusting the ratio of the MPI and distributed processing parallelism corresponding to the target problem settings and to slim down the Hamiltonian by considering the effect of accuracy on the calculation result. Ground-state energy calculations of a fermionic model were performed using a variational quantum eigensolver (VQE) on an HPC cluster with up to 1024 FUJITSU Processor A64FX connected to each other by InfiniBand; the processor is also used on supercomputer Fugaku. We achieved 200 times higher speed over VQE simulations and demonstrated 32 qubits ground-state energy calculations in acceptable time. This result indicates that > 30 qubit state vector simulations can be realistically utilized for further research on variational quantum algorithms.
Semiconductor spin qubits offer a promising route to compact and scalable quantum computers, combining long coherence times in isotopically purified silicon with compatibility with semiconductor manufacturing. Beyond computational speedup, their energy efficiency is an important consideration for future large-scale systems. We evaluate the power and energy requirements of a Loss-DiVincenzo spin-qubit quantum computer by analyzing the Quantum Fourier Transform (QFT) and benchmarking it against the classical Fast Fourier Transform (FFT). We develop a detailed energy budget, identifying cryogenic cooling as the dominant baseline contribution before quantum error correction (QEC). The comparison between the QFT and the classical FFT reveals that, for any quantum workload operating under a polynomially bounded sampling regime, the crossover in energy consumption occurs at smaller problem sizes than the crossover in execution time. Thus, quantum computing can become energetically competitive before achieving a speedup in execution time. We then incorporate surface-code QEC and analyze the energy costs of QEC cycles, gate synthesis, and decoding. At scale, instrumentation and decoding become dominant contributions, while cryogenic cooling imposes a constraint on scalability. We show that optimizing the control-signal demultiplexing ratio can reduce the overall energy cost by approximately two orders of magnitude. For a system with about one million physical qubits, we estimate a power consumption of approximately 100 kW, at least one order of magnitude below that of today's highest-performance classical computing systems.