Quantum simulation plays a critical role in advancing our understanding and development of quantum algorithms, quantum computing hardware, and quantum information science. Despite the availability of various quantum circuit simulators, they often face challenges in terms of maintainability and extensibility. In this paper, we introduce Lightweight Functional Quantum Simulator (LFQS), a compilation-based framework that addresses these issues by leveraging an intermediate language to synthesize efficient quantum simulators in C and CUDA. The intermediate language exploits the underlying structured sparsity behind the matrix representation of quantum gates. Our framework generates efficient code for multi-core CPUs and GPUs and outperforms state-of-the-art simulators, such as Qiskit, Catalyst, and QuEST, as well as a representative of dense tensor frameworks, NumPy, in both micro-benchmark and macro-benchmark circuits.
Quantum simulation plays a critical role in advancing our understanding and development of quantum algorithms, quantum computing hardware, and quantum information science. Despite the availability of various quantum circuit simulators, they often face challenges in terms of maintainability and extensibility. In this paper, we introduce Lightweight Functional Quantum Simulator (LFQS), a compilation-based framework that addresses these issues by leveraging an intermediate language to synthesize efficient quantum simulators in C and CUDA. The intermediate language exploits the underlying structured sparsity behind the matrix representation of quantum gates. Our framework generates efficient code for multi-core CPUs and GPUs and outperforms state-of-the-art simulators, such as Qiskit, Catalyst, and QuEST, as well as a representative of dense tensor frameworks, NumPy, in both micro-benchmark and macro-benchmark circuits.
Quantum computing holds transformative potential, but its adoption is hindered by the complexity of generating efficient, hardware-specific code. This work presents a modular, extensible compiler framework that bridges high-level quantum languages with diverse hardware architectures. The framework consists of three modules: a front-end for parsing quantum code into a hardware-agnostic intermediate representation (IR), an optimization module for enhancing quantum circuits through gate synthesis, qubit routing, and error mitigation, and a back-end for generating hardware-specific instructions. Major contributions include a hardware-agnostic IR for cross-platform compatibility, optimization techniques to reduce gate complexity and noise, and hardware-specific adaptations to improve execution fidelity. A practical demonstration optimizes quantum circuits, highlighting the impact of hardware constraints. Comparative analysis of IBM Quantum and IonQ platforms underscores the role of qubit connectivity and noise resilience in algorithmic performance. This scalable framework enhances quantum software development and efficient hardware utilization.
Quantum technologies are moving towards the development of novel hardware devices based on quantum bits (qubits). In parallel to the development of quantum devices, efficient simulation tools are needed in order to design and benchmark quantum algorithms and applications before deployment on quantum hardware. In this context, we present a first attempt to perform circuit-based quantum simulation using the just-in-time (JIT) compilation technique on multiple hardware architectures and configurations based on single-node central processing units (CPUs) and graphics processing units (GPUs). One of the major challenges in scientific code development is to balance the level of complexity between algorithms and programming techniques without losing performance or degrading code readability. In this context, we have developed qibojit : a new module for the Qibo quantum computing framework, which uses a just-in-time compilation approach through Python. We perform systematic performance benchmarks between our JIT approach and a subset of relevant publicly available libraries for quantum computing. We show that our novel approach simplifies the complex aspects of the implementation without deteriorating performance.
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 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.
In recent years, quantum computing has undergone significant developments and has established its supremacy in many application domains. While quantum hardware is accessible to the public through the cloud environment, a robust and efficient quantum circuit simulator is necessary to investigate the constraints and foster quantum computing development, such as quantum algorithm development and quantum device architecture exploration. In this paper, we observe that most of the publicly available quantum circuit simulators (e.g., QISKit from IBM, QDK from Microsoft, and Qsim-Cirq from Google) suffer from slow simulation and poor scalability when the number of qubits increases. To this end, we systematically investigate the deficiencies in quantum circuit simulation (QCS) and propose Q-GPU, a framework that leverages GPUs with comprehensive optimizations to allow efficient and scalable QCS. Specifically, Q-GPU features i) proactive state amplitude transfer, ii) zero state amplitude pruning, iii) delayed qubit involvement, and iv) lossless nonzero state amplitude compression. Experimental results across nine representative quantum circuits indicate that Q-GPU significantly reduces the execution time of the state-of-the-art GPU-based QCS by 71.89% (3.55× speedup). Q-GPU also outperforms the state-of-the-art OpenMP CPU implementation, the Google Qsim-Cirq simulator, and the Microsoft QDK simulator by 1.49×, 2.02×, and 10.82×, respectively.
Developing state-of-the-art classical simulators of quantum circuits is of utmost importance to test and evaluate early quantum technology and understand the true potential of full-blown error-corrected quantum computers. In the past few years, multiple theoretical and numerical advances have continuously pushed the boundary of what is classically simulable, hence the development of a plethora of tools which are often limited to a specific purpose or designed for a particular hardware (e.g. CPUs vs. GPUs). Moreover, such tools are typically developed using tailored languages and syntax, which makes it hard to compare results from, and create hybrid approaches using, different simulation techniques. To support unified and optimized use of these techniques across platforms, we developed HybridQ , a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to run on a variety of hardware. The philosophy behind its development has been driven by three main pillars: Easy to Use, Easy to Extend, and Use the Best Available Technology. The powerful tools of HybridQ allow users to manipulate, develop, and extend noiseless and noisy circuits for different hardware architectures. HybridQ supports large-scale high-performance computing (HPC) simulations, automatically balancing workload among different processor nodes and enabling the use of multiple backends to maximize parallel efficiency. Everything is then glued together by a simple and expressive language that allows seamless switching from one technique to another as well as from one hardware to the next, without the need to write lengthy translations, thus greatly simplifying the development of new hybrid algorithms and techniques.