Quantum circuit simulations play a critical role in bridging the gap between theoretical quantum algorithms and their practical realization on physical quantum hardware, yet they face computational challenges due to the exponential growth of quantum state spaces with increasing qubit size. This work presents PennyLane-Lightning MPI, an MPI-based extension of the PennyLane-Lightning suite, developed to enable scalable quantum circuit simulations through parallelization of quantum state vectors and gate operations across distributed-memory systems. The core of this implementation is an index-dependent, gate-specific parallelization strategy, which fully exploits the characteristic of individual gates as well as the locality of computation associated with qubit indices in partitioned state vectors. Benchmarking tests with single gates and well-designed quantum circuits show that the present method offers advantages in performance over general methods based on unitary matrix operations and exhibits excellent scalability, supporting simulations of up to 41-qubit with hundreds of thousands of parallel processes. Being equipped with a Python plug-in for seamless integration to the PennyLane framework, this work contributes to extending the PennyLane ecosystem by enabling high-performance quantum simulations in standard multi-core CPU clusters with no library-specific requirements, providing a back-end resource for the cloud-based service framework of quantum computing that is under development in the Republic of Korea.
Quantum circuit simulations play a critical role in bridging the gap between theoretical quantum algorithms and their practical realization on physical quantum hardware, yet they face computational challenges due to the exponential growth of quantum state spaces with increasing qubit size. This work presents PennyLane-Lightning MPI, an MPI-based extension of the PennyLane-Lightning suite, developed to enable scalable quantum circuit simulations through parallelization of quantum state vectors and gate operations across distributed-memory systems. The core of this implementation is an index-dependent, gate-specific parallelization strategy, which fully exploits the characteristic of individual gates as well as the locality of computation associated with qubit indices in partitioned state vectors. Benchmarking tests with single gates and well-designed quantum circuits show that the present method offers advantages in performance over general methods based on unitary matrix operations and exhibits excellent scalability, supporting simulations of up to 41-qubit with hundreds of thousands of parallel processes. Being equipped with a Python plug-in for seamless integration to the PennyLane framework, this work contributes to extending the PennyLane ecosystem by enabling high-performance quantum simulations in standard multi-core CPU clusters with no library-specific requirements, providing a back-end resource for the cloud-based service framework of quantum computing that is under development in the Republic of Korea.
We introduce PennyLane's Lightning suite, a collection of high-performance state-vector simulators targeting CPU, GPU, and HPC-native architectures and workloads. Quantum applications such as QAOA, VQE, and synthetic workloads are implemented to demonstrate the supported classical computing architectures and showcase the scale of problems that can be simulated using our tooling. We benchmark the performance of Lightning with backends supporting CPUs, as well as NVidia and AMD GPUs, and compare the results to other commonly used high-performance simulator packages, demonstrating where Lightning's implementations give performance leads. We show improved CPU performance by employing explicit SIMD intrinsics and multi-threading, batched task-based execution across multiple GPUs, and distributed forward and gradient-based quantum circuit executions across multiple nodes. Our data shows we can comfortably simulate a variety of circuits, giving examples with up to 30 qubits on a single device or node, and up to 41 qubits using multiple nodes.
Quantum computer simulators running on classical computers are essential for developing real quantum computers and emerging quantum applications. In particular, state vector simulators, which store a full state vector in memory and update it in every quantum operation, can be used to simulate an arbitrary form of quantum circuits, debug quantum applications, and validate future quantum computers. Since the time and space complexity grows exponentially with the number of qubits and easily exceeds the capability of a single machine, various distributed state vector simulators are developed. However, these are tuned for common CPUs and GPUs, not for ARM CPUs. Therefore, we develop a distributed state vector simulator, mpiQulacs, that is optimized for large-scale simulation on A64FX-based cluster systems. A64FX is a 48-core ARM-based CPU having high-bandwidth memory (HBM2) and also used in one of the world's top supercomputers, Fugaku. As computational optimization, we vectorize qubit operations with Scalable Vector Extension (SVE) to fully utilize its HBM2. To reduce the amount of inter-node communication for scalable simulation, mpiQulacs provides a fused-swap gate that collectively performs multiple swap operations as one operation and a quantum circuit optimizer that automatically adds fused-swap gates to a circuit. We evaluate weak and strong scaling of mpiQulacs with up to 39 qubits on our 512-node A64FX-based cluster system. The result shows that mpiQulacs achieves high-scalability on hundreds of nodes in benchmark and practical quantum circuits, and its performance is comparable with the existing well-known distributed state vector simulators.
Large-scale quantum circuit simulation on high-performance computing (HPC) systems is crucial for developing and verifying quantum algorithms to overcome the limitations of current noisy quantum computers. However, existing simulators face scalability bottlenecks due to memory limits and communication overhead. Many state-of-the-art approaches rely on static circuit partitioning, which makes it difficult to address the exponential growth in problem size as the number of qubits increases. To overcome these challenges, we present ScaleQsim, a highly scalable quantum circuit simulation framework for large-scale HPC systems. ScaleQsim focuses on providing a unified representation of the full qubit state, yet provides scalability through efficient synchronization and communication. ScaleQsim adopts a novel full state vector partitioning strategy that evenly distributes the full state vector across multiple nodes and GPUs. This distributed structure enables efficient parallel gate execution without costly synchronization, and ScaleQsim applies adaptive kernel configuration, which adjusts execution parameters based on GPU resources and task granularity to enhance simulation efficiency. Our evaluation on the Perlmutter supercomputer with up to 512 GPUs demonstrates that ScaleQsim simulates quantum circuits with up to 42 qubits and outperforms state-of-the-art simulators by up to 77.40× across various quantum circuits.
Achieving high-performance computation on quantum systems presents a formidable challenge that necessitates bridging the capabilities between quantum hardware and classical computing resources. This study introduces an innovative distribution-aware Quantum-Classical-Quantum (QCQ) architecture, which integrates cutting-edge quantum software framework works with high-performance classical computing resources to address challenges in quantum simulation for materials and condensed matter physics. At the heart of this architecture is the seamless integration of VQE algorithms running on QPUs for efficient quantum state preparation, Tensor Network states, and QCNNs for classifying quantum states on classical hardware. For benchmarking quantum simulators, the QCQ architecture utilizes the cuQuantum SDK to leverage multi-GPU acceleration, integrated with PennyLane's Lightning plugin, demonstrating up to tenfold increases in computational speed for complex phase transition classification tasks compared to traditional CPU-based methods. This significant acceleration enables models such as the transverse field Ising and XXZ systems to accurately predict phase transitions with a 99.5% accuracy. The architecture's ability to distribute computation between QPUs and classical resources addresses critical bottlenecks in Quantum-HPC, paving the way for scalable quantum simulation. The QCQ framework embodies a synergistic combination of quantum algorithms, machine learning, and Quantum-HPC capabilities, enhancing its potential to provide transformative insights into the behavior of quantum systems across different scales. As quantum hardware continues to improve, this hybrid distribution-aware framework will play a crucial role in realizing the full potential of quantum computing by seamlessly integrating distributed quantum resources with the state-of-the-art classical computing infrastructure.
Quantum circuit simulation is crucial for the development of quantum algorithms, particularly given the high cost and noise limitations of physical quantum hardware. While full-state quantum circuit simulation is commonly employed for prototyping and debugging, it poses challenges because of the exponential increase in simulation time for large quantum systems. In this work, we propose an extensible framework designed to enhance simulation performance by optimizing both data locality and computational efficiency, thereby addressing these challenges. This framework is seamlessly integrated with an optimizer that restructures quantum circuits and a simulator that adjusts execution strategies for various quantum operations. For the newly developed components, merge booster and diagonal detector, the underlying algorithms are inspired by the principles of quantum entanglement and gate fusion, as well as by the limitations identified in existing third-party simulation libraries. The experiments were conducted on eight DGX-H100 workstations, each equipped with eight NVIDIA H100 GPUs, employing both gate-level and circuit-level benchmarks. The results indicate a speedup of up to 160 times for circuit-level benchmarks and an acceleration of up to 34 times for diagonal-heavy gate-level benchmarks compared to existing simulators. The proposed methodologies are anticipated to deliver more robust and faster quantum circuit simulations, thereby fostering the advancement of novel quantum algorithms.
Quantum circuit simulation is crucial for algorithm development and cryptanalysis given the limited qubit counts and high noise in today’s quantum hardware. We present a Qiskit MPI framework that accelerates classical simulation of quantum circuits focusing on Shor’s integer factoring algorithm by distributing workloads across high performance computing (HPC) resources. A novel dynamic shot allocation strategy balances simulation tasks across MPI processes, preventing idle CPUs even when the total number of circuit "shots" (independent runs) is not evenly divisible. This load balancing approach, combined with support for hybrid CPU/GPU execution on each node, achieves near linear speedups (e.g. 7.5× on 8 processes and 14× on 16 processes) in simulating Shor’s algorithm circuits. We validate the correctness of our parallel simulator by showing that distributed execution produces identical quantum output distributions as a serial run, thus maintaining fidelity. All experiments are performed with fixed random seeds and provided in open source form for reproducibility. We also discuss compatibility with the emerging Quantum Message Passing Interface (QMPI), which extends MPI to quantum computing contexts. By enabling fast, scalable simulation of cryptographically relevant algorithms like Shor’s on classical clusters, our framework allows researchers to prototype quantum attacks and assess quantum algorithm performance ahead of large scale quantum hardware. We report extensive experiments, including factoring small composite numbers, that demonstrate significant wall clock speedups without loss of accuracy. These results position our approach as a practical tool for scalable quantum algorithm prototyping and cryptanalysis research in the pre-quantum era.