Chuan-Chi Wang

Quantum approximate optimization algorithm (QAOA) is one of the popular quantum algorithms that are used to solve combinatorial optimization problems via approximations. QAOA is able to be evaluated on both physical and virtual quantum computers simulated by classical computers, with virtual ones being favored for their noise-free feature and availability. Nevertheless, performing QAOA on virtual quantum computers suffers from a slow simulation speed for solving combinatorial optimization problems which require large-scale quantum circuit simulation (QCS). In this paper, we propose techniques to accelerate QCS for QAOA using mathematical optimizations to compress quantum operations, incorporating efficient bitwise operations to further lower the computational complexity, and leveraging different levels of parallelisms from modern multi-core processors, with a study case to show the effectiveness on solving max-cut problems.

10.48550/arxiv.2312.03019

Cite

Citations (0)

cuPSO

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (2022)

Chuan-Chi Wang Chun‐Yen Ho Chia-Heng Tu Shih‐Hao Hung

Particle Swarm Optimization (PSO) is a stochastic technique for solving the optimization problem. Attempts have been made to shorten the computation times of PSO based algorithms with massive threads on GPUs (graphic processing units), where thread groups are formed to calculate the information of particles and the computed outputs for the particles are aggregated and analyzed to find the best solution. In particular, the reduction-based method is considered as a common approach to handle the data aggregation and analysis for the calculated particle information. Nevertheless, based on our analysis, the reduction-based method would suffer from excessive memory accesses and thread synchronization overheads. In this paper, we propose a novel algorithm to alleviate the above overheads with the atomic functions. The threads within a thread group update the calculated results atomically to the intra-group data queue conditionally, which prevents the frequent accesses to the memory as done by the parallel reduction operations. Furthermore, we develop an enhanced version of the algorithm to alleviate the synchronization barrier among the thread groups, which is achieved by allowing the thread groups to run asynchronously and updating to the global, lock-protected variables occasionally if necessary. Our experimental results show that our proposed algorithm running on the Nvidia GPU is about 200 times faster than the serial version executed by the Intel Xeon CPU. Moreover, the novel algorithm outperforms the state-of-the-art method (the parallel reduction approach) by a factor of 2.2.

Xeon

10.1145/3477314.3507142

Cite

Citations (5)

Queen: A quick, scalable, and comprehensive quantum circuit simulation for supercomputing

arXiv (Cornell University) (2024)

Chuan-Chi Wang Yu‐Cheng Lin Yanjie Wang Chia-Heng Tu Shih‐Hao Hung

The state vector-based simulation offers a convenient approach to developing and validating quantum algorithms with noise-free results. However, limited by the absence of cache-aware implementations and unpolished circuit optimizations, the past simulators were severely constrained in performance, leading to stagnation in quantum computing. In this paper, we present an innovative quantum circuit simulation toolkit comprising gate optimization and simulation modules to address these performance challenges. For the performance, scalability, and comprehensive evaluation, we conduct a series of particular circuit benchmarks and strong scaling tests on a DGX-A100 workstation and achieve averaging 9 times speedup compared to state-of-the-art simulators, including QuEST, IBM-Aer, and NVIDIA-cuQuantum. Moreover, the critical performance metric FLOPS increases by up to a factor of 8-fold, and arithmetic intensity experiences a remarkable 96x enhancement. We believe the proposed toolkit paves the way for faster quantum circuit simulations, thereby facilitating the development of novel quantum algorithms.

Queen (butterfly)

10.48550/arxiv.2406.14084

Cite

Citations (0)

Toward cost-effective quantum circuit simulation with performance tuning techniques

Connection Science (2024)

Nai-Wei Hsu Chuan-Chi Wang Chia-Hsin Hsu Chia-Heng Tu Shih‐Hao Hung

Quantum circuit simulation is a popular approach to evaluating novel quantum algorithms before a physical quantum computer is available. Unfortunately, the simulation is often done with the full-state quantum circuit simulation scheme, and a huge memory space required by a full-state simulator is a limiting factor for the simulation of a larger qubit system. In this work, in order to support the simulation of a broadened qubit system, storage devices are introduced into the full-state quantum circuit simulation. A vertical qubit simulation design is proposed for the storage-based, full-state quantum circuit simulation, including qubit representation, threading model, and parallel state manipulations over storage devices. An empirical method of simulator parameter tuning is developed to achieve higher simulation performance. Our experimental results show that compared with the state-of-the-art memory-only simulator (QuEST), the storage-based simulation can achieve a 61x higher cost-delay ratio and can simulate a 39-qubit system on a commodity computer. The encouraging results indicate that our proposed simulator can help scale the full-state simulation for larger quantum circuits and achieve higher performance via the performance tuning method.

10.1080/09540091.2024.2349541

Cite

Citations (0)

Towards Scalable Quantum Circuit Simulation via RDMA

Chia-Hsin Hsu Chuan-Chi Wang Nai-Wei Hsu Chia-Heng Tu Shih‐Hao Hung

The trend towards optimization of quantum circuit simulators (QCS) has been driven by the rapid development of quantum computing, as there is a growing interest in creating high-efficiency QCS for quantum computing systems and applications. Despite this growth, the existing classical simulators pose challenges in simulating large quantum operations due to the exponential growth of memory and computation resources required. To overcome these issues, some studies have explored the use of non-volatile memory, such as solid-state disks (SSDs), to store a large amount of state vector while maintaining cost efficiency. However, such highly frequent read-and-write operations in large quantum simulations can quickly deplete the lifespan of SSDs, leading to overestimated practical efficiency and environmental concerns. Alternatively, Remote Direct Memory Access (RDMA) can be used to expand memory capacity, allowing computers in a network to access remote memories with low latency and high bandwidth. Therefore, we propose using one-sided RDMA operations along with a series of optimizations to expand memory capacity and efficiently maximize the utilization of unused memory assets. The experimental results demonstrate that our approaches can be practically scaled up without any lifespan consumption. The proposed optimization can get a 3.0x speedup compared to the local memory approach and a maximum speedup of 2.0x compared to the naive RDMA-based approach, as observed in our benchmark test.

Speedup

Benchmark (surveying)

Memory bandwidth

10.1145/3599957.3606215

Cite

Citations (0)

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

arXiv (Cornell University) (2022)

Chuan-Chi Wang Chun‐Yen Ho Chia-Heng Tu Shih‐Hao Hung

Particle Swarm Optimization (PSO) is a stochastic technique for solving the optimization problem. Attempts have been made to shorten the computation times of PSO based algorithms with massive threads on GPUs (graphic processing units), where thread groups are formed to calculate the information of particles and the computed outputs for the particles are aggregated and analyzed to find the best solution. In particular, the reduction-based method is considered as a common approach to handle the data aggregation and analysis for the calculated particle information. Nevertheless, based on our analysis, the reduction-based method would suffer from excessive memory accesses and thread synchronization overheads. In this paper, we propose a novel algorithm to alleviate the above overheads with the atomic functions. The threads within a thread group update the calculated results atomically to the intra-group data queue conditionally, which prevents the frequent accesses to the memory as done by the parallel reduction operations. Furthermore, we develop an enhanced version of the algorithm to alleviate the synchronization barrier among the thread groups, which is achieved by allowing the thread groups to run asynchronously and updating to the global, lock-protected variables occasionally if necessary. Our experimental results show that our proposed algorithm running on the Nvidia GPU is about 200 times faster than the serial version executed by the Intel Xeon CPU. Moreover, the novel algorithm outperforms the state-of-the-art method (the parallel reduction approach) by a factor of 2.2.

Xeon

10.48550/arxiv.2205.01313

Cite

Citations (0)

Towards Optimizations of Quantum Circuit Simulation for Solving Max-Cut Problems with QAOA

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (2024)

Yu-Cheng Lin Chuan-Chi Wang Chia-Heng Tu Shih‐Hao Hung

Quantum approximate optimization algorithm (QAOA) is one of the popular quantum algorithms that are used to solve combinatorial optimization problems via approximations. QAOA is able to be evaluated on both physical and virtual quantum computers simulated by classical computers, with virtual ones being favored for their noise-free feature and availability. Nevertheless, performing QAOA on virtual quantum computers suffers from a slow simulation speed for solving combinatorial optimization problems which require large-scale quantum circuit simulation (QCS). In this paper, we propose techniques to accelerate QCS for QAOA using mathematical optimizations to compress quantum operations, incorporating efficient bitwise operations to further lower the computational complexity, and leveraging different levels of parallelisms from modern multi-core processors, with a study case to show the effectiveness on solving max-cut problems. The experimental results reveal substantial performance improvements, surpassing a state-of-the-art simulator, QuEST, by a factor of 17 on a virtual quantum computer running on a 16-core, 32-thread AMD Ryzen 9 5950X processor. We believe that this work opens up new possibilities for accelerating various QAOA applications.

10.1145/3605098.3635897

Cite

Citations (0)

QueenV2: Future of Quantum Circuit Simulation

arXiv (Cornell University) (2024)

Chuan-Chi Wang

A state vector-based quantum circuit simulation can provide accurate results for the development and validation of quantum computing algorithms, without being affected by noise interference. However, existing quantum circuit simulators have consistently underperformed due to inadequate integration with quantum circuits and high-performance computing architectures. To tackle the challenges in quantum computing, we propose QueenV2, which builds upon the design principles of Queen and elevates performance to a new level. Experimental results on the NVIDIA RTX-4090 demonstrate that QueenV2 achieves up to a 40x improvement in gate performance and a 5x improvement in circuit performance compared to hyQuas. Furthermore, QueenV2 realizes a 137x speedup in gate benchmarks and a 14x speedup in circuit performance relative to NVIDIA cuQuantum, enabled by gate fusion via the IBM Qiskit toolkit. By eliminating reliance on third-party libraries, QueenV2 is positioned to significantly accelerate quantum circuit simulation, thus promoting the development of innovative accelerators and quantum algorithms.

10.48550/arxiv.2409.14697

Cite

Citations (0)

Chuan-Chi Wang

Papers

Citation

H-Index

i-10 index

Papers

Citation

H-Index

i-10 index