A distributed deep reinforcement learning framework based on GPU cluster was constructed to solve the computational problems of large-scale quantum chemistry simulation. The framework includes technologies such as parallel strategy optimization, asynchronous gradient update, and dynamic scheduling tasks. Efficient communication algorithms are adopted to maximize the utilization of hardware resources and achieve scaling. Experimental evaluation of representative quantum chemistry problems was performed on a multi-node GPU platform. The results show that the average GPU utilization rate exceeds 92% due to the near-linear scale expansion to 128 GPUs. This improves the simulation throughput and efficiency. The system has good performance and convergence behavior for a variety of workloads. The results show that these GPUs, matched with advanced improved distributed DRL models, can break thru the traditional bottleneck of using supercomputers to solve complex physical and chemical problems, so as to complete all these dazzling and complex molecules faster and better.
A distributed deep reinforcement learning framework based on GPU cluster was constructed to solve the computational problems of large-scale quantum chemistry simulation. The framework includes technologies such as parallel strategy optimization, asynchronous gradient update, and dynamic scheduling tasks. Efficient communication algorithms are adopted to maximize the utilization of hardware resources and achieve scaling. Experimental evaluation of representative quantum chemistry problems was performed on a multi-node GPU platform. The results show that the average GPU utilization rate exceeds 92% due to the near-linear scale expansion to 128 GPUs. This improves the simulation throughput and efficiency. The system has good performance and convergence behavior for a variety of workloads. The results show that these GPUs, matched with advanced improved distributed DRL models, can break thru the traditional bottleneck of using supercomputers to solve complex physical and chemical problems, so as to complete all these dazzling and complex molecules faster and better.
In Distributed Quantum Computing (DQC), quantum networks interconnect multiple Quantum Processing Units (QPUs) to overcome single-device limitations and enable cross-server computation. We present QTest, a modular simulation testbed for distributed quantum computing and networking. The platform employs Deep Reinforcement Learning (DRL)-based quantum circuit partitioning and distributed quantum network scheduling, coordinating cross-server operations under network constraints such as routing and channel utilization. With containerized QPU nodes and GPU-accelerated backends, QTest supports circuit execution across heterogeneous and geographically distributed environments. By enabling task partitioning, inter-node entanglement distribution, fidelity-aware routing, and latency-throughput evaluation, QTest provides a reproducible and extensible environment for protocol development and validation. Experimental results show that QTest achieves efficient runtime scalability under ideal conditions. Compared to CPU-based execution, GPU-accelerated distributed runs reduce execution time by up to an order of magnitude, validating the effectiveness of the proposed testbed.
Quantum computing holds great potential to accelerate the process of solving complex combinatorial optimization problems. The Distributed Quantum Approximate Optimization Algorithm (DQAOA) addresses high-dimensional, dense problems using current quantum computing techniques and high-performance computing (HPC) systems. In this work, we improve the scalability and efficiency of DQAOA through advanced problem decomposition and parallel execution using message passing on the Frontier CPU/GPU supercomputer. Our approach ensures efficient quantum-classical workload management by distributing large problem instances across classical and quantum resources. Experimental results demonstrate that enhanced decomposition strategies and GPU-accelerated quantum simulations significantly improve DQAOA's performance, achieving up to 10x speedup over CPU-based simulations. This advancement enables better scalability for large problem instances, supporting the practical deployment of GPU systems for hybrid quantum-classical applications. We also highlight ongoing integration efforts using the Quantum Framework (QFw) to support future HPC-quantum computing systems.
Predicting the chemico-physical properties of large molecular systems is a formidable challenge in chemistry and materials science. Traditional quantum mechanical methods, while accurate, have impractical scaling for large molecules with thousands of atoms, which are crucial in the development of novel therapeutics, catalysts, and nanomaterials. To address this, molecular fragmentation algorithms have been proposed to improve scalability and enable extensive parallelism. In this article, we introduce a significant enhancement to the Fragment Molecular Orbital (FMO) method, termed the Coulomb-Perturbed Fragmentation (CPF) method. CPF incorporates algorithmic improvements and implementation enhancements to optimize performance on heterogeneous computing systems equipped with a large number of GPUs. Key developments include a significant simplification of iteratitve self-consistent field (SCF) algorithm, advanced data management through a one-sided communication model, topology-aware optimizations, and a hybrid communication strategy for intra-group exchanges. Moreover, CPF integrates a distributed dynamic multi-layer load balancing scheme to optimise fragment distribution and workload management across nodes and GPUs. Performance evaluations on a 420-atom benzene molecule system comprising 35 fragments reveal that CPF outperforms existing GPU/CPU-based FMO algorithms in both efficiency and accuracy. When deployed on the Gadi supercomputer, CPF achieves over 97% parallel efficiency on 20 nodes, with scalability maintaining above 98% and 90% efficiency in weak-scaling tests for smaller and larger systems, respectively. Notably, CPF matches or exceeds the computational accuracy of conventional FMO methods, marking a substantial progress in the field of computational chemistry for fragmentation-based large-scale molecular modelling.
This paper explores the application of Deep Reinforcement Learning (DRL) for optimizing quantum resource allocation in quantum computing. Traditional quantum resource allocation often relies on manual design and expert knowledge, which can be computationally expensive and limit scalability. We propose a novel framework utilizing DRL to automate this process, learning optimal strategies for allocating quantum resources (qubits, gates, and time) to maximize the performance of quantum algorithms. The core of our approach involves constructing a DRL model where the agent learns to make decisions regarding resource allocation based on the current state of the quantum system and the desired algorithm outcome. We define a state space representing the quantum system's configuration, action space encompassing various resource allocation options, and a reward function that reflects the algorithm's success. Through this framework, we aim to significantly enhance the efficiency and performance of quantum computations. This work presents a foundational approach, paving the way for more sophisticated DRL-based quantum resource allocation strategies.
We explore the industrial and scientific applicability of the VQE-LSTM framework by integrating meta-learning with GPU accelerated quantum simulation using NVIDIA's CUDA-Q (CUDAQ) platform. This work demonstrates how an LSTM-FC meta-initialization module can extend the practical reach of the Variational Quantum Eigensolver (VQE) in both chemistry and physics domains. In the chemical regime, the framework predicts ground-state energies of molecular Hamiltonians derived from PySCF, achieving near FCI accuracy while maintaining favorable O(N^2) scaling with molecular size. In the physical counterpart, we applied the same model to quantized Simple Harmonic Motion systems (SHM), successfully reproducing its ground and excited states through VQE and Variational Quantum Deflation (VQD) methods. Benchmark results on NVIDIA GPUs reveal significant speedups over CPU-based implementations, validating CUDAQ's capability to handle large-scale variational workloads efficiently. Overall, this study establishes VQE-LSTM as a viable and scalable approach for GPU accelerated quantum simulation, bridging quantum chemistry and condensed-matter physics through a unified, meta-learned initialization strategy.
In this paper, we introduce Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning (Dist-QTRL), a novel approach to addressing the scalability challenges of traditional Reinforcement Learning (RL) by integrating quantum computing principles. Quantum-Train Reinforcement Learning (QTRL) leverages parameterized quantum circuits to efficiently generate neural network parameters, achieving a poly(log(N))reduction in the dimensionality of trainable parameters while harnessing quantum entanglement for superior data representation. The framework is designed for distributed multi-agent environments, where multiple agents, modeled as Quantum Processing Units (QPUs), operate in parallel, enabling faster convergence and enhanced scalability. Additionally, the Dist-QTRL framework can be extended to high-performance computing (HPC) environments by utilizing distributed quantum training for parameter reduction in classical neural networks, followed by inference using classical CPUs or GPUs. This hybrid quantumHPC approach allows for further optimization in real-world applications. In this paper, we provide a mathematical formulation of the Dist-QTRL framework and explore its convergence properties, supported by empirical results demonstrating performance improvements over centric QTRL models. The results highlight the potential of quantum-enhanced RL in tackling complex, highdimensional tasks, particularly in distributed computing settings, where our framework achieves significant speedups through parallelization without compromising model accuracy. This work paves the way for scalable, quantum-enhanced RL systems in practical applications, leveraging both quantum and classical computational resources.