- Hybrid Quantum-HPC Solutions for Max-Cut: Bridging Classical and . . .
This research explores the integration of the Quantum Approximate Optimization Algorithm (QAOA) into hybrid quantum-HPC systems for solving the Max-Cut problem, comparing its performance with classical algorithms like brute-force search and greedy heuristics
- Simulations of Quantum Approximate Optimization Algorithm on HPC-QC . . .
This study analyzes QAOA’s performance using various quantum simulators (e g , density matrix, statevector, and matrix product state) and demonstrates the benefits of HPC-QC integrated systems in solving QUBO problems on an active learning workflow
- QAOA in Quantum Datacenters: Parallelization, Simulation, and . . .
Abstract: Scaling quantum computing requires networked systems, leveraging HPC for distributed simulation now and quantum networks in the future Quantum datacenters will be the primary access point for users, but current approaches demand extensive manual decisions and hardware expertise
- Qoro Quantum and CESGA Demonstrate Distributed Quantum Circuit . . .
The project tested distributed implementations of VQE and QAOA, using Qoro’s Divi software to generate and schedule thousands of quantum circuits for simulation on CESGA’s infrastructure
- Bridging Classical and Quantum with SDP initialized warm-starts for QAOA
We study the Quantum Approximate Optimization Algorithm (QAOA) in the context of the Max-Cut problem Noisy quantum devices are only able to accurately execute QAOA at low circuit depths, while classically-challenging problem instances may call for a relatively high circuit-depth
- GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems
The Distributed Quantum Approximate Optimization Algorithm (DQAOA) addresses high-dimensional, dense problems using current quantum computing techniques and high-performance computing (HPC) systems
- Quantum Approximate Optimization Algorithm (QAOA)
QAOA stands as a significant milestone in quantum computing, especially in its integration with High-Performance Computing (HPC) This integration represents a strategic blend of quantum computing's problem-solving prowess with HPC's computational power, particularly in optimizing combinatorial tasks
- GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems
The Distributed Quantum Approximate Optimization Algorithm (DQAOA) addresses high-dimensional, dense problems using current quantum computing techniques and high-performance computing (HPC) systems
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