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IEEE International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST)

Cloud Quantum Resource Allocation using Game Theoretic Quantum Based Dynamic Combinatorial Auction

Rupesh Das, Soumili Pal, Punit Panda, Vishal Pradhan, Mahendra Kumar Gourisaria, Anjan Bandyopadhyay

Published Oct 25, 2024• 3 citations
Publisher

Abstract

Resource allocation in cloud quantum computing environments is a challenging problem due to the combinatorial nature of quantum resources and the strategic behavior of users. This paper presents a novel Game Theoretic Quantum Dynamic Combinatorial Auction (GTQBDCA) framework for efficient and fair allocation of quantum resources in cloud environments. By integrating game theory and dynamic combinatorial auctions, our approach addresses the unique challenges of quantum resource allocation, ensuring optimal resource utilization and user satisfaction. We demonstrate the effectiveness of our framework through theoretical analysis and simulation results.

DOI
10.1109/giest62955.2024.10959749

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Resource allocation in cloud quantum computing environments is a challenging problem due to the combinatorial nature of quantum resources and the strategic behavior of users. This paper presents a novel Game Theoretic Quantum Dynamic Combinatorial Auction (GTQBDCA) framework for efficient and fair allocation of quantum resources in cloud environments. By integrating game theory and dynamic combinatorial auctions, our approach addresses the unique challenges of quantum resource allocation, ensuring optimal resource utilization and user satisfaction. We demonstrate the effectiveness of our framework through theoretical analysis and simulation results.
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Quantum cloud computing will play a vital role in achieving quantum supremacy by providing flexible allocation of resources for applications that demand extensive computational power. Such a platform allows various clients to run their quantum jobs (quantum circuits) without managing the quantum hardware and to pay based on resource usage. Therefore, defining optimal quantum resource allocation is essential to prevent overcharging clients and to help quantum cloud providers maximize resource utilization. Our approach involves examining the problem from a game theory perspective. We introduce a quantum circuit partitioning resource allocation game model (QC-PRAGM) designed to minimize client costs and inter-node communication. This is achieved by solving a convex optimization problem that finds the system's optimal cost while maximizing the number of local gates within a partition through the selection of the best qubit combinations. We demonstrate analytically that clients are charged fairly. Additionally, our simulations show that our solutions outperform traditional methods.
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This paper presents a novel quantum-inspired algorithm designed for optimal resource allocation within cloud computing environments. Recognizing the potential of quantum superposition to explore multiple solutions concurrently, the proposed algorithm simulates this effect through a weighted sum approach. The core of the method involves evaluating various resource allocation scenarios simultaneously, assigning weights reflecting the desirability of each outcome. This parallel exploration significantly reduces the computational time required to find the optimal allocation compared to traditional, sequential methods. The algorithm's effectiveness is demonstrated through a theoretical framework and a conceptual implementation, highlighting its potential for substantial efficiency gains in dynamic cloud resource management. The algorithm aims to capture the essence of quantum exploration without the need for actual quantum hardware, offering a pragmatic solution for complex optimization problems.