论文标题
通过基于ILP建模的量子退火来启用触觉网络资源分配
Tactile Network Resource Allocation enabled by Quantum Annealing based on ILP Modeling
论文作者
论文摘要
具有快速适应和重新配置功能的敏捷网络需要按需提供各种网络服务。 我们为基于量子计算（QC）和整数线性程序（ILP）模型提供了一个新的有方法框架，以实现实时网络自动化。我们定义了将资源提供资源提供的几乎实际ILP模型映射到二次无约束的二进制优化（QUBO）问题的方法，该问题可在量子灭火器（QA）上解决。 我们专注于三节点网络，以使用最先进的量子退火器D-WAVE优势5.2/5.3评估我们的方法及其可获得的溶液质量。通过研究退火过程，我们发现退火构型参数可获得接近经典ILP-Solver CPLEX产生的参考溶液的可行解决方案。 此外，我们研究了网络问题的扩展，并提供了量子退火器硬件要求的估算，以使较大网络的适当QUBO问题嵌入。我们在D波优势上实现了具有多达6个节点的网络的QUBO嵌入。根据我们的估计，一个具有12至16个节点的实尺寸网络需要至少50000 QUAT或更多的QA硬件。
Agile networks with fast adaptation and reconfiguration capabilities are required for on-demand provisioning of various network services. We propose a new methodical framework for short-time network optimization based on quantum computing (QC) and integer linear program (ILP) models, which has the potential of realizing a real-time network automation. We define methods to map a nearly real-world ILP model for resource provisioning to a quadratic unconstrained binary optimization (QUBO) problem, which is solvable on quantum annealer (QA). We concentrate on the three-node network to evaluate our approach and its obtainable quality of solution using the state-of-the-art quantum annealer D-Wave Advantage 5.2/5.3. By studying the annealing process, we find annealing configuration parameters that obtain feasible solutions close to the reference solution generated by the classical ILP-solver CPLEX. Further, we studied the scaling of the network problem and provide estimations on quantum annealer's hardware requirements to enable a proper QUBO problem embedding of larger networks. We achieved the QUBO embedding of networks with up to 6 nodes on the D-Wave Advantage. According to our estimates a real-sized network with 12 to 16 nodes require a QA hardware with at least 50000 qubits or more.
