Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems. It is especially promising for Distributed Energy Resources (DERs) dominant systems that have been widely developed to promote energy sustainability. In those systems, knowing the maximum sections of power and data delivery is essential for monitoring, operation, and control. However, high computational effort is required. By leveraging quantum resources, Quantum Approximate Optimization Algorithm (QAOA) provides a means to search for these sections efficiently. However, QAOA performance relies heavily on critical parameters, especially for weighted graphs. Here we present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density. We verify the strategy with 39,774 expectation value calculations. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots its practical application to power systems in noisy intermediate-scale quantum devices.
Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems. It is especially promising for Distributed Energy Resources (DERs) dominant systems that have been widely developed to promote energy sustainability. In those systems, knowing the maximum sections of power and data delivery is essential for monitoring, operation, and control. However, high computational effort is required. By leveraging quantum resources, Quantum Approximate Optimization Algorithm (QAOA) provides a means to search for these sections efficiently. However, QAOA performance relies heavily on critical parameters, especially for weighted graphs. Here we present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density. We verify the strategy with 39,774 expectation value calculations. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots its practical application to power systems in noisy intermediate-scale quantum devices.
Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems, especially for Distributed Energy Resources (DERs) dominant cyber-physical systems that have been widely developed to promote energy sustainability. The systems’ maximum power or data sections are essential for monitoring, operation, and control, while high computational effort is required. Quantum Approximate Optimization Algorithm (QAOA) provides a promising means to search for these sections by leveraging quantum resources. However, its performance highly relies on the critical parameters, especially for weighted graphs. We present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density, and verify the strategy with 39, 774 instances. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots the practical application of quantum technique to power systems in noisy intermediate-scale quantum devices, heralding its next-generation computation in the quantum era.
The Quantum Approximate Optimization Algorithm (QAOA) is applied to Networked Microgrids (NMs) in this paper to search for the maximum power exchange section, which is playing an essential role in operating NMs. Mathematically, obtaining the maximum power section is to solve a Max-Cut problem over the modeling graph of NMs. Considering the integration and fluctuations of Distributed Energy Resources (DERs), the maximum power section will change frequently. To efficiently get the section, QAOA provides a powerful solution by leveraging quantum resources. The performance of QAOA highly depends on the critical parameters of quantum circuits. We find the designed parameters for QAOA are still effective under wide range change of output power of energy resources. Tests on a typical NMs system verify the effectiveness of the QAOA method in efficiently searching for the maximum power sections of NMs.
With the significant expansion of renewable energy integration, the scale of the power grid also increases rapidly. To effectively monitor the operational state of large-scale power grids, optimizing the placement of Phasor Measurement Units (PMUs) is a critical research focus. Optimizing PMU Placement (OPP) is a typical combinatorial optimization problem with NP-hard complexity, which brings substantial challenges to classical computers. The sheer scale of modern grids forces classical solvers into prohibitive runtimes and sub-optimal local minima, degrading both execution speed and solution quality. Recent advances in quantum computing have opened new opportunities for tackling combinatorial optimization problems, particularly through the Quantum Approximate Optimization Algorithm (QAOA). However, QAOA operates as a hybrid quantum–classical framework, where determining the optimal parameters depends on a classical optimization process that remains computationally challenging and inherently NP-hard. On the other hand, in the Noisy Intermediate-Scale Quantum (NISQ) era, obtaining optimal optimization results typically requires a large number of quantum measurement shots. In this work, we propose a graph-learning-based strategy to provide QAOA with guided parameter initialization, enabling effective operation under limited quantum resources, particularly when the number of available measurement shots is restricted. Both the OPP problem and the channel-limitation task are investigated, where the proposed graph-learning-based parameter predictor enhances QAOA performance on both tasks, improving both the approximation ratio and computational efficiency. Furthermore, due to the complexity of the channel-limitation task and the scarcity of its pretraining data, a transfer learning strategy is employed to leverage knowledge from the original OPP task, where QAOA parameter datasets are more readily available to train the graph learning framework for the QAOA parameter predictor. The transfer learning approach also outperforms both random initialization and graph learning trained solely on the channel-limitation dataset in terms of the approximation ratio and the time efficiency. In general, this work is aimed at providing a new benchmark for solving complicated real-world power system optimization problems in the current NISQ era.
Abstract Noisy intermediate-scale quantum (NISQ) devices are cutting-edge technology expected to demonstrate potential and advantages of quantum computing over classical computing. Its low number of qubits and imperfection from noises restrict running full-scale quantum algorithms on such devices; however, quantum advantages can still be obtained. To achieve quantum advantages from NISQ devices, the hybrid quantum-classical algorithms were introduced. Quantum approximate optimization algorithm (QAOA) is a variational hybrid algorithm, which utilizes a NISQ device as a sub-unit for specific tasks and performs most calculations on a classical computer. QAOA provides an approximate solution, with arbitrary precision as the number of operations increases, for optimization problems. In this work we investigate the possibility of applying QAOA to a clustering problem and compare its performance with the classical k-means algorithm. It turns out that the weights in graph connectivity can degrade the algorithm operation and make it more difficult to approximate the solution. We also benchmark the QAOA by comparing the approximated solutions with the exact one obtained from a classical clustering algorithm.
The Quantum Approximate Optimization Algorithm (QAOA) is one of the promising near-term algorithms designed to find approximate solutions for combinatorial optimization problems. The algorithm prepares a parametrized state that is aimed to maximize the expectation value of the objective function of the problem. The circuit for QAOA consists of p layers, and it depends on 2p parameters, the determination of which can be carried out using a variational quantum eigensolver (VQE) subroutine. This is a variational approach for finding optimal parameters, where the quantum state is prepared with a quantum computer, and a classical computer is used for calculating the corresponding objective function value and performing an outer loop optimization routine in order to find optimal parameters. Earlier work on the algorithm has shown that QAOA is closely related to the Quantum Adiabatic Algorithm, which results in noticeable patterns in the optimal parameters. To this purpose, two methods, INTERP and FOURIER, were proposed to exploit this structure by Zhou et al. (2018). In this thesis, I extend upon these results by examining the effectiveness of one of their methods, the INTERP method, on the Max-Cut problem from graph theory. The performance of QAOA using this method was studied on several classes of graphs and benchmarked against the currently best-known classical algorithm Goemans-Williamson, which achieves an approximation ratio of ρ ≈ 0.878 on arbitrary graphs. The classes of graphs examined include cyclic, weighted and unweighted 3-regular, and Erdős–Rényi graphs with edge probabilities of 0.5 and 0.75 and graph sizes ranging from 4 to 16. Moreover, it was investigated if the INTERP method offers an advantage compared to random initialization of parameters and whether or not the method is polynomial in p. It was found that different graph classes lead to similar but different parameter patterns. Furthermore, the performance of QAOA using the INTERP method was dependent on the graph class considered. For the small graphs investigated it was found that QAOA outperforms Goemans-Williamson on ER-0.50, ER-0.75 and weighted 3-regular graphs from p=7 and beyond with respect to how well the expectation of the objective function approximates the optimal value, while for unweighted 3-regular graphs p=6 suffices. However, the calculation of the parameters is costly and requires a number of function evaluations that is on the order of the number of possible bipartitions of small graph with sizes n ≤ 16. Moreover, the numerical results suggested that indeed the method is polynomial in p advocating that the algorithm might be a strong alternative to Goemans-Williamson for large graph sizes, once hardware allows it.
The rapid development of quantum computing has provided a ground-breaking tool for solving problems that are insurmountable on classical computers. In the power system domain, quantum computing is still in the nascent stage, with limited but ongoing studies. The Quantum Approximation Optimization Algorithm (QAOA) has been applied in solving several NPhard problems and can be further studied to address challenging problems in power systems. Key applications include contingency analysis, optimal power flow, and unit commitment, among others. With the significant integration of renewable energy, the intermittent nature of renewable energy introduces substantial volatility to the system. An overhead contingency analysis is required to evaluate the possible system outage scenarios, which will identify critical contingencies to maintain system stability. However, the contingency analysis is a prototypical NP-Hard problem, requiring substantial classical computation resources. In this work, QAOA is developed for theN−kcontingency analysis on line contingency as well as the general contingency scenarios, with the aim of minimizing the shedding of the power load while ensuring the power flow constraints. A tailored objective function is developed to encode theN−kcontingency condition as well as the power flow loss constraints into a problem Hamiltonian for QAOA computation. The developed method is verified on IEEE 9- and 14-bus systems compared with classical methods as the baseline. In addition, a QAOA parameter study including the number of QAOA parameters, the number of repetitions of the quantum circuits execution, and the number of iteration steps of the classical optimization algorithm, is conducted to offer insights for the parameter tuning under different problem sizes. This work is anticipated to establish a quantum framework for solving critical and complex power system problems in the Noisy intermediate-scale quantum (NISQ) era.