Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment
Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment
复制标题
采用量子近似优化算法 (QAOA) 进行机组承诺
DOI：
10.1109/qce52317.2021.00035
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Eskandarpour, Rozhin
中科院分区：
文献类型：
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作者：
Koretsky, Samantha;Gokhale, Pranav;Baker, Jonathan M.;Viszlai, Joshua;Zheng, Honghao;Gurung, Niroj;Burg, Ryan;Paaso, Esa Aleksi;Khodaei, Amin;Eskandarpour, Rozhin
在目前的噪声中尺度量子（NISQ），混合算法，利用经典的资源，以减少量子成本是特别有吸引力的。我们制定和应用这样的混合量子经典算法的电力系统优化问题称为机组组合，其目的是满足目标电力负荷以最小的成本。我们的算法扩展了量子近似优化算法（QAOA）与经典的极小，以支持混合二进制优化。使用Qiskit，我们模拟样本系统的结果，以验证我们的方法的有效性。我们还比较了纯粹的经典方法。我们的研究结果表明，经典的求解器是有效的，我们的模拟机组组合实例少于400发电机组。然而，对于较大的问题实例，经典的求解器要么在运行时呈指数级扩展，要么必须采用粗略的近似。这为具有数百个单位的系统打开了潜在量子优势的大门，尽管在这种规模下可能需要量子纠错。
In the present Noisy Intermediate-Scale Quantum (NISQ), hybrid algorithms that leverage classical resources to reduce quantum costs are particularly appealing. We formulate and apply such a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment, which aims to satisfy a target power load at minimal cost. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Using Qiskit, we simulate results for sample systems to validate the effectiveness of our approach. We also compare to purely classical methods. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units. However, for larger problem instances, the classical solvers either scale exponentially in runtime or must resort to coarse approximations. This opens the door to potential quantum advantage for systems with several hundred units, though quantum error correction may be necessary at this scale.
DOI：
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发表时间：
2020
期刊：
影响因子：
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作者：
Chin;E. Jones;P. Graf
通讯作者：
P. Graf