Natural evolution strategies and variational Monte Carlo

A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization. Recent work of Gomes et al. [2019] on heuristic combinatorial optimization using neural quantum states is pedagogically reviewed in this context, emphasizing the connection with natural evolution strategies. The algorithmic framework is illustrated for approximate combinatorial optimization problems, and a systematic strategy is found for improving the approximation ratios. In particular it is found that natural evolution strategies can achieve approximation ratios competitive with widely used heuristic algorithms for Max-Cut, at the expense of increased computation time

Advanced process control of coal gasification industrial process based on multiple models switching control

Coal gasification is a crucial unit of coal chemical production process. Coal and oxygen are used as the main reaction raw materials in the coal gasification process and the crude syngas is generated by chemical reaction under high temperature and high pressure. Compared with the petrochemical industry, it is more difficult to implement advanced process control (APC) in the coal gasification industrial process because of the time varying disturbance of coal quality. The time varying disturbance may lead to the mismatch of the APC model that could cause some large fluctuations of key process indices (oxygen coal ratio and gasifier temperature). In view of above problems, based on the actual production of coal-water slurry gasifier, a dynamic matrix control method based on multiple models switching is proposed in this paper. The off-line process data under different coal quality conditions are used to construct a multiple working model set for on-line dynamic matrix controller. The intergral squared error-total squared variation (ISE-TSV) is used as the controller performance index to monitor the controller performance and model mismatch and the multiple model prediction value is used as the model switching criterion. The advanced control of the coal gasification unit is realized through the multiple models switching dynamic matrix control. According to the proposed method, a multiple model switching control software Wisdom-Controller has been developed. The Wisdom-Controller has been tested on the UniSim simulation platform and applied to the real industrial gasifier. The simulation and industrial application results have verified that the proposed method can accurately control the change of oxygen-coal ratio and gasifier temperature under the condition of fluctuating coal quality conditions. Compared with the traditional manual operation, the mean square control deviation of oxygen-coal ratio and the gasifier temperature have been reduced obviously with the proposed advanced process control method based on multiple models switching. Also, the specific coal consumption has been reduced, the synthetic gas output has been increased and the economic benefit of the unit has been significantly improved. The industrial application results have verified that the proposed method provides a new and effective way to realize the advanced process control of coal gasification industrial process

Quantum-inspired variational algorithms for partial differential equations: Application to financial derivative pricing

Variational quantum Monte Carlo (VMC) combined with neural-network quantum states offers a novel angle of attack on the curse-of-dimensionality encountered in a particular class of partial differential equations (PDEs); namely, the real- and imaginary time-dependent Schr\"odinger equation. In this paper, we present a simple generalization of VMC applicable to arbitrary time-dependent PDEs, showcasing the technique in the multi-asset Black-Scholes PDE for pricing European options contingent on many correlated underlying assets