Variational quantum simulation of general processes

Variational quantum algorithms have been proposed to solve static and dynamic problems of closed many-body quantum systems. Here we investigate variational quantum simulation of three general types of tasks---generalised time evolution with a non-Hermitian Hamiltonian, linear algebra problems, and open quantum system dynamics. The algorithm for generalised time evolution provides a unified framework for variational quantum simulation. In particular, we show its application in solving linear systems of equations and matrix-vector multiplications by converting these algebraic problems into generalised time evolution. Meanwhile, assuming a tensor product structure of the matrices, we also propose another variational approach for these two tasks by combining variational real and imaginary time evolution. Finally, we introduce variational quantum simulation for open system dynamics. We variationally implement the stochastic Schr\"odinger equation, which consists of dissipative evolution and stochastic jump processes. We numerically test the algorithm with a six-qubit 2D transverse field Ising model under dissipation.Comment: 18 page

Optimal Control Strategy of Turbine Governor Parameters Based on Improved Beetle Antennae Search Algorithm

Aiming at the occurrence of long-term and ultra-low frequency oscillations in the hydropower network system, this paper derives the generalized turbine transfer function speed control system model including the flow factor Tpq based on the generalized turbine model, and analyzes the influence of Tpq and PID parameters on the ultra-low frequency damping of the hydraulic turbine governing system. In order to better suppress the ultra-low frequency oscillation caused by improper PID parameter settings, a comprehensive optimization objective function reflecting damping and turbine speed deviation index (ITAE) in ultra-low frequency band is established. Based on the fast and efficient optimization strategy of Beetle Antennae Search, an improved beetle antennae particle swarm optimization is constructed. In single-machine and multi-machine systems, the improved algorithm is compared with different optimization algorithms. The simulation results show that the improved algorithm can overcome the slow convergence speed and easily fall into local optimization problem, effectively improve the damping level of hydraulic turbine governing system in ultra-low frequency, and is more effective and superior than other optimization algorithms. It provides a new way of thinking and technical means to suppress the ultra-low frequency oscillation by optimizing the parameters of the speed control system

Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration With Provable Guarantees

Satisfiability Modulo Counting (SMC) encompasses problems that require both symbolic decision-making and statistical reasoning. Its general formulation captures many real-world problems at the intersection of symbolic and statistical Artificial Intelligence. SMC searches for policy interventions to control probabilistic outcomes. Solving SMC is challenging because of its highly intractable nature($\text{NP}^{\text{PP}}$-complete), incorporating statistical inference and symbolic reasoning. Previous research on SMC solving lacks provable guarantees and/or suffers from sub-optimal empirical performance, especially when combinatorial constraints are present. We propose XOR-SMC, a polynomial algorithm with access to NP-oracles, to solve highly intractable SMC problems with constant approximation guarantees. XOR-SMC transforms the highly intractable SMC into satisfiability problems, by replacing the model counting in SMC with SAT formulae subject to randomized XOR constraints. Experiments on solving important SMC problems in AI for social good demonstrate that XOR-SMC finds solutions close to the true optimum, outperforming several baselines which struggle to find good approximations for the intractable model counting in SMC

Thirty Years of American Studies in China: An Overview and A Case Study

This paper addresses the historical development and general characteristics of American Studies in China and examines its strengths and weaknesses with a case study of an American Studies graduate program in Beijing. The paper first provides an overview of the history of American Studies in China since 1979, the year that marked the normalization of the Sino-US diplomatic relationship and the institutionalization of American Studies as an academic field. Second, it summarizes the unique characteristics of American Studies in China and lists major challenges it now faces for further development. The third part uses the American Studies Center at Beijing Foreign Studies University, a flagship graduate-level study and research program in China, as a case study to examine more closely the accomplishments of the past 30 years and the challenges faced at the current stage. A questionnaire survey conducted among its alumni and a content analysis evaluation of all available M.A. theses demonstrate that the flagship program has its language strength and unique pedagogical accomplishments. However, the program has been uneven in disciplinary development and research training. The findings suggest that for further growth, the program should design more balanced curricula, put greater emphasis on research training, and incorporate more interdisciplinary or cross-disciplinary approaches into American Studies

Graph-based Facial Affect Analysis: A Review of Methods, Applications and Challenges

Facial affect analysis (FAA) using visual signals is important in human-computer interaction. Early methods focus on extracting appearance and geometry features associated with human affects, while ignoring the latent semantic information among individual facial changes, leading to limited performance and generalization. Recent work attempts to establish a graph-based representation to model these semantic relationships and develop frameworks to leverage them for various FAA tasks. In this paper, we provide a comprehensive review of graph-based FAA, including the evolution of algorithms and their applications. First, the FAA background knowledge is introduced, especially on the role of the graph. We then discuss approaches that are widely used for graph-based affective representation in literature and show a trend towards graph construction. For the relational reasoning in graph-based FAA, existing studies are categorized according to their usage of traditional methods or deep models, with a special emphasis on the latest graph neural networks. Performance comparisons of the state-of-the-art graph-based FAA methods are also summarized. Finally, we discuss the challenges and potential directions. As far as we know, this is the first survey of graph-based FAA methods. Our findings can serve as a reference for future research in this field.Comment: 20 pages, 12 figures, 5 table

Some variational recipes for quantum field theories

Rapid developments of quantum information technology show promising opportunities for simulating quantum field theory in near-term quantum devices. In this work, we formulate the theory of (time-dependent) variational quantum simulation of the 1+1 dimensional $\lambda \phi^4$ quantum field theory including encoding, state preparation, and time evolution, with several numerical simulation results. These algorithms could be understood as near-term variational analogs of the Jordan-Lee-Preskill algorithm, the basic algorithm for simulating quantum field theory using universal quantum devices. Besides, we highlight the advantages of encoding with harmonic oscillator basis based on the LSZ reduction formula and several computational efficiency such as when implementing a bosonic version of the unitary coupled cluster ansatz to prepare initial states. We also discuss how to circumvent the "spectral crowding" problem in the quantum field theory simulation and appraise our algorithm by both state and subspace fidelities.Comment: 28 pages, many figures. v2: modified style, add references, clear typos. v3: significant change, authors adde

Contrasting response of coexisting plant's water-use patterns to experimental precipitation manipulation in an alpine grassland community of Qinghai Lake watershed, China

Understanding species-specific changes in water-use patterns under recent climate scenarios is necessary to predict accurately the responses of seasonally dry ecosystems to future climate. In this study, we conducted a precipitation manipulation experiment to investigate the changes in water-use patterns of two coexisting species (Achnatherum splendens and Allium tanguticum) to alterations in soil water content (SWC) resulting from increased and decreased rainfall treatments. The results showed that the leaf water potential (Psi) of A. splendens and A. tanguticum responded to changes in shallow and middle SWC at both the control and treatment plots. However, A. splendens proportionally extracted water from the shallow soil layer (0-10cm) when it was available but shifted to absorbing deep soil water (30-60 cm) during drought. By contrast, the A. tanguticum did not differ significantly in uptake depth between treatment and control plots but entirely depended on water from shallow soil layers. The flexible water-use patterns of A. splendens may be a key factor facilitating its dominance and it better acclimates the recent climate change in the alpine grassland community around Qinghai Lake