Advanced Optimization Technologies Research Articles

Quantum Circuit Template Matching Optimization Method for Constrained Connectivity

The execution of quantum algorithms requires two key considerations. On the one hand, it should meet the connectivity constraint requirements of quantum circuit mapping for quantum architectures, and on the other hand, it needs to consider reducing the probability of errors in the execution of quantum circuits as much as possible. This paper proposes a novel optimization technique based on template matching that to satisfy both requirements. The template matching optimization method can significantly reduce the number of gates in a quantum circuit and further enhance its practicality. It stands as advanced optimization technology available today. Our method optimizes quantum logic circuits mapped onto quantum architecture by initially selecting their linear substructure. We then zone the circuit according to the gate dependency graph and optimize each block through template matching. Finally, we reorganize the circuit to obtain the optimized version as the final result. Our proposed method is amenable to various quantum architectures. To evaluate its efficacy, we conduct a comparative analysis with the t|ket⟩ and Qiskit compiler using a set of benchmark test circuits. Specifically, compare to the t|ket⟩ compiler method, the highest average optimization rate of our method can reach 25.75%. Compare with the Qiskit compiler method, the highest average optimization rate can reach 32.72%. Overall, our approach has significant optimization advantages.

Bi-level optimization for the energy conversion efficiency improvement in a photocatalytic-hydrogen-production system

Industrialization of the photocatalytic hydrogen production technology is plagued by the low energy conversion efficiency. To address this challenge, this study simultaneously optimizes the structure of the compound parabolic collector (CPC) and the operating conditions of the photoreactor to match the solar flow on the reactor surface and the energy required for chemical reaction at the catalyst interface by building a novel bi-level optimization model with partial differential equation (PDE) constraints. The Sobol method is used to identify the operating parameters that significantly influence the energy conversion efficiency. A new extreme learning machine (ELM) is developed as a surrogate model to approximate the relationship between the operating parameters of the reactor and hydrogen production, reducing computational load. A new nested algorithm is developed to solve the built bi-level optimization model. Numerical results indicate that the new method can find the optimal CPC structure and operating conditions for the photoreactor, achieving an increase in energy conversion efficiency as compared to the results from the systems with un-optimization, optimized CPC structure only and optimized reactor operating parameters only. Our study achieves the synergy of the CPC and photoreactor by combining the advanced optimization technology, machine learning and energy conversion process mechanisms.

Review of Recent Offshore Wind Turbine Research and Optimization Methodologies in Their Design

As international efforts to address climate change grow, an increasing number of countries and companies have put forward a clear “net zero” goal through accelerated renewable-energy development. As a renewable energy source, offshore wind energy has received particular attention from many countries and is a highly active research area. However, the design of offshore wind turbine structures faces challenges due to the large and complex design parameter space as well as different operational requirements and environmental conditions. Advanced optimization technology must be employed to address these challenges. Using an efficient optimization algorithm, it is possible to obtain optimized parameters for offshore wind turbine structures, balancing energy generation performance and the life of the floating wind turbine. This paper presents a review of the types and fundamental principles of several critical optimization technologies along with their application in the design process, with a focus on offshore wind turbine structures. It concludes with a discussion of the future prospects of optimization technology in offshore wind research.

Subspace-Projection-Based Geometric Unmixing for Material Quantification in Hyperspectral Imagery

Linear spectral unmixing is a widely used technique in hyperspectral remote sensing to quantify materials present in an image pixel. In order to produce accurate estimates of abundances, nonnegativity constraint and sum-to-one constraint must be imposed on the abundances of materials. Under these two constraints, linear spectral unmixing is often formulated as a convex optimization problem that requires more advanced optimization technology, leading to excessive computational complexity. In this paper, a novel geometric method is presented for solving the fully constrained linear spectral unmixing problem. Specifically, abundances are first expressed as the ratios of signed volumes of simplexes. Then, Laplace expansion is applied in the process of determinant calculation, which derives a new low-complexity abundance estimation method. Furthermore, the mixed pixel outside the simplex is iteratively projected onto the facet planes through the endmember vertices for making the abundances satisfy the nonnegativity constraint. This process is continued until one finds a projected point lying inside the simplex. The proposed method is in line with the least squares criterion. Experimental results based on simulated and the AVIRIS Cuprite data sets demonstrate the superiority of the proposed algorithm with respect to other state-of-the-art approaches.

Tachyon Common Lisp

Tachyon Common Lisp is an efficient and portable implementation of Common Lisp 2nd Edition. The design objective of Tachyon is to apply both advanced optimization technology developed for RISC processors and Lisp optimization techniques. The compiler generates very fast codes comparable to, and sometimes faster than the code generated by UNIX C compiler. Comparing with the most widely used commercial Common Lisp, Tachyon Common Lisp compiled code is 2 times faster and the interpreter is 6 times faster than the Lisp in Gabriel benchmark suit. Tachyon Common Lisp is the fastest among the Lisp systems known to the authors.