Progress and Challenges on Quantum Computer-Aided Design

Prabhat Mishra

Robert Wille, Austin G. Fowler, Y. Naveh

Quantum computation is currently moving from an academic idea to a practical reality. The recent past has seen tremendous progress in the physical implementation of corresponding quantum computers - also involving big players such as IBM, Google, Intel, Rigetti, Microsoft, and Alibaba. These devices promise substantial speedups over conventional computers for applications like quantum chemistry, optimization, machine learning, cryptography, quantum simulation, and systems of linear equations. The Computer-Aided Design and Verification (jointly referred as CAD) community needs to be ready for this revolutionizing new technology. While research on automatic design methods for quantum computers is currently underway, there is still far too little coordination between the CAD community and the quantum computation community. Consequently, many CAD approaches proposed in the past have either addressed the wrong problems or failed to reach the end users. In this summary paper, we provide a glimpse into both sides. To this end, we review and discuss selected accomplishments from the CAD domain as well as open challenges within the quantum domain. These examples showcase the recent state-of-the-art but also outline the remaining work left to be done in both communities.

Song Li, Ádám Gali, Bing Qiang Huang

Optically addressable point defects in semiconductors, such as the nitrogen-vacancy (NV) center in diamond, are promising candidates for quantum information processing. However, the NV center suffers from low emission efficiency in the zero-phonon line, a limitation that has motivated the development of alternative defect systems. Since exhaustive experimental discovery is impractical, recent advances have relied on computational methods to analyze defect parameters and design color centers with tailored properties. This perspective reviews recent progress in computational methods for the theoretical characterization of color centers, highlighting workflows and toolkits for their theoretical design, and discusses remaining challenges in the field.

哲也 山崎

Lukas Burgholzer, Robert Wille

Ashfaq Iftakher, M. M. Faruque Hasan

Computer-aided Molecular and Process Design (CAMPD) is an equation-oriented multi-scale decision making framework for designing both materials (molecules) and processes for separation, reaction, and reactive separation whenever material choice significantly impacts process performance. The inherent nonlinearity and nonconvexity in CAMPD optimization models, introduced through the property and process models, pose challenges to state-of-the-art solvers. Recently, quantum computing (QC) has shown promise for solving complex optimization problems, especially those involving discrete decisions. This motivates us to explore the potential usage of quantum optimization techniques for solving CAMPD problems. We have developed a technique for directly solving a class of mixed integer nonlinear programs using QC. Our approach represents both continuous and integer design decisions by a set of binary variables through encoding schemes. This transformation allows to reformulate certain types of CAMPD problems into Quadratic Unconstrained Binary Optimization (QUBO) models that can be directly solved using quantum annealing techniques. We illustrate this technique for the selection of optimal ionic liquids (IL) and the configuration of a reactor-separator process network. We also discuss several challenges that are associated with quantum optimization when solving large scale CAMPD problems.