The Potential of Quantum Computing Algorithms for Solving Chemistry Problems

Jie Liu, Huan Ma, Honghui Shang +2 more

High performance computing (HPC) is renowned for its capacity to tackle complex problems. Meanwhile, quantum computing (QC) provides a potential way to accurately and efficiently solve quantum chemistry problems. The emerging field of quantum-centric high performance computing (QCHPC), which merges these two powerful technologies, is anticipated to enhance computational capabilities for solving challenging problems in quantum chemistry. The implementation of QCHPC for quantum chemistry requires interdisciplinary research and collaboration across multiple fields, including quantum chemistry, quantum physics, computer science and so on. This perspective provides an introduction to the quantum algorithms that are suitable for deployment in QCHPC, focusing on conceptual insights rather than technical details. Parallel strategies to implement these algorithms on quantum-centric supercomputers are discussed. We also summarize high performance quantum emulating simulators, which are considered a viable tool to explore QCHPC. We conclude with challenges and outlooks in this field.

Panagiotis Kl. Barkoutsos, Jérôme F. Gonthier, Nikolaj Moll +3 more

Ruby Srivastava, Sravani Joshi

Computational chemistry plays a vital role in material sciences. A fast evolving technology called quantum computing has shifted computational capabilities as it promises to solve impossible calculations in some areas of material sciences and drug development. In principle, quantum computers can solve certain problems in chemistry exponentially more quickly than classical computers. As quantum computers have an error-corrected quantum system, it reduces the quantum resources to run the quantum calculations in space drastically using the Trotterization approach with small qubitization. This nascent field is showing fast progress and potential benefits as it can process large amounts of information. The algorithms used for optimization and quantum simulations help to improve the advanced computational calculations for large clusters. These algorithms help in protein structure prediction to network analysis. Yet this nascent field has its own caveats and challenges. In this chapter, we introduce the basic architecture of quantum computing and its promises and limitations in the field of computational chemistry. The quantum algorithms revolutionizing quantum computing are discussed in detail.

Vasileios Armaos, Dimitrios A. Badounas, Paraskevas Deligiannis +1 more

Alexander M. Dalzell, Sam McArdle, Mario Berta +10 more

The 1994 discovery of Shor's quantum algorithm for integer factorization—an important practical problem in the area of cryptography—demonstrated quantum computing's potential for real-world impact. Since then, researchers have worked intensively to expand the list of practical problems that quantum algorithms can solve effectively. This book surveys the fruits of this effort, covering proposed quantum algorithms for concrete problems in many application areas, including quantum chemistry, optimization, finance, and machine learning. For each quantum algorithm considered, the book clearly states the problem being solved and the full computational complexity of the procedure, making sure to account for the contribution from all the underlying primitive ingredients. Separately, the book provides a detailed, independent summary of the most common algorithmic primitives. It has a modular, encyclopedic format to facilitate navigation of the material and to provide a quick reference for designers of quantum algorithms and quantum computing researchers.

Philipp Schleich, Luis Mantilla Calderón, Chong Sun +4 more