Quantum computing (QC) has the potential to strongly impact various sectors like finance, healthcare, communication, and technology by driving innovation across optimization and machine learning. Applications of QC in chemical, pharmaceutical, and biomolecular industries are also predicted to grow rapidly in the near future. Advancements in quantum hardware and algorithms have helped accelerate the widespread adoption of QC. Yet, despite the progress, several research gaps and challenges need to be addressed before leveraging QC for chemical engineering applications. Quantum computers offer higher computational power due to the exploitation of their quantum mechanical properties. However, not all computationally intractable problems can benefit from QC's computational abilities. Achieving speedups over classical computing with quantum algorithms implemented on current quantum devices is possible for a few specific tasks. It is imperative to identify chemical engineering problems of practical relevance that may benefit from novel quantum techniques either with current quantum computers or of the future. Here, we present an introduction to basic concepts of QC while identifying the limitations of current quantum computers. A review of quantum algorithms that may benefit optimization and machine learning in chemical engineering with current quantum computers is also provided. This work also sets expectations for quantum devices of the future by exploring similar applications that may benefit from quantum algorithms implemented on such devices.
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