Quantum computing (QC) has made it possible for optimization and machine learning to get better. These improvements could have big effects on many areas, like medicine, technology, communication, and finance. There will probably soon be a huge rise in the use of QC in the chemistry, pharmaceutical, and bio-molecular fields. Improvements in quantum hardware and software have sped up the process of putting QC into action. It is very important to find real-life chemical engineering problems that cutting-edge quantum methods could help solve, no matter if they are used in computers today or in the future. The authors go over some basic QC ideas while also talking about the problems with current quantum computers. There is also an outline of quantum algorithms that, when used with current quantum computers, could help chemical engineers with machine learning and optimization. There are also plans for future quantum devices because this research looks into linked uses that could use quantum algorithms run on them.
Quantum computing (QC) has made it possible for optimization and machine learning to get better. These improvements could have big effects on many areas, like medicine, technology, communication, and finance. There will probably soon be a huge rise in the use of QC in the chemistry, pharmaceutical, and bio-molecular fields. Improvements in quantum hardware and software have sped up the process of putting QC into action. It is very important to find real-life chemical engineering problems that cutting-edge quantum methods could help solve, no matter if they are used in computers today or in the future. The authors go over some basic QC ideas while also talking about the problems with current quantum computers. There is also an outline of quantum algorithms that, when used with current quantum computers, could help chemical engineers with machine learning and optimization. There are also plans for future quantum devices because this research looks into linked uses that could use quantum algorithms run on them.
Abstract Quantum computing has been attracting public attention recently. This interest is driven by the advancements in hardware, software, and algorithms required for its successful usage and the promise that it entails the potential acceleration of computational tasks compared to classical computing. This perspective article presents a short review on quantum computing, how this computational approach solves problems, and three fields that quantum computing can potentially impact the most while relevant to chemical engineering: computational chemistry, optimization, and machine learning. Here, we present a series of chemical engineering applications, the developments, potential improvements with respect to classical computing, and challenges that quantum computing faces for each of these fields. This article intends to provide a clear picture of the challenges and potential advantages that quantum technology may yield for chemical engineering, together with an invitation for our colleagues to join us in the adoption and development of quantum computing.
Quantum simulations are changing the way drugs are found and materials are studied because they can model chemical structures and interactions with a new level of accuracy. This study looks at how to use quantum computing techniques, like Variational Quantum Eigensolvers (VQE) and Quantum Approximate Optimisation Algorithms (QAOA), to make traditional methods better at modelling complex chemical systems. Using quantum physics, these models make it possible to accurately predict molecule traits, reaction paths, and energy states. This speeds up the creation of new drugs and materials. Quantum models help find lead chemicals in drug development by correctly modelling how proteins and ligands interact, how strongly they bind, and how reactions happen. In material science, they make it possible to create new materials with specific qualities, like high-performance catalysts, ways to store energy, and long-lasting materials that can be used in industry. Quantum computing and artificial intelligence work well together to make these models more accurate and scalable, which means they can be used on a big scale. This study also looks at problems like quantum technology limits, fixing mistakes, and making algorithms work better. It suggests combining quantum and traditional methods to make computing more efficient. Case studies show that quantum models can be used to solve real-world problems, such as creating focused medicines and materials that are good for the environment. The results show that quantum models have the power to change everything and speed up creativity in many fields. This study shows how quantum computing can change the future of molecular science by connecting theory and practice. It could lead to big steps forward in drug development and material design that help solve important world problems.
As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this Perspective, a view of the current state of affairs in this new and exciting research field is offered, challenges of using machine learning in quantum chemistry applications are described, and potential future developments are outlined. Specifically, examples of how machine learning is used to improve the accuracy and accelerate quantum chemical research are shown. Generalization and classification of existing techniques are provided to ease the navigation in the sea of literature and to guide researchers entering the field. The emphasis of this Perspective is on supervised machine learning.
Quantum computers are recently being developed in wide varieties, but the computational results from quantum computing have been largely confined to constructing artificial assignments. The applications of quantum computers to real-world problems are still an active area of research. However, challenges arise when the limits of scale and complexity in biological problems are pushed, which has affected drug discovery. The fast-evolving quantum computing technology has transformed the computational capabilities in drug research by searching for solutions for complicated and tedious calculations. Quantum computing (QC) is exponentially more efficient in drug discovery, treatment, and therapeutics, generating profitable business for the pharmaceutical industry. In principle, it can be stated that quantum computing can solve complex problems exponentially faster than classical computing. Here it is needed to mention that QC will not be able to take on every task that classical computers perform—at least not now. It may be classical and quantum-coupled computational technologies combined with machine learning (ML) and artificial intelligence (AI) will solve each task in the future. This review is an overview of quantum computing, which may soon revolutionize the pharmaceutical industry in drug discovery.
Quantum computers are recently being developed in wide varieties, but the computational results from quantum computing have been largely confined to constructing artificial assignments. The applications of quantum computers to real-world problems are still an active area of research. However, challenges arise when the limits of scale and complexity in biological problems are pushed, which has affected drug discovery. The fast-evolving quantum computing technology has transformed the computational capabilities in drug research by searching for solutions for complicated and tedious calculations. Quantum computing (QC) is exponentially more efficient in drug discovery, treatment, and therapeutics, generating profitable business for the pharmaceutical industry. In principle, it can be stated that quantum computing can solve complex problems exponentially faster than classical computing. Here it is needed to mention that QC will not be able to take on every task that classical computers perform—at least not now. It may be classical and quantum-coupled computational technologies combined with machine learning (ML) and artificial intelligence (AI) will solve each task in the future. This review is an overview of quantum computing, which may soon revolutionize the pharmaceutical industry in drug discovery.
The integration of quantum computing and artificial intelligence (AI), collectively termed Quantum AI, is revolutionizing the pharmaceutical industry by reshaping the paradigms of drug discovery and development. Traditional approaches to drug design are often limited by high costs, extensive timelines, and the computational intractability of simulating complex molecular interactions. Quantum computing offers exponential processing power, enabling the accurate modeling of quantum-level chemical phenomena that classical computers struggle to handle. Concurrently, AI contributes by enhancing data interpretation, prediction of molecular behaviors, and pattern recognition across large biological datasets. Together, Quantum AI enables faster identification of drug candidates, improved simulation of drug-target interactions, and early prediction of pharmacokinetics and toxicity. Key quantum algorithms, such as the Variational Quantum Eigensolver (VQE) and quantum-enhanced machine learning, have demonstrated potential to outperform conventional methods in quantum chemical calculations. Moreover, this synergy facilitates personalized medicine by integrating patient-specific data to optimize therapeutic outcomes. Despite current challenges related to hardware scalability, algorithm optimization, and ethical integration, the future trajectory of Quantum AI is poised to redefine pharmaceutical innovation. This review underscores the transformative capabilities of Quantum AI and its potential to usher in a new era of efficient, cost-effective, and precision-driven drug development.