Quantum computational chemistry is an emerging interdisciplinary field that combines quantum computing and computational chemistry. It aims to solve classically intractable chemistry problems, such as understanding high temperature superconductivity, transition metal catalysis, and biochemical reactions. This could lead to the design of new compounds with scientific and industrial importance. However, building a sufficiently large quantum computer remains a significant challenge. Therefore, methods that require fewer quantum resources are crucial. Quantum computing uses qubits, which can exist in superposition and entanglement states, enabling parallelism and efficient simulation of quantum systems. Classical computational chemistry, on the other hand, uses methods like Hartree-Fock, configuration interaction, and coupled cluster to approximate electronic structures. These methods are limited by computational complexity, especially for large systems. Quantum computational chemistry maps chemical problems onto quantum computers using various encoding methods, such as grid-based and basis set methods. These methods involve transforming chemical problems into quantum circuits that can be executed on quantum hardware. Key algorithms include quantum phase estimation and variational algorithms with error mitigation, which are essential for solving the electronic structure problem. The electronic structure problem, which involves finding the lowest energy states of a molecule, is central to quantum computational chemistry. It is exponentially difficult for classical computers but may be more tractable on quantum computers. Quantum simulations can provide insights into chemical reactions, molecular properties, and material science. However, current quantum computers are noisy and limited in size, making error mitigation and resource reduction critical. This review provides an overview of quantum computing and simulation, classical computational chemistry, and the methods used to map chemical problems onto quantum computers. It discusses the challenges and potential of quantum computational chemistry, including the need for error correction and the development of efficient algorithms. The review also highlights the importance of connecting quantum computing with computational chemistry to advance the field. Despite the challenges, quantum computational chemistry holds promise for solving complex chemical problems and advancing scientific understanding.Quantum computational chemistry is an emerging interdisciplinary field that combines quantum computing and computational chemistry. It aims to solve classically intractable chemistry problems, such as understanding high temperature superconductivity, transition metal catalysis, and biochemical reactions. This could lead to the design of new compounds with scientific and industrial importance. However, building a sufficiently large quantum computer remains a significant challenge. Therefore, methods that require fewer quantum resources are crucial. Quantum computing uses qubits, which can exist in superposition and entanglement states, enabling parallelism and efficient simulation of quantum systems. Classical computational chemistry, on the other hand, uses methods like Hartree-Fock, configuration interaction, and coupled cluster to approximate electronic structures. These methods are limited by computational complexity, especially for large systems. Quantum computational chemistry maps chemical problems onto quantum computers using various encoding methods, such as grid-based and basis set methods. These methods involve transforming chemical problems into quantum circuits that can be executed on quantum hardware. Key algorithms include quantum phase estimation and variational algorithms with error mitigation, which are essential for solving the electronic structure problem. The electronic structure problem, which involves finding the lowest energy states of a molecule, is central to quantum computational chemistry. It is exponentially difficult for classical computers but may be more tractable on quantum computers. Quantum simulations can provide insights into chemical reactions, molecular properties, and material science. However, current quantum computers are noisy and limited in size, making error mitigation and resource reduction critical. This review provides an overview of quantum computing and simulation, classical computational chemistry, and the methods used to map chemical problems onto quantum computers. It discusses the challenges and potential of quantum computational chemistry, including the need for error correction and the development of efficient algorithms. The review also highlights the importance of connecting quantum computing with computational chemistry to advance the field. Despite the challenges, quantum computational chemistry holds promise for solving complex chemical problems and advancing scientific understanding.