Quantum Computing Algorithms: Harnessing The Power of Quantum Supremacy

Abstract

Radically new in paradigm quantum computing can address complexity related problems that may be intractable even to the best classical supercomputers. The core of this progress is quantum supremacy; when a quantum computer could be able to solve a problem which the classical computer would need an unimaginable amount of time to solve. In this abstract, the landscape of quantum computing algorithms is considered and their ability to take advantage of the laws of quantum mechanics (superposition, entanglement and quantum interference) to be more computationally efficient than their classical counterparts to reassess what is computable in practice. In contrast to classical quantum algorithms, quantum algorithms are coded to run on quantum bits: bits of information which can exist in a state of superposition. This allowsdestination-over extreme parallelism and arbitrarily expedited speed-ups of certain kinds of problems. The most significant of these algorithms is the one by Shor to address the integer factorization problem which solves the cryptographic techniques of classical encryption, by bringing the issue down to a polynomially rather than exponentially challenging problem (Shor, 1994). The algorithm of Grover is also able to give a quadratic speed-up in the solution of unstructured search problems and is promising to give a solution to optimization problems. These are three algorithms upon which quantum speedup and the development of more complex quantum protocols are based. Although it can give rise to applications on pandemics and the development of algorithms to support bio-research, we also witness the use of algorithms that are oriented to quantum supremacy in more real-world scenarios like cryptography, material science, drug discovery and machine learning etc. Quantum simulation algorithms and quantum approximate optimization algorithm (QAOA) can enable us to effectively study and design chemicals and materials, which will enable us to develop the domains of quantum systems which are large enough that they cannot be analyzed by classical algorithms. Similarly, quantum computing algorithms such as Quantum Machine Learning which include quantum speedups in data processing and pattern recognition workflows are going to shake big-data-driven industries. © 2025 IEEE.