Quantum Computing – The Next Tech Revolution Technology has evolved rapidly over the last few decades—from bulky mainframe computers to powerful smartphones in our pockets. Yet, despite these advances, traditional computers are approaching their physical limits. This is where quantum computing enters the scene, promising to revolutionize the way we process information and solve complex problems. What Is Quantum Computing? Quantum computing is a new paradigm of computing that uses the principles of quantum mechanics, a branch of physics that explains how matter and energy behave at the smallest scales. Unlike classical computers, which use bits that represent either 0 or 1, quantum computers use qubits. Qubits can exist in multiple states simultaneously, thanks to a property called superposition. Additionally, qubits can be interconnected through entanglement, allowing them to share information instantaneously. These unique properties give quantum computers immense computational power....
• A data structure is a particular way of storing and organizing data either in computer’s memory or on the disk storage so that it can be used efficiently.
• There are two types of data structures: primitive and non-primitive data structures. Primitive data structures are the fundamental data types which
are supported by a programming language. Non-primitive data structures are those data structures which are created using primitive data structures.
• Non-primitive data structures can further be classified into two categories: linear and non-linear data structures.
• If the elements of a data structure are stored in a linear or sequential order, then it is a linear data structure. However, if the elements of a data structure are not stored in sequential order, then it is a non-linear data structure.
• An array is a collection of similar data elements which are stored in consecutive memory locations.
• A linked list is a linear data structure consisting of a group of elements (called nodes) which together represent a sequence.
• A stack is a last-in, first-out (LIFO) data structure in which insertion and deletion of elements are done at only one end, which is known as the top of the stack.
• A queue is a first-in, first-out (FIFO) data structure in which the element that is inserted first is the first to be taken out. The elements in a queue are added at
one end called the rear and removed from the other end called the front.
• A tree is a non-linear data structure which consists of a collection of nodes arranged in a hierarchical tree structure.
• The simplest form of a tree is a binary tree. A binary tree consists of a root node and left and right sub-trees, where both sub-trees are also binary trees.
• A graph is often viewed as a generalization of the tree structure, where instead of a purely parent-to-child
relationship between tree nodes, any kind of complex relationships can exist between the nodes.
• An abstract data type (ADT) is the way we look at a data structure, focusing on what it does and ignoring how it does its job.
• An algorithm is basically a set of instructions that solve a problem.
• The time complexity of an algorithm is basically the running time of the program as a function of the input size.
• The space complexity of an algorithm is the amount of computer memory required during the program execution as a function of the input size.
• The worst-case running time of an algorithm is an upper bound on the running time for any input.
• The average-case running time specifies the expected behaviour of the algorithm when the input is randomly drawn from a given distribution.
• Amortized analysis guarantees the average performance of each operation in the worst case.
• The efficiency of an algorithm is expressed in terms of the number of elements that has to be processed and the type of the loop that is being used.