Atharv Gyan

Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines principles of quantum computing with machine learning algorithms. It aims to enhance the capabilities of machine learning by leveraging the unique properties of quantum mechanics, such as superposition, entanglement, and quantum parallelism. Here’s a detailed exploration of this topic: Key Concepts Quantum Computing Principles : Qubits : Unlike classical bits, which can be 0 or 1, qubits can exist in a superposition of states. This allows quantum computers to process a vast amount of information simultaneously. Superposition : A qubit can be in a combination of both 0 and 1 states at the same time. This property exponentially increases the computational power. Entanglement : Quantum entanglement is a phenomenon where qubits become interconnected such that the state of one qubit directly affects the state of another, even at a distance. Quantum Gates and Circuits : Quantum gates manipulate qubits, and q

Quantum Error Correction refers to a set of techniques used in quantum computing to address errors that inevitably arise due to the delicate nature of quantum systems. In classical computing, errors are typically corrected through redundancy and error-detection codes. However, the principles governing quantum mechanics introduce unique challenges and opportunities in error correction. In quantum computing, information is stored in quantum bits or qubits, which can exist in superposition states, allowing them to represent multiple values simultaneously. Moreover, qubits can be entangled, meaning the state of one qubit is dependent on the state of another, even if they are physically separated. These characteristics make quantum computing incredibly powerful but also prone to errors. The primary sources of errors in quantum computing include decoherence, which is the loss of quantum coherence due to interactions with the environment, and noise arising from imperfect operations on qubits.