Quantum computing uses phenomena in quantum physics to create new ways of computing. Quantum computing involves qubits. Unlike a normal computer bit, which can be either 0 or 1, a qubit can exist in a multidimensional state. In other words, qubits carry information in a quantum state that engages 0 and 1 in a multidimensional way. The power of quantum computers grows exponentially with more qubits with much less energy consumption. Classical computers that add more bits can increase power only linearly.
Quantum physics on which quantum computing is based on threefeatures:
- Superposition - refers to a combination of all possible configurations of the qubit.
- Entanglement - pairs of qubits can be made to become entangled. This means that the two qubits then exist in a single state.
- Decoherence - occurs when the quantum behavior of qubits decays. The quantum state can be disturbed instantly by vibrations or temperature changes. This can cause qubits to fall out of superposition and cause errors to appear in computing. It's important that qubits be protected from such interference by, for instance, supercooled refridgerators, insulation, and vacuum chambers
Quantum Machine Learning takes elements from classical Machine Learning theory, and views quantum computing from that lens. The main goal of Quantum Machine Learning is to speed things up by applying what we know from quantum computing to machine learning. At the moment rather than asking what quantum computing can do for ML, the most immediate application seems to be what machine learning can be used to reduce noise of quantum computing.