Nicolas M. Robles
The emergence of quantum computing suggests limitless possibilities in chemistry, material science, simulations to predict outcomes, and other areas, but, first, research on quantum algorithms must be prioritized to understand the feasibility, generality, and advantages of quantum computing over classical computing. Even if sufficiently good quantum hardware is built, are there algorithms that can leverage the laws of quantum mechanics to solve difficult problems?
Classical computers use on (1) / off (0) bits as the basic units of information to arrive at the best solution to a problem. Quantum computers employ quantum bits, known as qubits, which can represent information in both 0 and 1 states simultaneously. Known as superposition, this can allow all the solutions to a problem to exist at the same time. Qubits can be correlated with each other in such a way that the state of one qubit depends on the state of another, even when the qubits are physically separated. This is the principle of entanglement, a foundational element of quantum computing.
In a radical departure from classical physics, within quantum physics there are no certain outcomes; one must deal with probabilities of obtaining certain results from a series of measurements. This requires researchers to single out desirable outcomes of a measurement by increasing the probabilities of desirable outcomes (the solution to the given problem) and decreasing the probabilities of unwanted outcomes via a process known as interference. For instance, in solving a tough combinatorial problem in logistics such as route planning or resource allocation where all the possibilities (e.g. routes, and allocations) related to this problem exist in the superposition, interference can help zero-in on the desired solution amongst these possibilities.
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