A quantum computer is based on phenomena such as superposition and entanglement and uses such phenomena to perform operations on data. While binary digital electronic computers are based on transistors, quantum-mechanical phenomena form the basis of quantum computers.

Theoretically, large-scale quantum computers would be far more efficient and faster at solving certain problems than any classical computers. For example, Shor’s algorithm, a quantum algorithm for integer factorization running on a quantum computer would beat hands down the corresponding problem running on a classical computer. Another example where quantum computers would reign supreme is the simulation of quantum many-body systems. Quantum algorithms, such as Simon’s algorithm, run much faster compared to any possible probabilistic classical algorithm. It’s important to note that, in principle, a classical computer could simulate a quantum algorithm. This is because quantum computation does not violate the Church–Turing thesis. To perform this, however, a classical computer would require an inordinate amount of resources. Quantum computers, on the contrary, might efficiently solve problems that are too complex to be practically solved by classical computers.

A quantum computer uses quantum states to represent bits simultaneously to achieve an exponential increase in speed and power. Enormous, complex problems usually requiring massive amount of resources and time can be solved in a reasonable amount of time. This is highly beneficial for IoT data that requires a lot of computation power and other complex optimization functions. In drug discovery, for example, trillions of combinations of amino acids are examined to find a single elusive protein.

On the quantum level, you’re able to program the atoms to represent all possible input combinations simultaneously. That means when you run an algorithm, all possible input combinations are tested at once. With a regular computer, you’d have to serially cycle through every possible input combination to arrive at your solution. Interestingly, solving the most complex problems this way would take longer than the age of the universe.

For certain types of problems, quantum computers can provide an exponential speed boost. Quantum database search is the most well-known example of this.

Besides factorization and discrete logarithms, quantum algorithms offer more than polynomial speedup over the best-known classical algorithms. Simulation of quantum physical processes in solid state physics as well as chemistry, approximation of Jones polynomials, and solving Pell’s equation are some well-known examples.

A composite system is always expressible as a sum or superposition of products of states of local constituents.

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