What makes quantum computing so different from classical computing that it may lead to breakthroughs in chemistry, physics, medicine and finance?

In classical digital computing, data is encoded in binary digits (bits), which are always in one of two definite states, 0 or 1.

Quantum computers are based on qubits, which operate according to two principles of quantum mechanics: superposition and entanglement.

Quantum computing takes advantage of the ability of subatomic particles to exist in more than one state at a time.

Superposition means each qubit can represent both a 1 and 0 at the same time.

Entanglement means qubits in a superposition can be correlated with each other. The state of one can depend on the state of the other. Qubits, IBM explains, “Can act as more sophisticated switches, enabling quantum computers to function in ways that allow them to solve difficult problems today’s computers can’t.”

A common comparison is to think of a qubit as an imaginary sphere. A classical bit can be in two states at either pole of the sphere. A qubit can be at any point on the sphere, meaning a quantum computer can store an enormous amount of information and perform calculations much more quickly than a classical computer.

Qubits are so sensitive to light, heat, or radiation that the inside of a quantum computer is one of the coldest places in the universe, just a bit above absolute zero to prevent “decoherence,” or falling out of their quantum state.

Even so at near absolute zero, quantum computers can usually only hold a quantum microstate for seconds (IBM set a record in November 2017 when its 50-qubit computer held its microstate for 90 seconds.)

Due to superposition and entanglement, quantum computers can process a huge number of calculations simultaneously, which means they can tackle extremely difficult mathematical problems that stymie classical computers. They may make it possible to study in detail the interactions of atoms and molecules and help design new medicines or materials.

They may also be able to deal with complex financial data and solve difficult logistics problems, such as how to ship products globally at the lowest cost.