To Do in 2021: Get Up to Speed with Quantum Computing 101
(TechRepublic) The quantum computing industry is nearing the end of the early adopter phase, according to one expert, and the time is now to get up to speed. Denise Ruffner, the vice president of business development at IonQ, said that quantum computing is evolving much faster than many people realize. “When I started five years ago, everyone said quantum computing was five to 10 years away and every year after that I’ve heard the same thing,” she said. “But four million quantum volume was not on the radar then and you can’t say it’s still 10 years away any more.”
The basics of quantum computing:
Qubits: These are the 1s and 0s of quantum computing. Making and managing these objects is one of the most challenging elements of quantum computing. Some companies use superconducting circuits cooled to very cold temperatures. Others trap individual atoms in electromagnetic fields on a silicon chip in ultra-high-vacuum chambers. Yet another approach is making qubits with photons.
Superposition: This describes the combination of two states that are normally independent, such the heads side and the tails side of a coin. With quantum computing, qubits can also be in more than one state at the same time—both heads and tails simultaneously. Superposition allows quantum computers to consider many possibilities at once, as compared to traditional computers which work through one scenario at a time.
Quantum entanglement: Particles that are entangled behave together as a system. By linking two quantum computers, the interactions between the two systems can reveal information about the physical properties of each system.The current challenge with quantum computers is keeping the qubits stable for long enough to complete a calculation.
Early use cases for quantum computing:
Zapata’s Savoie said that one early use for quantum computing is optimization problems, such as the classic traveling salesman problem of trying to find the shortest route that connects multiple cities.
“Optimization problems hold enormous importance for finance, where quantum can be used to model complex financial problems with millions of variables, for instance to make stock market predictions and optimize portfolios,” he said.
Savoie also said that one of the most valuable applications for quantum computing is to create synthetic data to fill gaps in data used to train machine learning models.
Savoie also said that one thing no one is talking much about is how to use quantum as part of an analytics system in the enterprise that makes use of increasingly fragmented data and the increasingly fragmented possibilities for computation and backends.
“Quantum computing needs to be part of the analytics strategy,” he said, “making it an analytics discussion removes some of the hype of this edge technology.”