Quantum computing, qubits and more

Quantum computers and quantum physics open the door to new analytic opportunities. This technology will expand applications of Artificial intelligence and and machine learning, enabling automation to take on human like properties. Current small scale applications are the forefront of this expanding area of experience. army adopters will gain significant competitive advantage. Time is running out

quantum computers simply

You don't have to go back too far to find the origins of quantum computing. While computers have been around for the majority of the 20th century, quantum computing was first theorized less than 30 years ago, by a physicist at the Argonne National Laboratory. Paul Benioff is credited with first applying quantum theory to computers in 1981. Benioff theorized about creating a quantum Turing machine. Most digital computers, like the one you are using to read this article, are based on the Turing Theory. 

Qubits - quantum bits

Today's computers, like a Turing machine, work by manipulating bits that exist in one of two states: a 0 or a 1. Quantum computers aren't limited to two states; they encode information as quantum bits, or qubits, which can exist in superposition. Qubits represent atoms, ions, photons or electrons and their respective control devices that are working together to act as computer memory and a processor. Because a quantum computer can contain these multiple states simultaneously, it has the potential to be millions of times more powerful than today's most powerful supercomputers.

​
Quantum computers and artificial intelligence
n the early ’90s, Elizabeth Behrman, a physics professor at Wichita State University, began working to combine quantum physics with artificial intelligence — in particular, the then-maverick technology of neural networks. Most people thought she was mixing oil and water. “I had a heck of a time getting published,” she recalled. “The neural-network journals would say, ‘What is this quantum mechanics?’ and the physics journals would say, ‘What is this neural-network garbage?’”
​
Today the mashup of the two seems the most natural thing in the world. Neural networks and other machine-learning systems have become the most disruptive technology of the 21st century. They out-human humans, beating us not just at tasks most of us were never really good at, such as chess and data-mining, but also at the very types of things our brains evolved for, such as recognizing faces, translating languages and negotiating four-way stops. These systems have been made possible by vast computing power, so it was inevitable that tech companies would seek out computers that were not just bigger, but a new class of machine altogether.

Superposition

This superposition of qubits is what gives quantum computers their inherent parallelism. According to physicist David Deutsch, this parallelism allows a quantum computer to work on a million computations at once, while your desktop PC works on one. A 30-qubit quantum computer would equal the processing power of a conventional computer that could run at 10 teraflops (trillions of floating-point operations per second). Today's typical desktop computers run at speeds measured in gigaflops (billions of floating-point operations per second).

Entanglement


Quantum computers also utilize another aspect of quantum mechanics known as entanglement. One problem with the idea of quantum computers is that if you try to look at the subatomic particles, you could bump them, and thereby change their value. If you look at a qubit in superposition to determine its value, the qubit will assume the value of either 0 or 1, but not both (effectively turning your spiffy quantum computer into a mundane digital computer). To make a practical quantum computer, scientists have to devise ways of making measurements indirectly to preserve the system's integrity. Entanglement provides a potential answer. In quantum physics, if you apply an outside force to two atoms, it can cause them to become entangled, and the second atom can take on the properties of the first atom. So if left alone, an atom will spin in all directions. The instant it is disturbed it chooses one spin, or one value; and at the same time, the second entangled atom will choose an opposite spin, or value. This allows scientists to know the value of the qubits without actually looking at them.

​
Quantum future is here
Once considered a pie-in-the-sky technology, quantum computing is emerging as a way for enterprises to tackle machine learning (ML), optimization, search and challenges that classic computing models can't touch. CIOs must begin exploring the technology now or risk falling behind rivals, according to Gartner.

Quantum machines will process in seconds data that takes years for supercomputers to process. That could be game-changing for enterprises that figure out how to use them to solve significant computing challenges, says Gartner analyst Matthew Brisse. Machine learning may be the perfect use case, as quantum machines will process ML algorithms faster, accelerating enterprises' ability to process information and derive insights, according to Brisse. "If you can speed up the machine learning aspect of quantum computing you will accelerate the adoption of AI and make it more efficient," he says.
​
Gartner estimates that 20 percent of Fortune 500 companies will be budgeting for quantum computing projects by 2021. Brisse says IT leaders ask him what quantum computing is, what they can do with it and where to find engineers to work with the technology. Most importantly, CIOs want to know how to apply quantum computing to their business and identify opportunities for concise innovation.

The simple photos