What Is Quantum Computing? Super -Easy Explanation

Quantum Computing

It’s fascinating to think about the power in our pocket—today’s smartphones have the computing power of a military computer from 50 years ago that was the size of an entire room. However, even with the phenomenal strides we made in technology and classical computers since the onset of the computer revolution, there remain problems that classical computers just can’t solve. Many believe quantum computers are the

Quantum computing is a rapidly-emerging technology that harnesses the laws of quantum mechanics to solve problems too complex for classical computers. 

Today, IBM Quantum makes real quantum hardware — a tool scientists only began to imagine three decades ago — available to thousands of developers. Our engineers deliver ever-more-powerful superconducting quantum processors at regular intervals, building toward the quantum computing speed and capacity necessary to change the world. 

These machines are very different from the classical computers that have been around for more than half a century. Here’s a primer on this transformative technology.

Why do we need quantum computers?

For some problems, supercomputers aren’t that super.

When scientists and engineers encounter difficult problems, they turn to supercomputers. These are very large classical computers, often with thousands of classical CPU and GPU cores. However, even supercomputers struggle to solve certain kinds of problems.

If a supercomputer gets stumped, that’s probably because the big classical machine was asked to solve a problem with a high degree of complexity. When classical computers fail, it’s often due to complexity

Complex problems are problems with lots of variables interacting in complicated ways. Modeling the behavior of individual atoms in a molecule is a complex problem, because of all the different electrons interacting with one another. Sorting out the ideal routes for a few hundred tankers in a global shipping network is complex too.  

Where are quantum computers used?

  • Artificial intelligence. Quantum computing potentially opens up new opportunities in artificial intelligence, which often involves the combinatoric processing of very large quantities of data in order to make better predictions and decisions (think facial recognition or fraud detection). A growing research field in quantum machine learning identifies ways that quantum algorithms can enable faster AI. The current limitations on the technology and software make quantum artificial general intelligence a fairly remote possibility — but it certainly makes thinking machines more than a subject for science fiction.
  • Financial services. Finance was one of the earliest domains to embrace Big Data. And much of the science behind the pricing of complex assets — such as stock options — involves combinatoric calculation. When Goldman Sachs, for example, prices derivatives it applies a highly computing-intensive calculation known as a Monte Carlo simulation, which makes projections based on simulated market movements. Computing speed has long been a source of advantage in financial markets (where hedge funds vie to get millisecond advantages in obtaining price information). Quantum algorithms can increase speed for an important set of financial calculations.
  • Complex manufacturing. Quantum computers can be used in taking large manufacturing data sets on operational failures and translating them to combinatoric challenges that, when paired with a quantum-inspired algorithm, can identify which part of a complex manufacturing process contributed to incidents of product failure. For products like microchips where this production process can have thousands of steps, quantum can help reduce costly failures.

Why quantum computers are faster?

A supercomputer might be great at difficult tasks like sorting through a big database of protein sequences. But it will struggle to see the subtle patterns in that data that determine how those proteins behave.

Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.

A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. But as the protein sequences get longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways. No computer has the working memory to handle all the possible combinations of individual folds.

Quantum algorithms take a new approach to these sorts of complex problems — creating multidimensional spaces where the patterns linking individual data points emerge. In the case of a protein folding problem, that pattern might be the combination of folds requiring the least energy to produce. That combination of folds is the solution to the problem.

Classical computers can not create these computational spaces, so they can not find these patterns. In the case of proteins, there are already early quantum algorithms that can find folding patterns in entirely new, more efficient ways, without the laborious checking procedures of classical computers. As quantum hardware scales and these algorithms advance, they could tackle protein folding problems too complex for any supercomputer.

How complexity stumps supercomputers

Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.

A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. But as the protein sequences get longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways. No computer has the working memory to handle all the possible combinations of individual folds.

Quantum computers are built for complexity
Quantum algorithms take a new approach to these sorts of complex problems — creating multidimensional spaces where the patterns linking individual data points emerge. Classical computers can not create these computational spaces, so they can not find these patterns. In the case of proteins, there are already early quantum algorithms that can find folding patterns in entirely new, more efficient ways, without the laborious checking procedures of classical computers. As quantum hardware scales and these algorithms advance, they could tackle protein folding problems too complex for any supercomputer.

How do quantum computers work?

Quantum computers are elegant machines, smaller and requiring less energy than supercomputers. An IBM Quantum processor is a wafer not much bigger than the one found in a laptop. And a quantum hardware system is about the size of a car, made up mostly of cooling systems to keep the superconducting processor at its ultra-cold operational temperature.

A classical processor uses bits to perform its operations. A quantum computer uses qubits (CUE-bits) to run multidimensional quantum algorithms.

Superfluids
Your desktop computer likely uses a fan to get cold enough to work. Our quantum processors need to be very cold – about a hundredth of a degree above absolute zero. To achieve this, we use super-cooled superfluids to create superconductors.

Superconductors
At those ultra-low temperatures certain materials in our processors exhibit another important quantum mechanical effect: electrons move through them without resistance. This makes them “superconductors.” When electrons pass through superconductors they match up, forming “Cooper pairs.” These pairs can carry a charge across barriers, or insulators, through a process known as quantum tunneling. Two superconductors placed on either side of an insulator form a Josephson junction.

Control
Our quantum computers use Josephson junctions as superconducting qubits. By firing microwave photons at these qubits, we can control their behavior and get them to hold, change, and read out individual units of quantum information.

Superposition
A qubit itself isn’t very useful. But it can perform an important trick: placing the quantum information it holds into a state of superposition, which represents a combination of all possible configurations of the qubit. Groups of qubits in superposition can create complex, multidimensional computational spaces. Complex problems can be represented in new ways in these spaces.

Entanglement
Entanglement is a quantum mechanical effect that correlates the behavior of two separate things. When two qubits are entangled, changes to one qubit directly impact the other. Quantum algorithms leverage those relationships to find solutions to complex problems.

Quantum computing hype vs. reality

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IBM Quantum is your partner to cut through the hype surrounding quantum computing, and to position your institution to leverage this emerging technology for competitive advantage. Here’s what business leaders need to understand about quantum computing today.

Quantum computers are poised to transform the way you work in research. Classical computers get bogged down trying to model natural systems, including chemical reactions and folding proteins. Quantum computers offer a new set of tools to understand the universe.

Programming for quantum computers doesn’t require extensive retraining or new coding languages. But it will give you access to an entirely new computing paradigm all the same

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