What is Quantum Theory?
In order to understand quantum computing, it’s important to have a basic understanding of quantum theory. In physics, a quantum is essentially the smallest individual unit of physical property, such as energy.
Quantum theory originated in 1900. In a presentation to the German Physical Society, Max Plank proposed the notion that energy and matter exist in individual, discrete units, rather than continuous waves. This radical theory suggested that matter and energy can sometimes behave like waves or particles, and these minute particles move randomly and therefore in an unpredictable manner. If one wishes to measure complementary values like momentum or position at the same time, problems occur; the more closely one is measured, the more flawed the other value’s measurement will be.
What is Quantum Computing?
The field of quantum computing can be difficult to explain, even for those with a background in physics. In a nutshell, quantum computing focuses on developing computer-based technologies based on quantum theory. As more and more data is created, and society’s need for storing it increases. This has led to a corresponding demand for transistors capable of processing this huge amount of data. Most classical computers can only handle one task at a time, which means that complex problems can take an exorbitant amount of time and power to solve. These sorts of problems that are beyond the scope of classical computers are referred to as intractable problems. Quantum computers are specifically designed to handle such problems.
Quantum computers rely on the properties of subatomic particles, such as the fact that they can exist in more than one state at a time, to tackle large problems. Unlike conventional computers that use bits, quantum computers use qubits, which can store vast amounts of information and use much less energy. Because a qubit can be in superposition, which means it is on and off at the same time, computers that use qubits can complete many processes at once and process immense amounts of data.
It is projected that by the year 2040, it will not be possible to meet the power demands necessary to power all of the machines around the globe. Quantum computing on a commercial scale is one potential solution to this approaching problem. The use of quantum computers opens the doors to many possibilities, such as creating processors that are a million times faster than those in use today.
Uses of Quantum Computing in Data Analytics
Although quantum computing is a relatively new technology, it is already being used in the arena of data analytics. Here are a few ways quantum computing is helping analysts handle big data:
- When using large scattered data sets, quantum computing offers high-speed detection, analysis capabilities, integration, and diagnosis.
- Quantum computers can locate patterns quickly in large, unsorted data sets by simultaneously viewing every item in a huge database.
- Whereas a non-quantum computer could take thousands of years to perform extremely complicated calculations, quantum computers can perform these in mere seconds.
- Current artificial intelligence applications are often used to handle big data, as well as to help with analyzing datasets to locate regularities. Even with the rapid evolution of this technology, traditional computers have limited data processing capabilities. Quantum computers, however, are not hindered by this limit. Three areas within artificial intelligence benefit from quantum computing’s speed and capabilities:
- Natural Language Processing: In 2020, the first quantum-powered execution of natural language processing was carried out. Scientists succeeded in translating grammatical sentences into quantum circuits. Once these programs were run on a quantum computer, it was able to perform question-answering, which has vast implications for big data.
- Machine Learning: Quantum machine learning executes machine learning algorithms on a quantum computer. This new technology can draw from more computing power than it would on a traditional computer, which can drastically increase processing speed.
- Predictive Analytics: Artificial intelligence can be used to extract meaningful historical facts and current data from datasets. When combined with quantum computing, more data is processed, which yields relevant information that can then be used to make predictions. However, the huge amount of data available is sometimes too much for a predictive model, which must account for numerous decisions, factors, and variables. Quantum computing aids in building more scalable predictive models without causing slow-downs in the process.
Most quantum computers encounter hurdles to realizing their full potential, such as environmental factors like temperature fluctuations or vibrations, which can cause them to experience a state of decoherence that leaves them largely useless. For this reason, it may still be years before quantum computing finds its way into most enterprises, or becomes a mainstream tool for data analytics.
In 2021, quantum computing is still a relatively new technology. Currently, machine learning algorithms continue to improve based on the advances in quantum computing technology. Much is still to be learned about quantum computing’s capabilities and the ramifications of this powerful tool.
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