Quantum Computing And 17 Uses Of Quantum Computer

This article is about quantum computing and its uses and applications exist in our world, You may have heard about quantum computing, and sometimes you may have been thought about it. I have been daunted so many times by this question in my life, “What is Quantum computing, Is it really exists?”

I researched about it all over the web to help you in this question, I have got some news and some interesting things and facts about quantum computing for you, I have got only few information about quantum computing and it’s applications.


Is Quantum Computing Really Exists?

Is Quantum Computing Really Exists?
Yes, while some have disputed this claim, Quantum computing is still a significant milestone in the history of quantum computing. Quantum computing is modeled by quantum circuits. Quantum circuits are based on the quantum bit, or "qubit", which is somewhat analogous to the bit in classical computation, It shows that it exists, right?


How Quantum Computing Works?

How Quantum Computing Works?
The computers that perform quantum computing problems are called Quantum computers.
Using Quantum computers, these computers perform calculations based on the probability of an object's state before it is measured - instead of just 1s or 0s - which means they have the potential to process exponentially more data compared to classical computers.

Classical computers carry out logical operations using the definite position of a physical state. These are usually binary, which means operations are based on one of the two positions. A single state such as on/off, up/down, 1/ 0 - is called a bit.

In Quantum computing, these operations instead use the quantum state of an object to produce what is known as a qubit as I already told about it, These states are the undefined properties of an object before they've been detected, such as the spin of an electron or polarization of a photon. Sometimes there will be a superposition, which is occurred whenever quantum states are unmeasured, if there was an unclear position.

Quantum computers can solve problems that are impossible or would take a traditional computer an impractical amount of time (a billion years) to solve.

To build a functional quantum computer, it requires an object in superposition state as long as it takes time to carry out various processes on those objects, unfortunately, once a superposition meets with materials that are part of a measuring system, it loses its in-between state in what's known as quantum decoherence, which is the loss of quantum coherence, decoherence happens when different portions of the system's wave function become entangled in different ways with the measuring device, devices need to be able to shield quantum states from decoherence, while still making them easy to read, different processes are tackling this challenge from different angles, whether it's to use more robust quantum processes or to find better ways to check for errors.


What Are The Types Of Quantum Computers? 

What Are The Types Of Quantum Computers?
Three types of quantum computers are considered to be possible by IBM.
  1. Quantum Annealer
  2. Analog Quantum
  3. Universal Quantum
Below we have discussed these quantum computers briefly so that you can get an idea about them.

1. Quantum Annealer

The quantum annealer is the least powerful and most restrictive form of quantum computers. It is the easiest to build, yet can only perform one specific function. The consensus of the specific community is that a quantum annealer has no known advantages over conventional computing.
  • Applications: Optimization problems
  • Generality: Restrictive
  • Computational power: Same as the traditional computers
  • It’s a little bit difficult.

2. Analog Quantum

The Analog quantum computer will be able to simulate complex quantum interactions that are intractable for any known conventional machine, or some combinations of these machines.
This could happen within the next five years.
  • Applications: Quantum chemistry, Sampling, Quantum dynamics, Material science, Optimization problems.
  • Generality: Partial
  • Computational power: Not like quantum annealers, it’ power is high.
  • Its difficulty level is more than quantum annealers.

3. Universal Quantum

The universal quantum computer is the most powerful, the most general, and the hardest to build, posing several difficult technical challenges.
Current estimates indicate that this machine will comprise more than 100,000 physical qubits.
  • Applications: Machine learning, Sampling, Searching, Material Science, Quantum Dynamics, Cryptography, Secure computing.
  • Generality: Complete with known speed up
  • Computational power: Very high
  • It’s more difficult than all other quantum computers.
These types and characteristics of these computers are found out by research did by IBM.
Some big tech companies like Google, IBM, continue to cram more qubits together and build more accurate devices.



Application of Quantum Computers

“Rather than applications, is quantum computing really helpful, by knowing the applications of quantum computing you can come to an end, that it’s really useful.

It’s useful for many developments in science and medications to saving many lives in this world, quantum computers are being used for a long time, as computers were made to serve us to solve problems.

Here the 15 Amazing uses and applications of Quantum Computing, the list is not based on popularity.



1. Cryptography

Cryptography
Most cybersecurity and online security currently depends on the difficulty of factoring large numbers into primes. Meanwhile, this can presently be accomplished by using the digital computers to search through every possible factor, the huge time required makes “cracking the code” costly and also makes it impractical.

Quantum cryptography is the science of exploiting quantum mechanical properties to perform cryptographic tasks. The best-known example of quantum cryptography is quantum key distribution which offers an information-theoretically secure solution to the key-exchange problem.

The advantage of quantum cryptography lies in the fact that it allows the completion of various cryptographic tasks that are proven or conjectured to be impossible using only classical (i.e. non-quantum) communication.

For example, it is possible to copy data encoded in a quantum state. If one attempts to read the encoded data, the quantum state will be changed, NO cloning theorem. This could be used to detect eavesdropping in quantum key distribution.



2. Artificial Intelligence

Artificial Intelligence
Artificial intelligence is one of the primary applications of quantum computing, most of you may have heard about AI, What is AI? , Artificial intelligence (AI) is an area of computer science that emphasizing the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning.

Quantum artificial intelligence (QAI) is an interdisciplinary field that focuses on building quantum algorithms for improving computational tasks within artificial intelligence, including sub-fields like machine learning.

The overall concept of quantum-enhanced AI algorithms is still in the conceptual research domain.
For example, Lockheed Martin plans to use its D-Wave quantum computer to test the autopilot software that is also currently very complex for classical computers, and Google is using a quantum computer to design software to distinguish cars from landmarks.

We have already reached the point where AI is creating much more AI, and so its importance will rapidly grow.



3. Financial Modelling

Financial Modelling
Quantum finance is an interdisciplinary research field, applying theories and methods developed by quantum physicists and economists to solve problems in finance. It is a branch of econophysics.

One potential application for quantum technologies is algorithmic trading – the use of complex algorithms to automatically trigger share dealings based on a wide variety of market variables.

The advantages, especially for high-volume transactions, are significant.

For a finance industry to find the right mix for fruitful investments based on expected returns, the risk associated, and other factors are important to survive in the market.

To achieve that, the technique of ‘Monte Carlo’ simulation is continually being run on conventional computers, which, in turn, consume an enormous amount of computer time.

However, by applying quantum technology to perform these massive and complex calculations, companies can not only improve the quality of the solutions but also reduce the time to develop them.

Because financial leaders are in a business of handling billions of dollars, even a tiny improvement in the expected return can be worth a lot for them.



4. Molecular Modelling

Molecular Modelling
Molecular modeling encompasses all methods, theoretical and computational, used to model or mimic the behavior of molecules.

The methods are used in the fields of computational chemistry, drug design, computational biology, and materials science to study molecular systems ranging from small chemical systems to large biological molecules and material assemblies.

The simplest calculations can be performed by hand, but inevitably computers are required to perform molecular modeling of any reasonably sized system.

The common feature of molecular modeling methods is the atomistic level description of the molecular systems.



5. Weather Forecasting

Weather Forecasting
Currently, the process of analyzing weather conditions by traditional computers can sometimes take longer than the weather itself does to change.

But a quantum computer’s ability to crunch vast amounts of data, in a short period, could indeed lead to enhancing weather system modeling allowing scientists to predict the changing weather patterns in no time and with excellent accuracy something which can be essential for the current time when the world is going under a climate change.

Weather forecasting includes several variables to consider, such as air pressure, temperature and air density, which makes it difficult for it to be predicted accurately.



6. Optimization

Optimization
Mathematical optimization deals with finding the best possible solution to a problem based on some criteria from a set of possible solutions.

Mostly, the optimization problem is formulated as a minimization problem, where one tries to minimize an error which depends on the solution: the optimal solution has minimal error.

Different optimization techniques are applied in various fields such as mechanics, economics, and engineering, and as the complexity and amount of data involved rise, more efficient ways of solving optimization problems are needed.

The power of quantum computing may allow solving problems which are not practically feasible on classical computers or suggest a considerable speed-up concerning the best known classical algorithm.

Among other quantum algorithms, there are quantum optimization algorithms that might suggest improvement in solving optimization problems.



7. Machine Learning

Machine Learning
Machine learning is a popular area right now because we are now seeing significant deployments at the consumer level of many different platforms.

 Also, we are now seeing aspects of this every day in voice recognition, image recognition and also in handwriting recognition and so many examples are there.

But it is also difficult to do and computationally expensive one, specifically if you want to achieve the task with good accuracy.

Because of the potential payoff, there is a lot of research going right now based upon a sampling of Boltzmann distributions.



8. Particle Physics

Particle Physics
Particle physics is a branch of physics that studies the nature of the particles that constitute matter and radiation. Researchers at Lawrence Berkeley National Laboratory are working to tackle high volumes of particle physics data with quantum computing.



9. Healthcare

Healthcare
The primary aim of health-related QC applications is to analyze relationships between prevention or treatment techniques and patient outcomes.

1. Research

Quantum computers will allow much larger molecules to be simulated. At the same time, researchers will be able to model and simulate interactions between drugs and all 20,000+ proteins encoded in the human genome, leading to greater advancements in pharmacology.

2. Diagnostics

Quantum technologies could be used to provide faster, more accurate diagnostics with a variety of applications. Boosting AI capabilities will improve machine learning – something that is already being used to aid pattern recognition.

High-resolution MRI machines will provide greater levels of detail and also aid clinicians with screening for diseases.

3. Treatment

Targeted treatments, such as radiotherapy, depend upon the ability to rapidly model and simulate complex scenarios to deliver the optimal treatment. Quantum computers would enable therapists to run more simulations in less time, helping to minimize radiation damage to healthy tissue.



10. Drug Design & Drug Discovery

Drug Design & Drug Discovery
In computational chemistry, designing and developing a drug is the biggest challenging problem, computational chemistry is one of the most promising quantum computing applications said by IBM, drugs are discovered by trial and error method, which is not only very expensive and also risky and challenging one to complete.

Therefore, researchers believe quantum computing can be an effective way of simulating how a drug will react, by which they can save a ton of money and time.

These advancements in ML and optimization could enhance the biomedical and chemical simulations could help companies carry more drug discoveries.



11. Quantum Search

Quantum Search
Grover's algorithm is a quantum algorithm that finds with high probability the unique input to a black box function that produces a particular output value, using just evaluations of the function, where. Is the size of the function's domain. It was devised by Lov Grover in 1996.



12. Quantum Simulation

Quantum Simulation
Quantum simulators permit the study of quantum systems that are very difficult to study in the laboratory and impossible to model with a supercomputer, Quantum simulators are devices that use quantum effects to answer about model systems and, through them real systems.



13. Solving Linear Equations

Solving Linear Equations
Linear systems of equations are solved by a quantum algorithm, designed by Aram Harrow, Avinatan Hassidim, and Seth Lloyd, formulated in 2009, the algorithm estimates the result of a scalar measurement on the solution vector to a given linear system of equations.



14. Quantum Supremacy

Quantum Supremacy
In quantum computing, quantum supremacy is the goal of demonstrating that a programmable quantum device can solve a problem that no classical computer can feasibly solve.



15. Quantum Annealing And Adiabatic Optimization

Quantum Annealing And Adiabatic Optimization
Quantum annealing (QA) is a metaheuristic for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctuations (in other words, a meta-procedure for finding a procedure that finds an absolute minimum size/length/cost/distance from within a possibly very large, but nonetheless finite set of possible solutions using quantum fluctuation-based computation instead of classical computation).

Quantum annealing is used mainly for problems where the search space is discrete (combinatorial optimization problems) with many local minima; such as finding the ground state of a spin glass or the traveling salesman problem.


Conclusion

As I mentioned at the beginning of this article, the list above is still a few applications of quantum computing, there are so many applications exist all over the world.

Even though the true quantum computer is still not a real thing in this world, it’s clear that the race is on, We know that they will be faster for many computational tasks, from modeling nature to searching large amounts of data.

I think there are many more applications and, perhaps, the most important ones are still waiting to be discovered.

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