Despite the fact that quantum computing is still in its infancy, numerous inventions and scientific advances have already been made. Major corporations have been making significant investments in the technology, including IBM, Microsoft, Google, and Honeywell.
What is quantum computing, then? Well, it is comparable to conventional computing, which uses bits, or the 0s and 1s, to encode data. However, the quantum bit, or qubit, is this in the context of quantum computing. Here, the information might exist in numerous states simultaneously. The impact of quantum physics' effects, such as superposition and entanglement, is the cause of this. Yes, this is all about the bizarre world of Schrodinger's cat, who is simultaneously alive and dead.
What is Quantum Computing?
The field of study known as quantum computing is concerned with creating computer technology based on the ideas of quantum theory. The ability of the quantum computer to be in various states and to carry out tasks using all potential permutations at once would give it immense processing capability, according to the laws of quantum mechanics.
A fast developing technology called quantum computing uses the principles of quantum physics to solve issues that are too complicated for conventional computers.
A technology that scientists had only just begun to envisage thirty years ago is now made accessible to hundreds of thousands of developers thanks to IBM Quantum. Our engineers consistently produce superconducting quantum processors with increased power along with significant software and quantum-classical orchestration advancements. The world-changing speed and capacity of quantum computing are being advanced by this effort.
Quantum information science (QIS), artificial intelligence, soft computing, computational intelligence, machine learning, deep learning, optimization, and other fields are all intersected in research on quantum artificial intelligence. It covers a wide range of crucial aspects of noisy intermediate-scale quantum (NISQ) devices and near-term quantum computing. Theories, models, and important studies on hybrid classical-quantum algorithms using classical simulations, IBM Q services, PennyLane, Google Cirq, D-Wave quantum annealer, etc. serve as the foundation for research on quantum artificial intelligence.
We currently have the tools necessary to gain quantum advantage and solve issues in combinatorial optimization, soft computing, deep learning, and machine learning significantly more quickly than with conventional classical computing, thanks to studies in quantum artificial intelligence. It's crucial to find solutions to these issues if we want to deploy quantum computing for noisy large-scale applications.
In general, AI and ML are effective methods for asking a computer to deliver a solution to a problem based on some prior knowledge. For instance, it might be difficult to explain to a computer what a cat is. Still, a neural network can recognize other cats that it had never seen before if you show it enough photographs of cats and tell it they are cats. Some of the most well-known and frequently used AI and ML algorithms seem to be much faster when performed on quantum computers.
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Why do you need Quantum Computing?
The constraints of classical computing create a need for quantum computing. Even while traditional computers have come a long way in the last few decades, they still have a long way to go in some areas. These difficulties result from the fact that traditional computers are constrained by the physical characteristics of traditional bits, which can only exist in a 0 or a 1.
One of the main drivers for growing the field of quantum computation has been the prospect of creating a quantum computer capable of carrying out Shor's algorithm for huge numbers. However, it is critical to recognize that quantum computers will probably only significantly speed up a small subset of problems if one wants to acquire a more comprehensive perspective on them. Researchers are striving to create algorithms that demonstrate quantum speedups as well as identify the issues that are most suited for them. In general, it is anticipated that quantum computers would be of great assistance with problems involving optimization, which are crucial to everything from financial trading to defense.
Multiple variables that interact in intricate ways are considered complex problems. Because there are so many different electrons interacting with one another, modeling the behavior of individual atoms in a molecule is a challenging task. Complex issues include finding minor fraud tendencies in bank transactions or discovering novel physics in a supercollider. We are unable to use traditional computers at any scale to handle some complex problems.
When a supercomputer struggles, it's often because the large classical machine was given a challenging problem to answer. Complexity is frequently the cause of failure for conventional computers.
Construction of quantum computers is extremely challenging. On the scale of a single atom, there are numerous candidate qubit systems, and those physicists, technologists, and materials scientists working to implement quantum operations on these systems are continuously juggling two opposing demands. In order to safeguard qubits from the environment, which can obliterate the delicate quantum states required for computation, we must first shield them from it. A qubit's "coherence time" increases with how long it remains in the desired state. Isolation is valued from this angle. Second, however, qubits must be entangled, moved around physical architectures, and programmable on demand for algorithm execution. The more these procedures' "fidelity," the better they can be executed. It's challenging to strike a balance between the necessary solitude and engagement.
Use cases of Quantum Artificial Intelligence
Quantum computing is slowly but definitely getting ready for the spotlight.
In October 2019, Google made waves by announcing that it had made the long-awaited quantum supremacy breakthrough. A quantum computer can thus complete a task that a traditional computer is unable to. Not any time soon enough to be useful. For instance, Google asserted that the test problem it ran would have required a traditional computer to solve thousands of years, however some detractors and rivals criticized that claim as being greatly exaggerated.
The following are some of the applications of this super combination of quantum computing and AI, keeping in mind that the term "quantum AI" means the use of quantum computing for computation of machine learning algorithms, which takes advantage of the computational superiority of quantum computing to achieve results that are not possible to achieve with classical computers.
Processing Big Data Sets
Every day, we generate 2.5 exabytes of data. That is equal to the contents of 250,000 Library of Congresses or 5 million laptops. With 9,722 Pinterest pins, 347,222 tweets, 4.2 million Facebook likes, and all the other data we generate by taking pictures and videos, storing documents, opening accounts, and more, 3.2 billion people utilize the internet every minute of every day.
The study and development of molecular structures in the biopharmaceuticals sector has the potential to be revolutionized by quantum computing. It also has the ability to add value during production and farther down the value chain. For instance, new pharmaceuticals developed through research and development (R&D) take more than ten years and an average of $2 billion to commercialize. By reducing the reliance on trial and error and increasing efficiency, target identification, drug design, and toxicity testing might all be far faster, more targeted, and precise thanks to quantum computing. A shorter R&D cycle may provide products to the correct patients more quickly and effectively—in other words, it would enhance the quality of life for more people. Quantum computing may potentially be advantageous for supply chains, logistics, and manufacturing.
The first commercial quantum computer from IBM, which only has 20 qubits of power and can operate outside the research lab, was unveiled in January 2019. The company's developers later revealed the creation of a 53-qubit computer in October 2019. A 128-qubit computer is currently being created by the startup Rigetti Computing in addition to a 32-qubit one. With a 53-qubit quantum computing processor, Google claimed in October 2019 to have attained "quantum supremacy," doing calculations that would have taken a classical computer 10,000 years to complete in just 200 seconds. IBM quickly refuted that assertion, saying a different classical technique could have solved the same issue as Google's machine in just 2.5 days.
The automotive sector can use quantum computing to improve its research and development, product design, supply-chain management, production, mobility, and traffic management. By improving components like path planning in complicated multi robot processes (the route a robot takes to do a task), such as welding, glueing, and painting, the technology might, for instance, be used to reduce manufacturing process-related costs and cut cycle times. In an industry whose manufacturing expenditures total $500 billion annually, even a 2 to 5 percent efficiency increase might generate $10 to $25 billion in value annually.
Information can be transferred securely and encrypted using quantum cryptography. It does not rely on mathematical methods or safe key exchanges, unlike other types of cryptography; rather, it provides security by the laws of physics. Currently, extremely secure quantum communication based on quantum cryptography is impossible to wiretap or intercept. The most well-known example of this is quantum key distribution (QKD), which uses quantum mechanical phenomena to carry out cryptographic operations.
Finally, financial applications of quantum computing are somewhat in the future, and the benefits of any potential near-term applications are hypothetical. The most exciting applications of quantum computing in finance, in our opinion, are risk and portfolio management. Loan portfolios that are efficiently quantum-optimized and concentrate on collateral, for instance, can enable lenders to enhance their services, perhaps lowering interest rates and freeing up money. Estimating the value potential of quantum computing-enhanced collateral management is difficult and infeasible at this point, but the $6.9 trillion worldwide lending market in 2021 shows that quantum optimization might have a substantial impact.
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The use of quantum computers has the potential to transform computation by solving some types of issues that were previously insoluble. Despite the fact that no quantum computer is now sophisticated enough to perform calculations that a classical computer cannot, significant development is taking place. Existing non-error-corrected quantum computers with several tens of qubits are presently in use by a select few big businesses and fledgling startups, and some of them are even open to the general public via the cloud. Quantum simulators are also making progress in domains like many-body physics and molecular physics.
A field devoted to immediate uses of quantum computers is beginning to take off as modest devices come online. With this advancement, some of the advantages and insights of quantum processing could be realized much sooner than previously thought.