Quantum Computing the Future of AI
Quantum computing holds the potential to revolutionize AI despite still being in its early phases. Companies like Microsoft, IBM, Google, and Honeywell have already invested in technology to create many innovations and breakthroughs.
But, before we get our hands dirty, lets us first understand:
What is Quantum Computing?
Well, quantum computing is quite similar to traditional computing as it relies on bits to encode information. But what makes quantum computing unique is its version of bits, also called a qubit.
Qubit makes quantum computing revolutionary as it can have information in multiple states at the same time. This results in the effects of quantum mechanics, such as entanglement and superposition.
And, if you are wondering, yes, this is the spooky world of Schrodinger’s cat, both alive and dead.
According to Dr. Jay Gambetta, vice president of IBM Quantum, “Quantum computing is a new kind of computing, using the same physical rules that atoms follow to manipulate information.”
“At this fundamental level, quantum computers execute quantum circuits—like a computer’s logical circuits, but now using the physical phenomena of superposition, entanglement, and interference to implement mathematical calculations out of the reach of even our most advanced supercomputers.”
Quantum computers aren’t mainstream yet; their arrival will require algorithms. Google, recently, launched a new version of TensorFlow Quantum (TFQ), the TensorFlow framework. TFQ is an open-source library for prototyping learning models to make it possible for developers to create hybrid AI algorithms.
TFQ, a smart amalgamation of TensorFlow and Cinq, allows building deep learning models combining both traditional and quantum computing techniques to run with minimal lines of Python.
Google AI, in a blog post, said TFQ has been designed to provide the necessary tools to bring in the techniques of quantum computing and machine learning research communities together to build and control artificial quantum systems. e.g., Noisy Intermediate Scale Quantum (NISQ) processors with ~50 – 100 qubits.
Quantum computing aims to extend the abilities of traditional computing by performing tasks accurately and efficiently than conventional computers. Experts believe that, instead of replacing their traditional counterparts, quantum computers will leverage classical computers to support their specialized abilities.
How Quantum Computing Can Benefit Artificial Intelligence?
To run increasingly complex programs, scientists have been trying to better software for decades; however, software optimization has its limitations.
With increasingly complex machines, businesses will require more powerful machines sooner or later. Therefore, experts are working to figure out a way to extract value by expediting this process from the unmanageable swaths of data. This has given rise to a new discipline known as Quantum Machine Learning.
Reportedly, quantum computing will grow from USD 93 million in 2019 to USD 283 million by 2024, at a CAGR of 24.9%.
Now, we know that quantum machine learning is more efficient than classic machine learning. However, there is still no known scope as to what extent do these models appear in practical applications.
So, let’s pounce on the ways quantum computing could change the future of artificial intelligence:
We are dealing with newer technologies every day, for instance, AI and machine learning. These technologies tend to eat a lot of data which makes it difficult for traditional computers to evaluate massive datasets.
On the other hand, quantum computers are designed to manage huge datasets, along with spotting anomalies and uncovering patterns quickly. Developers can manage the potential of qubits with the newly launched iteration of new designs and improvements made on the quantum error-correction code.
Another way quantum computing can facilitate revolution, apart from sampling large datasets, is to be the same for solving all kinds of business problems. Quantum computers will give immense power to businesses for better decision-making.
2. Complex Problem Solving
Today, businesses are managing the growth of dataset that is faster than our computing resources. Quantum computers can complete these calculations within seconds, which today’s computers may take years to calculate.
Traditional computers work on a principle called superposition representing a combination of both zero and one, unlike traditional computers. Because of this, quantum computers are exponentially faster and can-do multiple calculations with multiple inputs simultaneously.
Google’s quantum computer can calculate 100 million times faster than today’s computer systems. Such a system is critical to process the monumental amount of data generated by businesses daily. The fast calculation can be used to solve very complex real-world problems by converting them into quantum language.
3. Building Better Models
With the increasing amount of data, businesses are losing ties with classical computing rope. They require complex models with the potential to process the most complex situations to have a better data framework.
Here, quantum computers play a huge role by creating better models with quantum technology. They lead to a decrease in a financial implosion in the banking sector, better treatments for diseases in the healthcare sector, and improve the logistics chain in the manufacturing industry.
4. Integration of Multiple Datasets
Organizations face the problem of varying amounts of data provided, either it could be too much or too little. Many time the data is placed in a variety of datasets to manage and integrate multiple numbers of datasets
Quantum computers can be used can makes the process quicker and analysis easier. This means that businesses will allow for quick analysis and integration of large datasets to improve and transform machine learning and artificial intelligence capabilities.
Quantum computers’ ability to handle many stakes makes them an adequate choice for solving business problems.
5. Combat Fraud Detection
Quantum computing applications with integration of AI, in the banking and financial sector, will improve and combat fraud detection.
A fraud detection model trained using quantum computers is capable of detecting hard-to-spot patterns using conventional equipment. However, improvement in algorithms helps in managing the volume of information.
Also, for the companies aiming to provide customers in the BFSI sector with tailored products, the best way to achieve this is by using advanced recommendation systems. Several quantum models could also be used to enhance these systems’ performance.
One starts to understand the caveats buried and the existing challenges of quantum computing when you dig deeper into the details.