Quantum A.I. : An intelligent mix of Quantum+A.I.

The biggest scientific & technological revolution of the decade, Artificial Intelligence, has the potential to flourish human civilizations like never before. At the surface level, it seems to be all about automated functioning and intelligent coding. But at the core, algorithms require huge data, quality training, and complex models. Processing of these algorithmic computations need hardware. Presently, digital computers operate on the classical Boolean logic. Quantum computing is the next-gen hardware and software technology, based on the quantum law. It typically means that, they use qubits instead of the boolean logic in order to speed up calculations.

The concoction of the both path-breaking techs, i.e. AI and Quantum Computing is said to be the future of technology. Quantum A.I. is all about implementing fast computation capabilities of quantum computers to Artificial intelligence based applications.

Understanding Quantum Computing

Before we jump into Quantum A.I., let us first understand Quantum Computing in detail. In physics terminology, quantum mechanics is the study of nature at the atomic and subatomic level. Totally opposite of classical physics theory which describes the nature at macroscopic level. At the quantum level, nature particles may take form of more than one state at the same time. Quantum computing utilizes this fundamental quantum phenomena of the nature to process information.

Quantum computer stores information in the form of quantum bits, known as qubits, similar to the binary logic used by digital computers. However, the state of the bits is not defined. It can encode information as both 1s and 0s with the help of quantum mechanical principles of superposition, entanglement, and tunneling. The use of quantum logic enables a quantum computer to solve problems at an exponentially faster rate than present day computers. Physicists and researchers consider that quantum computers are powerful enough to outperform the present processors.

Quantum Computing for Artificial Intelligence

Regardless of smart AI algorithms, a high-processing hardware is essential for them to function. Current GPUs, allow algorithms to run at an operable speed, a speckle of what quantum computing does.

Quantum computing approach helps AI algorithms undergo exponential speedups over existing digital computers. In this way it will ease problems related to machine learning, clustering, classification and finding constructive patterns in large quantities of data. Quantum learning amalgamates with AI to speed up ML and AI algorithms in order to develop systems which can better interpret, improve, and understand large data sets of information.

Specific use cases in the area of Quantum AI:

Random Number Generation

Classical, digital computers are only able to generate pseudo-random numbers. They use computational difficulty for encryptions, making them easily crackable using quantum computers. Certain machine learning algorithms require pure random numbers to generate ideal results, specifically for financial applications. Quantum systems have the mechanism to generate pure random numbers as required by machine learning applications. QRNG (Quantum Random number generator) is a quantum computer by Certes Networks, used for generating high-level random numbers for secure encryption key generation.

Quantum-enhanced Reinforcement Learning

Reinforcement learning is an Artificial intelligence area which allows agents to learn about an environment and take actions to achieve rewards. Usually it is time consuming in the initial training process and choosing an optimal path. With the help of a quantum agent, the training time reduces dramatically. Additionally, a quantum agent is thorough with the description of the environment after the end of each learning process. This is marked as an advancement over the classical approach where reinforcement learning schemes are model-free.

Quantum-Inspired Neural Nets

Quantum neural networks leverage ideas from the quantum theory for a fuzzy logic based neural network implementation. Current Neural network in the areas of big data applications are generally difficult to train as they use a feedback loop to update parameters in the training phase. In quantum computers, quantum forces such as interference and entanglement can be used to quickly update parameters in the training phase, easing the entire training process.

Big data Analytics

Quantum computers have the ability to handle huge amount of data generated and will continue to do so at an exponential rate. Using quantum computing techniques for big data analytics, useful insights would be within every individual’s reach. This would lead to better portfolio management, optimal routing for navigation, best possible treatments, personalized medications, etc. Empowering Big data analytics with quantum computing will ease out sampling, optimizing, and analyzing large quantities of data, giving businesses and consumers better decision making ability.

These are few examples in terms of measuring Quantum AI capabilities. Quantum computers powered by Artificial Intelligence is set to have tremendous impact in the field of science and engineering.

Ongoing Research and Implementation

Google plans to build a 49-qubit quantum chip by the end of 2017. Microsoft CEO, during his keynote session at Microsoft Ignite  made the announcement of a new programming language designed to work on quantum simulator as well as quantum computer. In this rat race, IBM successfully built and measured a 50 qubit quantum computer. Additionally, Google is collaborating with NASA to release a number of research papers pertaining to Quantum A.I. domain.

Rigetti Computing plans to devise a computer that will leverage quantum physics for applications pertaining to artificial intelligence and chemistry simulations. They will offer a cloud based service, on the lines of Google and Microsoft for remote usage.

Volkswagen, a German automaker, plans to collaborate with Google quantum AI to develop new-age digital features for cars and intelligent traffic-management system. They are also contemplating to build AI systems for autonomous cars.

Future Scope

In the near future, high-level quantum computers will help in development of complex AI models with ease. Such Quantum enhanced AI algorithms will influence application development in the field of finance, security, healthcare, molecular science, automobile and manufacturing etc. Artificial intelligence married to Quantum computing is said to be the key of a brighter, more tech-oriented future. A future that will take intelligent information processing at a whole new altitude.