Quantum Computing and Artificial Intelligence




Quantum Computing and Artificial Intelligence

Here, Sri Amit Ray discusses the power, scope, and challenges of Quantum Computing and Artificial Intelligence in details.

In recent years there has been an explosion of interest in quantum computing and artificial intelligence. Quantum computers with artificial intelligence could revolutionize our society and bring many benefits. Big companies like IBM, Google, Microsoft and Intel are all currently racing to build useful quantum computer systems. They have also made tremendous progress in deep learning and machine intelligence.  

Artificial intelligence (AI) is an area of science that emphasizes the development of intelligent systems that can work and behave like humans. Quantum computing is essentially using the amazing laws of quantum mechanics to enhance computing power. These two emergent technologies will likely have huge transforming impact on our society in the future. Quantum computing is finding a vital platform in providing speed-ups for machine learning problems, critical to big data analysis, blockchain and IoT. 

The main purpose of this article is to explain some of the basic ideas how quantum computing in the context of the advancements of artificial intelligence; especially quantum deep machine learning algorithms, which can be used for designing compassionate artificial superintelligence.

More details are discussed in the book, compassionate artificial superintelligence AI 5.0. Quantum computing has incredible role in understanding and designing mind and brain functions. The book discusses how quantum computing can be used in the five phases of Artificial Intelligence; namely Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), Artificial Consciousness, Artificial Super-Intelligence (ASI) and Compassionate Artificial Super-Intelligence (CAS).

There are two main reasons to discuss quantum computing and artificial intelligence here. First, is introducing the basic ideas of quantum computers. The second purpose is how they can be used to implement the full power of artificial intelligence. Especially, quantum neural networks is the next natural step in the evolution of quantum-neuro-computing systems.

What is Quantum Computing?

Quantum computing is developing computing power using the laws of quantum mechanics of particle physics. Classical physics applies to things you can see, where as quantum physics applies to the world at the scale of atoms and below.  Quantum particles can move forward or backward in time, exist in two states simultaneously and even “teleport.”

Just as a traditional digital computer requires that the data be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1),  a quantum bits (qubits) is the basic unit of information in a quantum computer. Unlike classical computing, where each bit represents either a 0 or a 1 but not both at once, a quantum bit simultaneously superposes 0 and 1 and only resolves (or “collapses”) to a single value when measured. 

Qubits represent the sates atoms, ions, photons or electrons and their respective control devices that are working together to act as computer memory and a processor. While a classical computer operates serially, essentially dealing with one bit after another, a quantum computer’s qubits interact parallel. Because a quantum computer can contain these multiple states simultaneously, it has the potential to be millions of times more powerful than today’s most powerful traditional supercomputers. 

Quantum computing uses mainly two properties of quantum particles followed by the laws of quantum mechanics: the principle of superposition of states and the concept of entanglement. Superposition is a “one-particle” property; while entanglement is a characteristic of two or more particles.

A quantum bit (qubit) can be thought of like an imaginary sphere. Whereas a traditional bit can be in two states – at either of the two poles of the sphere. A quantum bit can be any point on the sphere. This means a computer using these bits can store a huge amount of information using less energy and space than a classical computer. So rather than having bits that can only be 1 or 0 at any given moment, qubits can be anything and everything at once. This means they can perform many calculations simultaneously, giving them the potential for unparalleled processing power.   

So, while a bit represents just 1 or 0, one Qubit represents an array of possibilities and all can be calculated simultaneously taking probabilities in account.

Power of Quantum Computing

The ability to process the zeros and ones at the same time gives tremendous power. Thus a relatively small quantum computer could surpass the most powerful classical supercomputers. The power of quantum computing is limited by qubits not much by processing time. Quantum entanglement enables the quantum computers to principally perform many calculations at once. And the number of such calculations should, in principle, double for each additional qubit, leading to an exponential speed-up.

Quantum computers can solve problems that are impossible or would take a traditional computer an unrealistic amount of time (a billion years) to solve. Once the development of quantum computer gets settled, the time for machine learning will exponentially speed up even reducing the time to solve a problem from hundreds of thousands of years to seconds.

A quantum computer contained of 500 qubits would have a potential to do 2^500 calculations in a single step. This is an awesome number 2^500 is infinitely more atoms than there are in the known universe. A 30 qubits quantum computer will have computing power equivalent to 10 teraflops classical computer. To exceed the limits of classical computing, computers of at least 50 to 100 qubits are needed. Advances in quantum communication, quantum cryptography, which rely heavily on extending entanglement, could change the way information is stored, processed and transferred in the future.

Limitations of Quantum Computing

Quantum information is extremely fragile and requires special techniques to preserve the quantum state. The qubits are extremely sensitive to environmental noise. This can be noise from outside the device like temperature etc. It can also come from inside—the cooling system, microwave cabling, and the chip components themselves. Generally, the quantum systems requires special infrastructures to produce ultra-cold liquid helium of about -460 degrees Fahrenheit. This difficulty in developing and maintaining a cold enough environment for a quantum computer to operate is the main reason they are still experimental, and can only process a few qubits at a time.

Artificial Intelligence and Quantum Computing

As dicussed in my book (AI 5.0), artificial intelligence has five generations. They are; Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), Artificial Consciousness, Artificial Super-Intelligence (ASI) and Compassionate Artificial Super-Intelligence (CAS). Quantum computing can be used in all the five generations of AI. However, they are especially needed in the higher order AI systems. Especially, quantum computing and quantum neural networks learning algorithms have promises to design emotional intelligence part higher AI based systems.

Read: Quantum Computing Algorithms for Artificial Intelligence

Artificial Consciousness

Consciousness is the link between subjective inner perception and objective physical reality. Human experiences, feelings, emotions, dreams, and visions are part of consciousness. Machine self-awareness is part of Artificial Consciousness. 

But what is consciousness? There are three popular theories for consciousness. Firstly, some scientists think consciousness is a quantum phenomenon. Some scientists think consciousness is a mere illusion and they think that our thoughts and feelings don’t seem to be able to have any impact on changing the world around us. Some scientists think consciousness, whether awake or asleep, is largely due to the firing of neurotransmitters between neurons. But many others feel we have not grasped where consciousness comes from at all.

Let us put these philosophical issues aside for the moment. The fact is  consciousness gives us the power to think about others and the feelings about our inner experiences. The scope of quantum computing to develop these feelings and experiences are enormous. 

Quantum Neural Networks 

Integrating quantum computing with training and implementation of artificial neural networks has been studied by many researches [2].  Presently, deep learning algorithms have achieved “superhuman” levels of perception. New quantum algorithms of machine learning based on qubits like Quantum Convolutional Neural Nets (QCNN) and Quantum Reinforcement Neural Network (QRNN) or (QRL) [3]  are rapidly developing a new era of AI. In reinforcement learning, control strategies are improved according to a reward function. A reinforcement learning agent interacts with its environment in discrete time steps. Researchers observed that quantum neural networks can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks [4].

Summary:

We are on the verge of a new age of human discovery with quantum computing. A full-fledged true quantum computer has yet to be invented, and still appears years away, but quantum computing operations can be carried out on today’s qubit hardware. IBM has developed a number of quantum computer systems ranging from 5 to 50 qubits.  Currently, big companies like IBM, Google, Intel, and Microsoft are positioning themselves as a first mover in establishing the era of commercial quantum computing. Quantum computing will give power to the AI for developing higher levels superintelligent systems.

References:

 [1]  Compassionate Artificial Superintelligence AI 5.0 by Dr. Amit Ray, 2018

[2] C. Y. Liu, C. Chen, C. Ter Chang, and L. M. Shih, “Single-hidden-layer feed-forward quantum neural network based on Grover learning,” Neural Networks, vol. 45, pp. 144–150, 2013.

[3] T. Fösel, P. Tighineanu, T. Weiss, and F. Marquardt, “Reinforcement Learning with Neural Networks for Quantum Feedback.”, 2018

[4] Kwok Ho Wan, et al. Quantum generalisation of feedforward neural networks, Nature, Quantum Information, 2018.

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