How Quantum Computing Supports Artificial Intelligence

The revolution of artificial intelligence will manifest with contributions from quantum computing.

Photo by Maurício Mascaro from Pexels

Introduction and Context

This post is a follow-up to my previous article on NewsBreak titled The Significance of Quantum Computing for the Future of Artificial Intelligence.

So, my friends keep asking me: "What is quantum computing got to do with artificial intelligence?".

I am glad they ask as this is a trillion-dollar question considering just a single company like Toshiba targets $3 billion in revenue in quantum cryptography by 2030. IBM, Microsoft, Google, Honeywell, and D-Wave have already invested billions of dollars in quantum computing.

Government organizations are also investing. For example, the US Department of Energy (DOE) announced $218 million in funding for 85 research awards in the important emerging field of Quantum Information Science (QIS). The US government begins a $1 billion quantum computing plan to get ahead of 'adversaries'.

Let me answer this important question by briefly focusing on the key points.

The critical premise of quantum computing is dealing with enormous amounts of data extremely quickly. It is an order of magnitude faster.

AI systems need this kind of agility to perform better for real-time speed. Autonomous vehicles are examples of this business use case.

There are many business use cases of quantum computing for AI. This video from scientists at MIT provides useful insights.

Let me introduce significant contributions of quantum computing to artificial intelligence solutions.

Natural Language Processing

Natural Language Processing (NLP) is a critical business use case for language processing. AI uses NLP algorithms to deal with translations and text analytics. Currently, NLP is a very process-intensive and costly solution for AI companies.

Current algorithms work based on characters and words. Quantum algorithms are designed to create the concept of being “meaning aware”.

So, these algorithms will be able to work on sentences and paragraphs to create real-time speech patterns. The Economist posted a video to YouTube focusing on how quantum computing will change the world.

Big Data Predictive Analytics

Predictive analytics as part of the Big Data domain are critical AI applications and business use cases.

AI is good at using machine learning, deep learning and neural networks based on massive data.

However, extraordinarily complicated and nebulous problems like stock market estimates and climate change control systems need unique data generated by quantum principles using entanglement and superpositions.

Here is a useful TED Talk about the application of AI and quantum computers in handling Big Data.

Nanotechnology and Nanoscience

Quantum computing can also integrate nanotechnology and nanoscience to AI for extremely small, microscopic objects at molecular, atomic, and sub-atomic levels.

Nanotechnology is an application of quantum physics.

To give you an idea about the magnitude of this scale, an inch includes 25,400,000 nanometres.

Here is a remarkable video introducing nanoparticles as a quantum phenomenon.

Rapid Training of Machine Learning Models

These are only a few highlights of the contributions of quantum computing to AI and machine learning. Data scientists have started developing machine learning applications for quantum devices.

They aim to use quantum computing for the rapid training of machine learning models and to generate optimized learning algorithms.

Google offers TensorFlow Quantum (TFQ) which is an open-source library for quantum machine learning to the quantum community.

Screen Capture from Google Quantum Computing Website

“TFQ integrates Cirq with TensorFlow, and offers high-level abstractions for the design and implementation of both discriminative and generative quantum-classical models by providing quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators”.

Here is the link to the TensorFlow Quantum paper: A Software Framework for Quantum Machine Learning by Google. "Overall, quantum computing will expedite constraint solving, uncertainty handling, constraint satisfaction, optimizing problems, neuromorphic cognitive models, adaptive machine learning, spatial and temporal reasoning."

Business and Economy

From a business and economic perspective, even though we are at the nascent stage of quantum computing, it is a good time for startup companies to join this journey.

For example, many investors are now following the solution offerings of Rigetti, QC Ware, Q-CTRL, ISARA, 1Qbit, IonQ. With these solutions and investments by supporters, quantum business solutions will mature and be an inevitable part of the AI and robotics journey.

Here is a video introducing the top 10 quantum computing business companies.

Conclusion

As speculated by financial experts and entrepreneurs, the future of our economy will not be determined by cryptocurrencies but by quantum computing solutions.

Thank you for reading my perspectives. I’d be delighted to obtain your insights.

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