Over the last decade, advances in computing have given us a teaser of what artificial intelligence is capable of. Through machine learning, algorithms can learn on their own using large amounts of real-time data. These algorithms can answer myriad questions, including what we should buy, what we should watch, and who we should date. However, the true benefits of AI and machine learning are yet to be discovered, and they extend to more impactful application areas such as computer vision, speech recognition, and medicine.
Artificial intelligence is a mammoth computing challenge because of the large amount of new data generated every day. Cisco forecasts that by the year 2020, annual global data center traffic will reach 1.3 zettabytes (1 trillion gigabytes) per month, and Gartner estimates the number of connected devices in the world will be more than 20 million by then. At this year’s IEEE International Solid-State Circuits Conference, it became clear that quantum computing will make it possible to process exponentially increasing amounts of data necessary for machine-learning applications.
SAY HELLO TO QUANTUM COMPUTING
Quantum computing has long been referred to as the “sleeping giant” of computing. It has the potential to tackle large mathematical problems beyond the reach of supercomputers, but its scalability remains limited by the extreme cooling required to keep quantum bits (qubits) stable and the bulky equipment required to read and write quantum data.
What is quantum computing, and why is it so fast? In contrast to classical binary data, which can be only a 0 or a 1 at any one time, a quantum state can be both a 0 and a 1 at the same time. That enables exponentially faster computation using specialized hardware, leading to faster analytics and predictions, which could enable advances in cybersecurity, surveillance, fraud detection, legal research, and early disease detection.
Quantum computing cannot arrive fast enough. As big data and the Internet of Things continue to proliferate, the amount of data collected is exceeding the rate at which we can process it. Semiconductor chips for high-speed machine learning are a step in the right direction, but the true realization of AI will happen only after we solve some of the basic problems with quantum computing.
FACE THE FACTS
The first, and probably most challenging, problem is cooling qubits down to cryogenic temperatures (below minus 150 °C) to preserve quantum states. Second, new algorithms must be developed that specifically target quantum hardware. IBM recently released a free platform, the Quantum Experience, that allows anyone to connect to the company’s quantum processor to experiment with algorithms and learn how to manipulate quantum data. Such open projects are a step toward building a community that will understand how to apply AI algorithms to quantum computers, once they do become available.
The third challenge is building and integrating enough qubits to be able to solve meaningful problems. Researchers at the QuTech research center in Delft, Netherlands, are working on this grand challenge with interdisciplinary teams as well as industry partners such as Intel and Microsoft. Today D-Wave of Vancouver, B.C., Canada, is the only company selling quantum computers. The company’s recent announcement of a 2,000-qubit machine for defense and intelligence applications shows promise that ubiquitous quantum computing is not too far away.
WILL QUANTUM COMPUTERS KEEP US SAFE?
Once quantum computers know everything about us and can predict our next moves, what happens then? Will we be safe? Will our data be protected? There are many unanswered ethical and technical questions, but luckily researchers have kept up.
Security and cryptography for the quantum world have been hot areas of research for the past 25 years. Technologies such as quantum key distribution will provide us with a means to communicate securely, while post-quantum cryptography will ensure that our encrypted data remains safe, even during brute-force attacks by a quantum computer.
The IEEE Rebooting Computing Initiative has an important role to play in the development of next-generation computing paradigms, which span across multiple technical areas including circuits and systems, components and devices, and electronic design automation.
Human life expectancy continues to rise, and quantum computing–based technologies undoubtedly will help us solve some pressing challenges in the coming decades. However, we must ensure that the energy consumption of quantum-based technologies remains feasible and within the confines of the planet’s natural resources.
Public policy groups such as IEEE-USA should continue to work with governments to ensure adequate funding for science and engineering jobs and research, while simultaneously expressing concerns about the importance of energy regulations. We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology’s impact on the environment.
IEEE Member Mario Milicevic is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of Toronto. His research focuses on the integrated circuit design of error-correction decoders for wireless, optical, and quantum security systems. Milicevic served as the 2015–2016 chair of IEEE Young Professionals and helped launch the IEEE Entrepreneurship effort.