What are you going to do when RSA algorithm is broken by quantum computer, machine learning, and artificial intelligence? This is just one of the fears people have about encryption. Is it real, or is it hype?
Quantum computing is currently rising on the Gartner Hype Cycle, and could become one of the most impactful disruptions of the modern era. Most people’s eyes glaze over when you try to explain the concept. And, yet, those in the know also fear the power of that same concept. How can something so misunderstood be poised to affect so many different areas across the spectrum of science and computing?
For CIOs, it’s critical to understand the reality of the quantum computing disruption and how it might become a practical platform for computing, machine learning (ML) and artificial intelligence (AI) in the future.
Is encryption at risk?
Some scientists have speculated that quantum computing would kill, or at least significantly weaken, cryptography. If true, this would jeopardize any business that relies on encryption. If a sufficiently powerful quantum computer becomes available within 10 or so years, any data that has been published or intercepted is subject to cryptanalysis by a future quantum computer.
Most security professionals believe that quantum computing will eventually render RSA cryptography and ECC useless but will not be able to effectively counter hash, code, lattice-based or multivariate-quadratic-equations cryptography until it has matured. Symmetric key cryptographic systems like Advanced Encryption Standard (AES), SNOW 3G, 3GPP and Kerberos are resistant to a quantum computing attack if they use a large enough key size. The problem is, we don’t exactly know how large a key, will be needed in the future.
However, it’s important to note that quantum computers will most likely never replace classic computers for general-purpose computing. They are probabilistic and not deterministic, but they do have a narrow set of algorithms.
What are the potential applications and impacts?
Despite the hype, this technology is still experimental and nascent.
Applying quantum algorithms to real-world problems will provide the greatest competitive advantage in future
On the Gartner Hype Cycle for Emerging Technologies 2017, quantum computing is climbing the Innovation Trigger phase. It currently offers limited business applications and can only run very specific quantum algorithms. Furthermore, the equipment is expensive, fragile, lacks standardization, with materials, designs and approaches varying wildly.
Gartner predicts that quantum computing-as-a-service (QCaaS) will be the predominant method used by data scientists to obfuscate this risk. Gartner recommends that organizations first focus on QCaaS to gain experience with quantum algorithms as they apply to business solutions. Because it’s a new field, everything must be built from ground up, plus it’s difficult to even comprehend the potential of the technology or the problems QCaaS could potentially solve.
However, the potential that quantum computing has for solving problems in ML, AI and big data, where classic computing limits potential, is driving a lot of innovation and growth among data scientists. Investors are putting millions of dollars toward the technology, and more than 50 companies, universities and research companies are working on development.
What are the applications?
Current and future applications for quantum computing will be narrow and focused. General-purpose quantum computing will most likely never be realized. However, the technology does hold the potential to revolutionize certain industries, including AI, cryptography, and even weather prediction.
For example, billions and billions of IoT devices are providing petabytes/ second of information, most of which is discarded because of the storage requirements. A weather prediction model might require millions of IoT devices, sensors, and external feeds such as satellite imagery and radar information all transmitting continuous data that ideally, could be analyzed instantaneously. Due to this, all this information would have to be loaded directly into quantum memory resulting in immediate analysis. This continuous analysis could potentially provide meteorologist with more accurate weather forecasting. Other applications include:
• Machine learning: Improved ML through faster structured prediction. Examples include Boltzmann machines, quantum Boltzmann machines, semi-supervised learning, unsupervised learning, and deep learning.
• Artificial intelligence: Faster calculations could improve perception, comprehension, self-awareness, and circuit fault diagnosis/binary classifiers.
• Finance: Quantum computing could enable faster, more complex Monte Carlo simulations, for example, trading, trajectory optimization, market instability, price optimization and hedging strategies.
• Healthcare: DNA gene sequencing, such as radiotherapy treatment optimization/brain tumor detection, could be performed in seconds instead of hours or weeks.
• Computer science: Faster multidimensional search functions, for example, query optimization, mathematics, and simulations.
Not every CIO needs to worry about quantum computing, but for now, those looking to explore the technology should focus their data scientists on the advancement of quantum algorithms and how they can be applied to solve practical business problems. Applying quantum algorithms to real-world problems will also provide the greatest competitive advantage in future.