It is easy to think of science fiction shows like Star Trek when the words ‘quantum’ and ‘computing’ are spoken. It doesn’t make the concept sound any less complex when you learn quantum computing executes calculations very quickly by harnessing the collective properties of superposition, interference, and entanglement. Thankfully, most of us don’t need to be concerned with the granular details. We only need to know that quantum computing means faster access to data and more secure networking.
With every document we save, link we click, and photograph we snap, we are the constant creators and consumers of data. According to Bernard Marr, humans produce at least 2.5 exabytes of data every day (the equivalent of 250,000 Libraries of Congress).
This tremendous inventory of data provides the basis for effective machine learning used by AI; the more information an algorithm can consume, the more successful it can be at making predictions or decisions. Unfortunately, exponential growth and the increased sophistication of queries requires the speed and stability offered by quantum computing.
AI is a general-purpose technology, rooted in big data. By analyzing datasets, AI can identify patterns and predict events. In the past, the bottleneck to improving AI was the cost of collecting and storing data. These days, the challenge is in consuming, searching, and providing meaningful results within a reasonable timeframe.
Improve Business Decision-Making Processes
As we head into the future of quantum computing, increased productivity and faster decision-making will be the name of the game. Analysing data, predicting trends, and reaching target audiences comes with considerable advantages.
How can quantum computing and AI lend value to your business decision-making processes? Consider the following possibilities, identified by industry:
- Enhance fraud detection, determine asset pricing, simulate trade activities, and analyse historical data to improve market predictions and limit financial risks.
- Utilities and Energy
- Process energy system data to assist with grid optimization.
- Review customer analytics to predict usage, preferences, and future needs.
- Expand simulations to include weather data or market trends (such as an increased number of electric vehicles) to offer insight into infrastructure upgrades that may be required to maintain service.
- Employ predictive analytics to assist with airline schedules and staffing.
- Recover from operational disruptions like mechanical failures, weather events, or even pandemic concerns using complex scenario modelling.
- Perform weather simulations for catastrophe modelling to drive the development of policy limits and to guide customer pricing.
- Attract and retain customers by finding ways to automate claims functions, predict preferences, and provide pre-emptive product and service recommendations.
- Track year over year sales to help predict inventory needs and manage supply chain management concerns.
- Provide access to the information provided by pharmaceutical companies outlining expected efficacies, potential side-effects, and contraindications.
- Predict the outcome of treatment plan options, harnessing the power of quantum simulation and multivariable scenarios to account for age, gender, underlying conditions, and geographical location.
- Provide just-in-time access to all medical imaging, while providing comparative analysis for abnormalities and anomalies.
- Streamline and automate administrative processes, identify service bottlenecks, eliminate costly redundancies, and increase the speed by which patients are able to access healthcare resources.
Also read: Data Management with AI: Making Big Data Manageable
AI and Quantum Security
Keeping up with the evolution of security threats and attacks has always been a challenge. By combining the data analytics power of AI with the speed of quantum computing, enterprises can better predict possible security risks and ward off potential attacks.
As quantum computing and AI evolve, it is important to understand that validating data is as important as analyzing it. Weaponizing data, corrupting analytics, and derailing the experiential learning being done by AI systems is an emerging form of cyberterrorism that should not be ignored.
Quantum Computing and AI Compliment DevOps
Quantum computing and AI are powerful allies for DevOps teams as they work to identify business priorities and objectives, design and develop new software solutions, and manage the ongoing maintenance and testing of existing applications.
DevOps teams can look at the data provided by AI to assist with regression testing, functional testing, and user acceptance testing. Because quantum computing provides AI with the ability to quickly and efficiently process data from numerous sources (like various, siloed, departments within a large organization), testing can be consistent and comprehensive.
Using Quantum Computing and AI to Assist ITOps
Where are our IT systems vulnerable? When do we need to upgrade our hardware or software? How do we resolve incidents faster? How much time is being spent managing tasks that could be automated? These types of ITOps questions are best answered with the analysis of big data. With the speed offered by quantum computing, these AI queries can deliver full visibility into operations data, providing insight in real time.
As businesses utilize quantum computing and AI, it is most exciting to consider how propelling these technologies forward can actually serve humanity as a whole by helping develop cures for diseases, detangling traffic, or protecting sensitive data.