The phrase “quantum computing” sounds like something from a science fiction show, but it’s very real. Some federal agencies are already investing in it and more are sure to follow. Agency leaders need to understand what it is, how it differs from the traditional computer technology everyone uses, and what to do in order to implement it.

Put as simply as possible, quantum computing uses quantum mechanics to perform computation. These phenomena enable certain kinds of calculations at a speed and scale that conventional computers cannot come close to matching. However, much of its potential remains theoretical as scientists continue to refine the technology.

In particular, quantum computers can perform integer factorization quickly—that is, finding two previously undetermined prime numbers that produce a known number when multiplied. It might sound like an abstract math puzzle, but it’s actually an important aspect of encryption/decryption and artificial intelligence (AI). (A more complete explanation is beyond the scope of this post.)

President Trump issued an executive order in February 2019 titled “Executive Order on Maintaining American Leadership in Artificial Intelligence,” defining AI as a priority in research and development. While it does not specify quantum computing, it does provide a strong motivation for government agencies to explore the technology as a means to accomplish the end.

Jason Porter will take part in a panel discussion on AI's role in fighting fraud, waste and abuse on Government Matters TV on WJLA on June 30. Get details and broadcast times here

Getting smart about AI

Quantum computing, and related technologies such as hyperconverged infrastructure and scalable computing, provide ways to deliver powerful AI; but before bringing quantum into the picture, it is important to think through your use case and determine what you really need.

Define the outcomes. What are you looking for the AI to tell you? The goal of AI is to get the computer to think a bit on its own. To do that, you have to be specific about your desired outcomes. If you do not clearly define the desired outcomes, AI and machine learning components may provide skewed or misleading information.

Build for what you need. AI and ML are similar, but they are not the same. AI refers to systems that carry out processes on their own, requesting additional data or asking questions to refine the outcomes of processing information.    

With machine learning, on the other hand, you must tell the computer what it needs to learn. An ML-capable system uses operator-programmed workflows or algorithms to process the data sets it accesses. A machine learning system can learn, but only with direct instruction and management from human operators.

AI involves significantly greater cost and complexity, so do not build for AI if you only need ML.

Understand the data needed. Both AI and ML enable you to grab a much larger data set than previously practical, and process the data with complex algorithms to create actionable data and outcomes.  Both enable proactive and reactive responses, rather than a backward-looking forensic approach. Under a relief program pumping hundreds of billions of dollars into our economy, AI and ML can automate the management of large volumes of information within seconds. Human analysts can then focus on areas of need and not get lost in the volumes of information.

Rethink your expectations for data; don’t underestimate how much you can utilize with ML and AI. 

Take an Agile approach. Building a workflow to feed information into the algorithm can be an iterative process. Test early to see if you’re getting the expected results. Fine-tune the details to make ongoing improvements. 

Be alert to data source changes throughout your process lifecycle. If a source modifies the way it reports the data, or goes dark entirely, you are AI and ML engines must account for that without invalidating the output. 

The power of quantum

Quantum computing might provide a capability to process enormous volumes of data and information, to apply single and layered analytics to data with such speed that actionable data is published essentially in real/near time. Computations that once required hours or days using conventional servers could take only milliseconds through quantum computing.  Quantum computers can “see” data in layers, viewing everything simultaneously.

Quantum computing is admittedly challenging, and there are problems yet to solve before it will be widely practical. But innovators are making continual progress on it, and it holds tremendous potential for supercharging the next generation of technology.

To learn more about CGI Federal’s capabilities in artificial intelligence, machine learning and robotic process automation, visit our Intelligent Automation page.

About this author

Picture of Jason Porter

Jason Porter

Vice President

Jason Porter is Vice President of CGI Federal’s Emerging Technology Practice (ETP). He has more than 25 years of experience in government technology implementations, including AI, machine learning, physical security, operational surveillance, health care, C3I & C4I technology, data analytics, video analytics and profiling systems. ...

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