Weighing quantum AI's business potential
Quantum AI has the potential to revolutionize business computing, but logistic complexities create sizeable obstacles for near-term adoption and success.
New trends in AI have to be carefully evaluated in terms of their benefits and challenges for businesses, and quantum AI is no exception. A nascent branch of the much-hyped field of quantum computing, it uses quantum computing to implement machine learning algorithms.
Quantum computing has been heralded as the future of computation, yet there is confusion around how it's different from traditional computing. For starters, while a bit is the most basic unit of information in a traditional computer, a qubit is the basic unit in quantum computers.
Because today's quantum computers are small, they are impractical for performing most traditional computing functions. Therefore, while traditional computing is likely to dominate most business applications for the foreseeable future, there are certain business issues that lend themselves to quantum approaches.
Quantum AI business strategies
Quantum algorithms provide tremendous increases in computational speed to solve certain problems. For example, quantum AI can be very good at assisting machine learning, especially where that learning takes the form of neural network models.
While classic computing methods artificially create these hidden node models, they can be built naturally with qubits. The fundamental entanglement associated with backpropagation, a mathematical tool for improving the accuracy of predictions made by a machine learning model, can be computed much faster with qubits. This means training neural networks on quantum computers can be orders of magnitude faster.
Another space is nonstructured database searches, for which there is an ever-increasing set of problems that will exist as the internetworking of global computation creates massive amounts of data. While classical computers do an excellent job of searching through structured data, searches through unstructured data are much less efficient. Lov Grover, an Indian-American computer scientist, developed a quantum algorithm that can guarantee a dramatic speedup in searches. On small data sets, a speedup is not significant, but on large volumes of data, the practical speedups are significant.
Consider quantum AI's benefits, drawbacks
Benefits become apparent when quantum computers introduce algorithms that can solve problems exponentially faster. In practice, these "better" algorithms will only make sense where the problem space is large enough to justify the cost of quantum computing. As quantum machines get larger and pricing per qubit decreases, the solution space for these problems will increase.
There is no straightforward answer as to whether using quantum AI makes sense for any given company. Business executives need to evaluate the costs against the benefits to determine when quantum AI is going to bring something compelling to their businesses. Three current drawbacks of quantum AI include the following:
Elusive ROI. Today, the ROI will be elusive in most cases. Even where the quantum algorithms prove faster, the cost is currently high enough to limit current investments to large governments, university research and large enterprises that can justify their investments.
Error correction is needed. Quantum data is very "noisy." To get clean or "perfect" data, systems need to be able to quiesce. Existing qubits do that in near absolute zero temperatures. The further from absolute zero you get, the more noise you will find in these systems. Therefore, to make these systems viable, the systems need to be built both in very cold temperatures and with error correction as a fundamental part of the architecture. This means such systems are typically even more expensive than experts anticipate.
Integration costs to classic systems. In order for quantum AI systems to be used today, they will need to be integrated into existing classical computing infrastructure, which adds yet another layer of cost and complexity.
Quantum AI clearly holds tremendous promise for the future of business computing. However, the current costs, complexities and niche algorithms remain sizeable obstacles for the near-term adoption of quantum AI for most businesses.
