Three Key Quantum-Inspired Techniques

While several quantum-inspired techniques are available, three stand out as particularly worth evaluating. Not every technique will be applicable to every industry, organization or problem, so leaders should assess each against their specific context and identify which ones merit exploration.

Quantum-Inspired Feature Engineering for Machine Learning

First, there’s quantum-inspired feature engineering for machine learning. Feature engineering is the process of reshaping or enriching data before it is consumed by a machine learning model so that it has a better chance of finding meaningful signals.

Quantum-inspired feature engineering borrows concepts from quantum physics to present data in a richer, higher-dimensional space, making it easier to detect subtle patterns that classical models alone might miss. In turn, the sensitivity and accuracy of predictive models and algorithms can increase, which is particularly effective for use cases such as fraud detection and payment processing in financial services. The approach also carries practical operational advantages, according to a research paper on quantum-state-based feature engineering for machine learning, it has: “low computational cost, almost no additional parameters, and is easy to integrate with other modules.”

Quantum-Inspired Simulated Annealing

The second technique is quantum-inspired simulated annealing. In metallurgy, annealing is defined as “heat treatment that primarily consists of heating a material to a specific temperature, [and] holding it at that temperature for a specific time until microstructural changes have taken place. Then, the said material is cooled.” In other words, annealing describes the process of heating a material and gradually settling into its lowest energy, most stable state. Simulated annealing applies the same logic algorithmically. It explores a problem’s solution space and progressively narrows it, converging on a solution the same way cooling metal converges on stability.

While quantum annealers exist as physical devices, organizations can also run quantum-inspired simulated annealing algorithms on classical computers. This approach is well-suited for solving optimization problems, especially those involving many variables and constraints. A few use cases include determining optimal delivery routes, coordinating an hourly workforce, and minimizing fuel usage while still meeting delivery requirements.

Quantum-Inspired Monte Carlo Alternatives

Finally, there’s quantum-inspired integration. In calculus, calculating the area under a curve becomes exponentially harder as the number of variables increases—a challenge that maps directly onto complex business decisions. Consider trying to calculate the value of an exotic financial derivative. Often, the best available techniques are Monte Carlo simulations, which “forecast a range of possible outcomes by modeling randomness in a system” using “historical data and statistical assumptions about market behavior.”

The drawbacks of Monte Carlo simulations, however, are that they rely on approximations and can require millions of simulations to achieve a reasonably accurate answer. Quantum-inspired techniques can offer organizations a path to improved computational efficiency, which in turn can support more accurate valuations and risk assessments. This can be particularly beneficial in financial services, as well as in asset-heavy industries such as manufacturing, where companies face similarly complex calculations when pricing long-term supply contracts, hedging commodity exposure or assessing risk on energy-linked derivatives.

The Potential Benefits Of Using Quantum-Inspired Techniques—And How Leaders Can Get Started

Quantum-inspired techniques offer three core benefits for organizations.

First, they can generate near-term value by improving or enhancing work that is already underway. In the life sciences and healthcare industries, quantum-inspired feature engineering for machine learning can accelerate drug discovery by uncovering promising molecular structures more efficiently and quickly.

Second, they can build leaders build the skills and expertise at their organizations needed for the quantum future. Quantum computing is a complex discipline that requires considerable training and specialized knowledge. Quantum-inspired techniques offer a path to quantum fluency. Leaders who begin developing that capability now will be better positioned and less likely to face a talent gap as the technology matures.

Finally, the organizations that implement quantum-inspired techniques will likely have a head start on quantum computing as the technology becomes more widespread in the corporate world. The more prepared leaders are, the more likely they’ll be able to weather whichever form the quantum future takes and capitalize on it once it’s commercially viable.

However, successfully implementing quantum-inspired techniques requires expert guidance to navigate the subtleties and complexities. In a number of cases, I’ve observed organizations attempt to implement quantum-inspired techniques without expert help, and their efforts stalled or caused disappointing results.

Additionally, implementation is only part of the equation—maintenance is also necessary. For instance, if an organization uses quantum-inspired feature engineering for machine learning, those models will need to be retrained periodically.

Why It’s Vital For Leaders To Start Their Organizations’ Quantum Journeys Today

From my observations, quantum-inspired feature engineering and simulated annealing are already in use across industries. Quantum-inspired integration is more nascent, but I predict that its adoption will accelerate as awareness grows.

Whichever quantum-inspired techniques leaders decide to explore, the time to start is now. Quantum computing’s move to the corporate world will likely occur sooner than many leaders anticipate. Organizations that begin their quantum-inspired journey today will not only capture near-term value but will be far better equipped for their quantum futures.

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