Quantum machine learning is a relatively new field that combines machine learning concepts with quantum computing. The latter is a rapidly evolving technology that utilizes the laws of quantum mechanics to facilitate high-level problem-solving. Quantum computing is viewed in many ways as the successor of classical computers — subsequently, quantum machine learning would be the successor of classical machine learning models.
The core goal of quantum machine learning is relatively simple: Speed up machine learning processes by applying quantum computing concepts. While the premise of quantum machine learning is straightforward, the field itself is anything but. The theory of quantum machine learning is derived from the various concepts of quantum computing, machine learning, probabilistic theories, and classical ML models.
If researchers successfully bring their theoretical quantum machine learning concepts to life, this emerging field has the potential to reshape businesses and leave an undeniable impact on the lives of consumers. At the more granular level, we believe that applying quantum computing to machine learning can:
Improve Run Time
Broadly speaking, run time refers to how long it takes an algorithm to process and analyze a data set. Under the classical computing model, processing massive data sets is incredibly time-consuming and resource-intensive.
While the size of the data set will still have a direct impact on the run time of a quantum computing algorithm, the quantum model is more efficient in many instances. Specifically, users can decrease run time by coding their algorithm in an efficient manner that utilizes a minimum number of qubits (the basic unit of quantum information). Due to the nature of qubits and the phenomenon known as superposition, qubits can represent more than a single value, thereby facilitating a more condensed algorithm.
Several theoretical far-term quantum computing algorithms already have the potential to significantly speed up run time by replacing some commonly used classical computing models. However, this use case will be time-consuming and costly in the initial phases, as creating the long-form models needed to perform these calculations is challenging. As such, these theoretical algorithms are at least a few years away from being developed.
Despite this, many researchers are incredibly optimistic about these use cases of quantum machine learning. This positive outlook is largely fueled by the success achieved through the limited application of near-term quantum algorithms.
Increase Learning Efficiency
While improving the run times of machine learning models using quantum computing will certainly boost efficiency, there are other ways to do so–such as the fact that QML models have the potential to learn from smaller amounts of data.
When compared to classical models, QML models yield more efficient learning. This alone represents a huge leap forward for researchers, as they can sort through incredibly complex challenges in a fraction of the time. Expediting data analysis is one of the most promising use cases for QML, as these models could be applied to climate research, pharmaceutical R&D projects, and countless other initiatives.
Machine learning speeding up algorithmic analysis of climate change, as envisioned by Midjourney AI
Another efficiency-related factor that researchers consider is accuracy. Classical computing models are accurate in their own right, but they cannot reveal as many correlations. As a result, there is a greater chance that the “right answer” the researcher is looking for goes overlooked.
Theoretically, every quantum computation could be performed using classical models, but doing so would take an exponential amount of resources. This is because classical models process problems in a serial manner, whereas quantum machine learning models analyze variables simultaneously (or in parallel).
Boost Learning Capacity
Instead of focusing on raw speed for near-term devices, many researchers are more concerned with quantum machine learning models and their ability to increase ML capacity. After all, quantum models provide improved machine learning capacity due to their ability to tap into probabilistic models that are non-classical in nature. So from a practical standpoint, quantum computing machine learning models can efficiently factor and classify complex yet condensed data sets.
Additionally, quantum machine learning models are capable of revealing correlations that are hard (if not impossible) to identify using classical models. In turn, users can leverage these insights to uncover trends or key data points, make more accurate predictions, etc.
Quantum machine learning models can run through far more permutations and analyze the data yielded from each interaction. Conversely, classical models are most efficient at analyzing pairs of correlations.
Quantum machine learning offers a new computing horizon for the future
In the long term, the increased learning capacity and efficiency of quantum machine learning models may prove useful for solving some of the world’s greatest challenges. For instance, researchers might be able to leverage quantum machine learning models to predict how human greenhouse gas emissions will influence the climate. However, the models that are robust enough to support this sort of high-level problem-solving are still in the theoretical phase of development.
Most researchers agree that quantum models will significantly impact machine learning capabilities, enterprise companies, and society as a whole. But they also believe that businesses and consumers will not experience any real-world benefits from these improvements for at least two years — if not longer.
With that being said, the theory and practice of quantum computing have accelerated at an unprecedented rate in the last few years. This revolutionary field continues to push the limits of what is possible in the computing realm, which means that researchers may be just one major breakthrough away from outpacing that five-year timetable.