Quantum physics promises big changes for many fields, including machine learning—in theory, at least. In reality, quantum machine learning is still largely theoretical and has a long way to go before it becomes practical for widespread application.
When and if quantum computers become more feasible and accessible, they will greatly increase the speed of machine learning processing and open a lot of possibilities for new types of machine learning. Therefore, it’s important to understand what quantum machine learning is, how it theoretically works, how it compares to classical machine learning, and what challenges lie in store for the future of quantum ML.
What is quantum machine learning?
To grasp what quantum machine learning is, we first need to understand machine learning and quantum mechanics separately.
Machine learning (ML) is a subset of artificial intelligence (AI) and computer science that uses data, algorithms, and software to imitate the way that humans learn knowledge over time. On the other hand, quantum computing is a cutting-edge technology field that uses the principles of quantum mechanics to solve problems that are too complicated for classical computers to handle.
Thus, quantum machine learning effectively combines these two fields to create computers that can operate and learn at a scale far beyond that of “ordinary” machine learning.
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How does quantum machine learning work?
Because quantum computing machines are only just now becoming reality, it’s important to emphasize that most of the research on the nuts and bolts of quantum machine learning is hypothetical now. Most of what we currently know is based on propositions about how quantum machine learning should work, assuming that we can eventually build feasible and affordable quantum computers.
In classical computing, two classical bits can take one of four states: 00 or 01 or 10 or 11. This means that each pair of bits can only represent a single binary combination at any given time.
However, in quantum computing, a single quantum bit (qubit) can exist simultaneously. This means a single pair of interacting qubits can save all four binary configurations at once, allowing for much more intricate and powerful computations. This superposition allows the quantum system to solve complex equations faster and more efficiently when compared to classic computing.
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Classical machine learning vs. quantum machine learning
Quantum machine learning offers multiple potential advantages over traditional machine learning.
First, quantum systems can create odd, unusual, or counter-intuitive patterns that are not possible to create on a classical computer that lacks the capacity for quantum weirdness. This means that quantum systems can recognize patterns that are beyond the constraints of a classical computer system.
Quantum systems also offer the opportunity for parallel processing that is not possible on a classical computer. Quantum systems can achieve this because one qubit pair can save all four possible configurations at once, whereas a single pair of traditional bits can only save one binary configuration at a time. Not only do the operations themselves go faster, but quantum machine learning programs can also perform multiple operations simultaneously to speed things up even further.
This speed and parallel processing will have a big impact on the rate at which machine learning programs can advance. Part of the limitation with machine learning right now is that classical computers can only process information so quickly. Machine learning algorithms, by extension, can only learn so fast. Quantum computing will lead to significant increases in processing speeds that will allow machine learning to advance at a much faster pace than classical computing currently supports.
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Future challenges for quantum machine learning
While quantum machine learning is a very promising field, it does face multiple challenges.
The biggest and most obvious obstacle is that quantum computers are mostly theoretical at this point and viable hardware is not yet available. Until this obstacle is surmounted, all quantum fields—including quantum machine learning—will continue to remain largely untested in practicality.
Classical computing data also needs to be encoded in a quantum mechanical form that the quantum computer can process, which is another major challenge. This will hypothetically be done via integrations such as qRAM, but those hardware integrations haven’t been solved yet.
All forms of quantum computing offer great promise, but it remains to be seen how much can be realized in practice.
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