One of the most exciting fields emerging today is QML, which is an amalgamation of quantum computing and classical machine learning at the most basic level. Quantum machine learning is the application of quantum computing for machine learning algorithms, and as a research area, examines the interaction of concepts between these respective technologies.
In the latter regard, it might be a question of if quantum computers can create faster, more efficient training and evaluation cycles for machine learning models. Or if machine learning methods could be deployed to help build new quantum algorithms, for example.
QML is currently a hot industry topic and is positioned to be a major disruptor. This domain is still in the early stages of research, development, and growth, and applications continue to emerge. However, QML presents numerous compelling possibilities to further expand the scope of artificial intelligence and machine learning, and vice versa.
Exploring Quantum Machine Learning
As developments in quantum computing have rapidly accelerated alongside AI and machine learning, quantum technologies are being explored to improve learning algorithms. Quantum machine learning has grown from this line of thought. At its core, it embraces the idea of drastically improving the efficiency and solutions for existing problems that cannot be solved via classical computing.
Occasionally machine learning algorithms are too strenuous for classical computers, and quantum computers possess the computational capabilities to manage these kinds of algorithms and solve the problem much faster. Quantum computing relies on quantum bits, or qubits, which are different from the binary bits in a traditional computer because they’re a unit that not only can contain more data than a class binary bit, but they can simultaneously exist in more than one state — a concept called superposition. Calculating the state of a qubit would compromise its superposition state, which is why quantum computers calculate the probability of the qubit state prior to its observation.
Leading technology companies like Amazon, Google, IBM, and Microsoft have been investing in the development of massive-scale quantum computing software and hardware.
But there are many challenges associated with scaling this technology. Qubits are delicate and quantum information can be easily damaged or even destroyed if disturbed. The machines must therefore be maintained in secluded environments that operate at extremely cold temperatures.
The complexity of the software and hardware ecosystem needed to leverage this technology will most likely pose significant obstacles in widespread adoption and accessibility, and commercial application. For now, classical computing will be the primary vehicle for problem solving, and because mainstream machine learning remains constricted to classical computing, the potential for QML to push future AI application and quantum computing developments remains to be further explored.
Applications and Areas of Disruption
A recent report estimates that quantum computing technology will reach $1 trillion in global market value by 2030. As quantum computing gradually becomes more mainstream, value across applications such as prime number factorization, quantum simulation, optimization of multivariate problems, and QML are emerging. Organizations in industries like manufacturing, logistics, pharmaceuticals, and finance, automotive, and chemical are likely to be among the first to leverage the advantages of these developments.
This machine learning technology may generate new innovations in autonomous driving systems, most notably in accelerating the training process for these systems. Many car manufacturers run hours of diverse data through sophisticated neural networks to teach cars to make critical decisions. But training the algorithms using this approach demands computationally taxing calculations that become more complex as additional data and variable relationships are introduced, which strains even the fastest computers. Quantum computers, with their ability to conduct numerous complicated calculations with multiple variables at once, could increase the training of the AI and ML systems.
Other areas QML will potentially disrupt include:
- New material creation via atomic and molecular maps
- Drug discovery and medical research through molecular modeling
- Nanoparticle exploration
- Space exploration advancement
- Furthering cohesive security connectivity via unification of IoT and blockchain.
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Preparing for the Quantum Machine Learning Revolution
Organizations in the sectors that may benefit from the capabilities offered by quantum machine learning should start preparing and evaluating strategies for adoption and the potential value that this technology can offer in the near future. Data science professionals should likewise begin shaping their skills accordingly if they want to seize exciting new opportunities in this field.
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