Highlights:
- Overview of techniques for representing classical data on quantum computers.
- Discusses the advantages of gate-based quantum computers over quantum annealers for universal quantum computing.
- Introduces methods like binary, angle, and amplitude encoding for converting text, numbers, and images to quantum states.
- Applications of quantum image processing and potential exponential memory savings for large datasets.
TLDR:
The paper by Thomas Lang and colleagues explores methods for representing various types of classical data—text, numbers, images—on gate-based quantum computers. With techniques like binary and angle encoding, classical information is transformed into quantum states, allowing for efficient quantum computing applications, including quantum image processing and optimization.
Quantum computing has captured the imaginations of scientists and industries alike, promising revolutionary changes in fields like cryptography, data processing, and machine learning. A vital step toward realizing these breakthroughs is effectively representing classical data—such as numbers, text, and even complex images—on a quantum system. In a recent study by Thomas Lang and his colleagues at the Fraunhofer Institute for Integrated Circuits, several key methods are reviewed that allow this transformation to occur.
The Potential of Gate-Based Quantum Computing
Gate-based quantum computers, unlike their counterparts—quantum annealers, offer a broader range of computational possibilities. Annealers are primarily suited for optimization problems, whereas gate-based systems provide access to universal quantum computing, making them ideal for solving more complex issues. However, before these machines can tackle real-world problems, classical data must first be translated into quantum-readable formats.
Representing Classical Data: Text, Numbers, and Categorical Values
Classical data takes many forms. The researchers start by reviewing how simple text strings can be encoded, highlighting basic binary conversions alongside advanced techniques like word2vec, which utilizes machine learning to create high-dimensional vectors for words.
Similarly, categorical data (think genders or city names) can be encoded using either ordinal or one-hot encoding. For instance, a category such as “red,” “blue,” or “green” could be mapped as numerical values and then converted into quantum states for further processing. The paper suggests using machine learning for even more efficient embeddings.
From Binary to Angle Encoding
Numbers, the most basic form of data, are relatively easy to translate. By converting integers into binary strings, these values can be represented as bit strings on quantum devices. But binary encoding isn’t always efficient, particularly when dealing with real numbers. Here, angle encoding offers an elegant solution—by using the rotation of qubits, values are stored as phases in quantum states.
The study explains how these methods ensure data is efficiently encoded without overloading the system’s limited qubits, a current limitation in quantum devices. Angle encoding, for example, requires fewer qubits while preserving the data’s complexity.
Quantum Image Processing
One of the more intriguing aspects of quantum computing is its potential to process and store images with incredible efficiency. By converting images into matrices and embedding them on quantum devices, massive datasets can be compressed. The paper outlines several techniques like Flexible Representation of Quantum Images (FRQI) and NEQR, both of which offer new ways to represent images as quantum states, allowing quantum computers to handle complex image-based tasks, such as medical imaging or satellite imagery.
These methods reduce the need for large amounts of memory, giving quantum computers the ability to perform tasks exponentially faster than classical computers.
Graph Encoding and Advanced Applications
Graph-based data is another area of classical information that quantum computers can excel at processing. By converting graphs into quantum states, quantum computers can process relational data far more efficiently than classical machines. This has promising implications for fields like network theory and logistics, where graph-based data plays a central role.
Conclusion
Lang and his team’s work presents a future where quantum computers will handle everything from text encoding to large-scale image processing. The techniques reviewed in their study not only pave the way for more effective use of quantum devices today but also signal a major leap forward in the application of quantum computing across multiple industries.
Source: Lang, T., Heim, A., Dremel, K., Prjamkov, D., Blaimer, M., Firsching, M., Papadaki, A., Kasperl, S., & Fuchs, T. (2024). Representation of classical data on quantum computers. arXiv:2410.00742.