Streaming data and quantum machines
NISQRC Algorithm Could Allow QCs to Take on Streaming Data
The Quantum Insider reports:
- A new algorithm enables quantum machines to learn from continuously flowing data streams, overcoming a major hurdle that has previously limited their applicability to dynamic real-world scenarios.
- Quantum memory is so fragile it is difficult to retain information long enough to process temporal data effectively.
- In theory, quantum computers could now learn from patterns and relationships within the data stream, even as individual bits fade away.
Researchers have achieved a significant advance in real-time quantum computing with the development of Noisy Intermediate Scale Quantum Reservoir Computing, NISQRC, according to a study posted on the pre-print server ArXiv. This novel algorithm enables quantum machines to learn from continuously flowing data streams, overcoming a major hurdle that has previously limited their applicability to dynamic real-world scenarios.
The key challenge addressed by NISQRC lies in the inherent limitations of current quantum hardware, according to the team of scientists from Princeton, IBM and Raytheon. Quantum memory is notoriously fragile, making it difficult for these machines to retain information long enough to process temporal data effectively. NISQRC cleverly leverages the existing leakage of information from qubits, the fundamental units of quantum information, to create a persistent memory within the system. This allows the quantum computer to learn from patterns and relationships within the data stream, even as individual bits fade away.
You could think of it as trying to decipher a long, complex message whispered in snippets. Classical computers struggle with such “streaming data,” their rigid brains needing the whole message before making sense of it and often require special techniques to overcome that limitation. You and I might need struggle to completely understand the message with this missing data, too. However, this new method offers a peek into a future where computers can learn as they listen, even with messy signals and limited attention spans.
In a successful demonstration, researchers implemented NISQRC on a 7-qubit quantum processor to tackle the task of equalizing a noisy wireless channel. The results were promising, showcasing the algorithm’s ability to learn the channel’s characteristics and improve signal quality on the fly.
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