Scientists say they created a breakthrough after developing quantum computing techniques to implement machine learning algorithms better than cutting-edge classic computers.
Researchers revealed their findings in a study published in Nature Photonics on June 2nd.
Scientists used methods that rely on quantum photonic circuits and bespoke machine learning algorithms.
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Using only two photons, the team’s method has successfully demonstrated an increase in speed, accuracy and efficiency over standard classical computing methods for running machine learning algorithms.
Scientists say this is one of the first times that quantum machine learning was used for real problems and offered the advantages that cannot be simulated using binary computers. Furthermore, they said that its new architecture can be applied to quantum computing systems with only a single kit.
Unlike many existing methods for achieving speedup through hybrid quantum classic computing technology, this new method does not require intertwined gates. Instead, it relies on photon injection.
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Essentially, the team used a femtosecond laser. It uses a laser (10° sec sec) that emits light with very short pulses measured in femtoseconds, which writes to a borosilicate glass substrate and classifies data points from the data set. The photons were then injected in six different configurations and processed by a hybrid quantum binary system.
Scientists have determined where photonic measurements are better than those made through classical computing by measuring how long it took photons to complete a quantum circuit. We then isolated the processes in which quantum processing provides benefits and compared the results with the classical output.
Researchers have found that experiments performed using photonic quantum circuits are faster, more accurate and more energy efficient than those performed using only classical computing techniques. This boosted performance applies to a special class of machine learning called “kernel-based machine learning”, which can have countless applications across data sorting.
Deep neural networks have become an increasingly common alternative to kernel methods for machine learning over the past decade, but kernel-based systems have revived in the past few years due to the relatively simplicity and advantages of working with small data sets.
Team experiments could lead to more efficient algorithms in the field of natural language processing and other monitored learning models.
Perhaps most importantly, this study introduces new ways quantum computers can identify superior tasks in hybrid computer systems.
Researchers say the technology used is scalable. This means that performance may improve even more as the number of photons and qubits increases. This will allow us to develop machine learning systems that can exceed the limits of today’s models.
Researchers argue that their approach “opens the door to hybrid methods in which photonic processors are used to improve the performance of standard machine learning methods.”
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