Good news! This could be a huge leap!
"... QML combines the power of quantum computing with the predictive capabilities of ML to overcome the limitations of classical algorithms and offer improvements in performance. In their paper “On the role of entanglement in quantum-computational speed-up,” Richard Jozsa and Neil Linden, of the University of Bristol in the UK, write that “QML algorithms hold the promise of providing exponential speed-ups over their classical counterparts for certain tasks, such as data classification, feature selection and cluster analysis. In particular, the use of quantum algorithms for supervised and unsupervised learning has the potential to revolutionize machine learning and artificial intelligence.” ...
QML differs from traditional machine learning in several key ways
- Quantum parallelism ...
- Quantum superposition ...
- Quantum entanglement ...
Research on quantum machine learning began in the 1980s.
In the late 1990s and early 2000s researchers developed quantum neural networks to demonstrate the potential of quantum systems for machine learning that can be trained to recognize patterns in data. These networks have since been applied to a wide range of real-world problems. ...
A decade later, researchers developed quantum algorithms and software tools for machine learning tasks. These included quantum versions of popular machine learning algorithms such as support vector machines, decision trees and neural networks. ...
Financial markets are another area where QML has shown promise. A 2021 paper from JPMorgan’s Future Lab for Applied Research and Engineering concluded that QML can perform tasks such as asset pricing, predicting volatility, predicting the outcome of exotic options, fraud detection, stock selection, hedge fund selection, algorithmic trading, market making, financial forecasting, accounting and auditing, and risk assessment much faster and more accurately than classical algorithms. ...
Cybersecurity is another area where she predicts QML to make an impact. ..."
In the late 1990s and early 2000s researchers developed quantum neural networks to demonstrate the potential of quantum systems for machine learning that can be trained to recognize patterns in data. These networks have since been applied to a wide range of real-world problems. ...
A decade later, researchers developed quantum algorithms and software tools for machine learning tasks. These included quantum versions of popular machine learning algorithms such as support vector machines, decision trees and neural networks. ...
In the 2010s and 2020s, several companies and research groups developed quantum computers that could perform machine learning tasks. These included both gate-based quantum computers and quantum annealers.
By the 2020s, QML began to be widely adopted in applications including pattern recognition, natural language processing and optimization.
Today, one of the most promising applications of QML is drug discovery. ...By the 2020s, QML began to be widely adopted in applications including pattern recognition, natural language processing and optimization.
Financial markets are another area where QML has shown promise. A 2021 paper from JPMorgan’s Future Lab for Applied Research and Engineering concluded that QML can perform tasks such as asset pricing, predicting volatility, predicting the outcome of exotic options, fraud detection, stock selection, hedge fund selection, algorithmic trading, market making, financial forecasting, accounting and auditing, and risk assessment much faster and more accurately than classical algorithms. ...
Cybersecurity is another area where she predicts QML to make an impact. ..."
From the abstract:
"For any quantum algorithm operating on pure states we prove that the presence of multi-partite entanglement, with a number of parties that increases unboundedly with input size, is necessary if the quantum algorithm is to offer an exponential speed-up over classical computation. Furthermore we prove that the algorithm can be classically efficiently simulated to within a prescribed tolerance \eta even if a suitably small amount of global entanglement (depending on \eta) is present. We explicitly identify the occurrence of increasing multi-partite entanglement in Shor's algorithm. Our results do not apply to quantum algorithms operating on mixed states in general and we discuss the suggestion that an exponential computational speed-up might be possible with mixed states in the total absence of entanglement. Finally, despite the essential role of entanglement for pure state algorithms, we argue that it is nevertheless misleading to view entanglement as a key resource for quantum computational power."
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