Quantum Machine Learning

Quantum machine learning (QML) is an amalgamation of machine learning and quantum computing. The goal of QML is to use a quantum algorithm (or device) to solve a machine learning task with more incredible speed or accuracy than its classical counterpart. 

Quantum machine learning also involves exploring the methodological and structural similarities between various physical and learning systems like neural networks. Some mathematical and numerical techniques from quantum physics apply to deep classical learning and vice versa.

Some machine learning algorithms are extensively used for classical algorithms and are not used in high volume and processing real-time velocity. The inherent properties of superposition and entanglement of quantum qubits help speed up the classical algorithms used. 

While many innovations and research are towards understanding the impact that quantum technologies might have on machine learning, the field is one of the most rapidly evolving and promising.

Many quantum learning algorithms rely on the application of Grover's search algorithm, which includes mostly unsupervised methods: K-means, hierarchical clustering, support vector machines, or quantum neural networks.

Though many criterion are available to compare QML with corresponding classical ones; some prominent ones are:

  1. The loss function
  2. The time complexity
  3. The number of samples for the training dataset 

Many machine learning tasks face the curse of dimensionality as there are many more features available to model.  Quantum computing provides various optimization algorithms for dimensionality reduction when dealing with high-dimensional features and provides solutions that might take an unreasonable amount of time on classical computers.

Challenges and opportunities

Mathematically and theoretically, no established quantitative improvement quantum models can offer classical datasets and the fundamental limitations quantum information theory has for QML. Quantum data sets have also yet to go through the standardization process, which also seriously impacts ML algorithms' empirical evaluation.

We are actively pursuing research on quantum machine area on various software packages, including IBM Qiskit, Google CIRQ, and Regetti, primarily focusing on below:

  1. Quantum SVM 
  2. Quantum Neural Networks and barren plateaus 

In the current scenario, the computing power of existing computers is still limited. There is an increase rise in data volume and requirements for data accuracy and identification have not been sufficient to meet existing supplies fully. The emergence of quantum computing and quantum algorithms have become an effective means to break the bottlenecks.

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