What is Machine Learning?
Machine learning (ML) is a branch of Artificial Intelligence(AI) that leverages data and algorithms to imitate the human way of learning, gradually improving its accuracy with time and new data. The technology is helping improve business processes by automating decision-making. Machine learning platforms facilitate data collection and analysis, identify patterns and learn from them to aid in the decision-making.
Building a machine learning model requires huge computational capacity and deploying them is a tricky aspect. The limits of computer hardware on which algorithms are run have always defined the limits of what machines can learn.
What is Quantum Computing?
Quantum computing uses the properties of quantum mechanics to store data and perform calculation more efficiently than classical computers. The classical computers such as desktops, laptops, and smartphones encode information in binary “bits”, while in a quantum computer the basic unit of information is qubit or quantum bit. The classical bits are binary and can hold only positions of 0’s or 1’s unlike qubits that can hold a superposition of all possible states, which is the ability to represent multiple possible combinations of 1 and 0 at the same time.
Quantum computing has advanced in both theory and practical applications. One of the application areas is how quantum computing can advance machine learning. Quantum machine learning is an extension of the pool of hardware for machine learning models with the use of new computing devices called quantum computers.
What are the advantages of quantum machine learning?
- Improve training models
The efficiency and success of the machine learning model depends on the dataset it is given. The size and quality of the dataset determines the output, and if the information is not adequate, then the outcome won’t be promising either.
The ability of quantum computing to go beyond the traditional binary coding system through qubit, makes it possible to enrich the dataset in terms of volume and diversity. With better quality of dataset, it becomes possible to improve the training models, which enhances machine learning ability to solve real-world problems.
- Reduce training time
Machine learning (ML) models need to be trained, and in addition to quality dataset, this requires time, with some models being trained for months. The inherent advantage of quantum machines over classical machines helps to reduce the training and at the same time improve the accuracy of the ML models.
Researchers have demonstrated that quantum-enabled ML models have performed faster with greater accuracy compared to classical computers for certain classes of supervised ML models. The quantum-enabled ML models are also able to generate more valuable insights.
- Filling in the gaps in the data needed to train ML models
The generative capabilities of quantum computers help to fill in the gaps in the data needed to train ML models. The quality and variety of the data have been a limiting factor when it comes to training ML algorithms. The ability of ML to predict outcomes for rare or black swan events such as pandemic or 2008 financial crisis is limited due to non-availability of a robust dataset. Quantum-enabled generative AI models enable the creation of “synthetic” datasets resembling the training data that is already available and mitigates the limitations of scarce datasets.
The approach can be leveraged to improve ML models in a variety of real-world cases from detecting rare diseases to modelling risk scenarios in different industries, and others.
What are the challenges of quantum machine learning?
In quantum computers the information is encoded as qubits equivalent to binary coding in classical computers. However, the information in qubits gets degraded very easily when the qubits are exposed to noises, and the process is known as decoherence. The slightest vibration or change in temperature- disturbances referred to as noises in quantum terminology, can cause the decay. The noises cause a lot of errors to creep into the calculations that impact the performance of machine learning models. The qubits are protected from the outside world in super cool fridges. Decoherence is one of the reasons for quantum machine learning to be still at the early stages of adoption.
Machine learning models made on quantum computers are more powerful and faster compared to the models that are run on classical computers. Quantum machine learning helps to solve problems with complex correlations between inputs that are challenging to solve with the traditional classic computers.
Even as there are challenges to be overcome for broad commercial adoption of quantum machine learning, it will improve Artificial Intelligence (AI) Machine learning(ML) by leaps and bounds. As the advantages outweigh the risks, quantum machine learning adoption will accelerate in the near future.
Even as you plan to incorporate quantum computing in your AI strategy, you can leverage E2E Cloud services for implementing artificial intelligence models. It is one of the best platforms to implement artificial intelligence. You can contact us for more details.