The current and future state of quantum algorithms

Founder, Quantica Computacao. TEDx speaker and author of “Quantum Computing Solutions”.

Using quantum algorithms can reduce infrastructure costs, as they allow you to solve problems very quickly compared to current techniques. Quantum AI has algorithms similar to AI and will have a prefix of “quantum” attached to the same name as techniques from AI. For example, while you have support vector machines (SVMs) in AI, quantum AI has quantum SVMs. This is also the case with quantum classification, quantum cryptography, quantum simulated annealing, and quantum k-nearest neighbor (KNN).

In quantum algorithms, quantum methods, languages, and circuits are modeled and implemented. A quantum computer has a quantum processing unit and a quantum network controller and is based on the quantum internet. You can also use quantum algorithms on a classical computer.

The use cases for quantum algorithms are varied. They can be used for scheduling, resource task management, process optimization, predictive analytics, and improving security planning. Quantum Approximate Optimization Algorithm (QAOA), quantum classifiers, quantum generated adversarial networks, quantum neural networks, and quantum simulated annealing are additional popular techniques based on quantum algorithms. There are also quantum walks equivalent to the Markov chain and Markov walks. Quantum Monte Carlo methods can be used for portfolio management and technical analysis in financial companies. If you step back from the quantum world and start with a classical random walk, you can walk left and right with a 50-50 probability. Similarly, you can have analog quant walks, which are used to simulate a wealth management portfolio over a three to five year period.

Quantum perceptron is like a neural network based perceptron. If you look at a classic perceptron, you can process N dimensions of data. With the quantum perceptron, you can process two to four dimensions. Post-quantum cryptographic algorithms are also popular in use. We always see one side of the screen, which focuses on building the best quantum computer. On the other side of the screen are criminals and hackers looking for a lot of computing power for a short period of time – an hour or less. They just want a higher-powered computer—like a computer with higher processing speed and higher, faster capabilities—to open the doors of a system. Hackers try to break existing cryptographic algorithms using a powerful computing resource, and therein lies the danger. We need to make sure that all of our algorithms in banking, financial services and wherever you have a cryptocurrency algorithm are post-quantum resistant.

There are various techniques to do this, such as quantum annealing, hidden quantum Markov model, and Grover’s quantum algorithm (related to search). For example, Mklas encryption, Merkle hash, three signatures, Merkle-Hellman knapsack encryption and Bushman Williams peer encryption is the new quantum cryptographic algorithms. Most cryptographers have tried to evaluate them, and these are the cryptographic algorithms that need to be changed. This research will help make cryptographic algorithms more quantum-resistant.

Looking ahead, we have a lot to come. New fields such as generative quantum machines and quantum machine learning are evolving. In quantum machine learning, hybrid quantum algorithms based on quantum Boltzmann machine, quantum random access memory and QRAM concepts are used was investigated. The new algorithms can be used for image processing, object tracking and other use cases for optical character recognition (OCR), intelligent character recognition (ICR) and so on. In the quantum world, quantum NLP, quantum computer vision and quantum RAM are evolving.

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