### Tuning artificial intelligence to enrich quantum insights

**OVERVIEW**

Quantum Computation is the “scientific field that reviews how the quantum conducts of certain subatomic particles (for example photons, electrons, and so forth) can be utilized to perform calculation and in the long run huge scope data processing”. Quantum Computing is a computation device that utilizes quantum mechanical wonders, for example, superposition and entanglement, to perform procedures on information. The quantum processing field was first presented by Yuri Manin in 1980 and Richard Feynman in 1982. This article provides a very brief introduction about traditional and quantum computers, overview of quantum systems, quantum computation components and algorithms, quantum computing for artificial intelligence and artificial intelligence for quantum computing.

**INTRODUCTION**

Quantum Computing is a beautiful combination of “quantum physics, computer science, and information theory”. Quantum Computers (QCs) function in a different manner from regular computers. It should be able to solve specific types of problems in seconds that would take normal computers thousands of years. QCs are currently in the domain of academic research. Google and IBM also have active research programs.

**1.1 Traditional Computers Vs Quantum Computers**

Traditional Computers follow the binary system. Here, data is represented by binary digits (bits), which can be either 1 or 0. And every element within the computer must be in a state of “1” or “0” at all times. The computer executes instructions by transitioning between different combinations of “1” and “0”, but only one combination can be active at a time.

A Quantum Computer is any gadget that utilizes quantum mechanical marvels to perform calculations and control information. Quantum Computers is a superposition of quantum bits. A quantum bit (Qubit) can be both 1 and 0 at the same time. This state is called superposition.

**1.2 Overview of Quantum Systems**

A quantum system is a “portion of the whole Universe (environment or physical world) which is taken under consideration to make analysis or to study for quantum mechanics pertaining to the wave-particle duality in that system”.

Quantum systems are described by a wave function and it is denoted by a symbol ‘ψ’. For a given potential (V(x)), we discover all answers for Schrödinger's condition. These arrangements structure a premise of a vector space called a Hilbert space.

**QUANTUM COMPUTATION COMPONENTS AND ALGORITHMS**

**2.1 Quantum Computation Components**

A traditional, as well as a quantum computer, essentially consists of 3 parts:

- Memory - which holds the current machine state,
- Processor - which performs elementary operations on the machine state, and
- Input/output - which allows setting the initial state and extracting the final state of the computation.

“Quantum gates” are the fundamental calculation parts for QC. They are altogether different from gates in classical computation systems.

**2.2 Quantum Computation Algorithms**

In this section, we broadly classify quantum algorithms according to their area of application. And also we will discuss quantum algorithms for graph theory, number theory, and machine learning and so on. Quantum algorithms utilize a “few explicit highlights of the quantum world for instance quantum superposition to get from classical inputs through entangled states to classical outputs more effectively than classical algorithms”.

Quantum algorithms use a combination of algorithmic paradigms specific to quantum computing. These paradigms are the “Quantum Fourier Transform (QFT), the Grover Operator (GO), the Harrow Hassidim-Lloyd (HHL) method for linear systems, variational quantum eigen value solver (VQE), and direct Hamiltonian simulation (SIM)”. The complete list of algorithms in this article, classified according to their application areas, can be found in Table 1.

**Table 1: Overview of Quantum Algorithms**

**ARTIFICIAL INTELLIGENCE KEY ASPECTS**

The key parts of artificial Intelligence solutions are as per the following [1] – [7]:

- Automation
- Natural Language Understanding (NLU) and Natural Language Processing (NLP)
- Machine Learning
**Automation:**

Artificial Intelligence (AI) is in some cases mistook for computerization (Automation), and the terms are regularly utilized reciprocally. At the point when robotic process automation is joined with components of AI, for example, AI, the outcome is known as intelligent process automation (IPA). An IPA device is amazing in light of the fact that it permits us to receive both the rewards of robotization — sped up, productivity, time-reserve funds, and capacity to scale — with the experiences, adaptability, and preparing intensity of AI.

**NLU and NLP:**

The two ideas manage the connection between natural language (as in, what we as people talk, not what PCs comprehend) and artificial intelligence. They share a shared objective of comprehending ideas spoke to in unstructured information, similar to language, rather than organized information like insights, activities, and so forth. Keeping that in mind, NLP and NLU are contrary energies of a great deal of other data mining methods.

**Machine Learning:**

Machine Learning [8] – [9] is the state of art technology where it can analyze and model the data. Machine Learning algorithms are examined in two ways: Learning and Inference. The prime aim of the learning step is to describe the data which is called as feature vector and aggregate it in a model. The learning algorithm selects a model and actively searches for the model’s parameters. The Learning stage is more time consuming and the inference step uses the model created by the learning step to mold it and project an intelligent model.

**QUANTUM COMPUTING FOR ARTIFICIAL INTELLIGENCE**

The utilization of “quantum algorithms in artificial intelligence techniques will support machines' learning capacities”. This will prompt upgrades in the turn of events, among others, of predication frameworks, including those of the financial industry.

“Quantum machine learning can be more efficient than classic machine learning, at least for certain models that are intrinsically hard to learn using conventional computers.” However, “We still have to find out to what extent these models appear in practical applications” – by Samuel Fernández Lorenzo, a quantum algorithm researcher.

Some ways quantum computing could change the future of artificial intelligence:

- Solve complex problem quickly
- Handling of large Datasets
- Building better models
- Integration of multiple datasets
- Combat fraud detection
- More accurate algorithms

The regular expansion that quantum computers offer AI and machine learning isn't lost on business people, who are occupied presently learning approaches to misuse the specialized blend.

**ARTIFICIAL INTELLIGENCE FOR QUANTUM COMPUTING**

Every quantum computer experiences decoherence, but quantum computers that are successful at delaying and minimizing decoherence perform better. That's why, when discussing a quantum computer and its ability to do computation, we need to discuss how well it does at preventing decoherence.

To quantify that, the parameters T1 and T2 are particularly important:

• T1 helps to quantify how quickly the qubits experience energy loss due to environmental interaction (energy loss would result in a change in frequency, which would make coherent qubits experience decoherence).

• T2 helps to quantify how quickly the qubits experience a phase change due to interaction with the environment, again a cause of decoherence.

Evolutionary algorithms could be useful to “compile” quantum circuits in order to generate equivalent circuits characterized by better values of T1 and T2. Pattern recognition techniques could be used to identify critical sequence of quantum gates in circuits and replace them with suitable collections of gates in order to improve T1 and T2.

**HOW MAY QUANTUM COMPUTING AFFECT ARTIFICIAL INTELLIGENCE**

If science were a dating app, quantum physics and machine learning probably wouldn’t be a match. They’re from completely different fields and often require completely different backgrounds and skills. But, throw in a little quantum computing and, suddenly, that science-matchmaking app becomes Tinder and the attraction between the two is palpable.

“Quantum machine learning can be more efficient than classic machine learning, at least for certain models that are intrinsically hard to learn using conventional computers,” says Samuel Fernández Lorenzo, a quantum algorithm researcher who collaborates with BBVA’s New Digital Businesses area. “We still have to find out to what extent do these models appear in practical applications.”

Here are four ways quantum computers could change the future of AI. Forever.

**Handling HUGE Amounts of Data**

Machine learning and AI eat data. Quantum computers are designed to manage huge amounts of data. With each iteration of quantum computer design and improvements to quantum error-correction code, programmers are able to better master the potential of qubits — to manage exponentially more data, according to Lorenzo.

**Building Better Models**

Several industries, such as pharmaceutical, life sciences and finance, are nearly at the end of their classical computing rope. These industries require complex models that classical computers just can’t generate. Quantum computers, on the other hand, have the potential processing power to model the most complex situations. If quantum technology can create better models, it may lead to better treatments for disease, decreased risk of financial implosions, and improved logistics.

**More Accurate Algorithms**

According to Lorenzo, supervised learning is used for most industrial applications of artificial intelligence, such as image recognition or consumption forecasting. Quantum Machine Learning (QML) researchers are trying to find ways to develop better quantum computer algorithms.

**Using Multiple Datasets**

The problem often isn’t that there is not enough data, or that there’s too much data, the problem is that the data is placed in a variety of datasets, according to futurist and strategic adviser Bernard Marr. He writes that quantum computers could handle the integration of different datasets for much quicker and easier analysis.

**CONCLUSION**

Quantum computing is promising field. The reason for quantum computing is to help and broaden the capacities of customary processing. Quantum computers are intended to perform undertakings considerably more precisely and proficiently than ordinary PCs, giving engineers another instrument for explicit applications. Despite the fact that quantum figuring has significant hindrances their latent capacity have numerous applications that exceed the expenses. A portion of the applications incorporate cryptanalysis, PC models of climate frameworks or of complex concoction responses and issues which include an incredibly large number of factors.

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