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Quantum Computing Interview Questions
Quantum Computing is the process of using quantum-mechanics for solving complex and massive operations quickly and efficiently. As classical computers are used for performing classical computations, similarly, a Quantum computer is used for performing Quantum computations. Quantum Computations are too complex to solve that it becomes almost impossible to solve them with classical computers. The word 'Quantum' is derived from the concept of Quantum Mechanics in Physics that describes the physical properties of the nature of electrons and photons. Quantum is the fundamental framework for deeply describing and understanding nature.
 
Quantum-mechanics is based on the phenomena of superposition and entanglement, which are used to perform the quantum computations.Quantum-mechanics is based on the phenomena of superposition and entanglement, which are used to perform the quantum computations.
The differences between classical computing and quantum computing are described in the below table :

Classical Computing Quantum Computing
Classical Computers are used for classical computing. Quantum Computers make use of the quantum computing approach.
Data is stored in bits. Data is stored in Qubits.
It performs calculations in the form of binary digits. It performs calculations on the basis of the object's probability.
It can only process a limited amount of data. It can process exponentially more data.
Logical operations are carried out using the physical state, i.e., usually binary. Logical operations are performed using the quantum state, i.e., qubits.
Fails to solve too complex and massive problems. Quantum Computers deals with complex and massive problems.
It has standardized programming languages such as Java, C, C++. It does not rely on any specific programming language.
Classical systems are used for daily purposes. These systems cannot be used for daily purposes as it is complex in nature, and scientists or engineers can use it.
It is built with CPU and other processors. It has a simple architecture and runs on the set of qubits.
It provides security to data but limited. It provides highly secured data and data encryption.
Low speed and more time taking systems. Improved speed and saves much time.
The future of Quantum Computing seems quite enhanced and productive for world trade. The above-discussed points tell that it is the beginning of the concept and will surely become a part of our life. It is not the mainstream yet. In the future, the quantum systems will enable the industries to tackle those problems, which they always thought impossible to solve. According to reports, the market of quantum computing will grow strongly in the coming decades.

Google is showing a great focus and interest in the theory of quantum computing. Recently, Google has launched a new version of TensorFlow, which is TensorFlow Quantum (TFQ). TFQ is an open-source library. It is used to prototype quantum machine learning models. When it will be developed, it will enable developers to easily create hybrid AI algorithms that will allow the integration of techniques of a quantum computer and a classical computer.

The main motive of TFQ is to bring quantum computing and machine learning techniques together to evenly build and control natural as well as artificial quantum computers. Scientists are still facing some new and known challenges with quantum computing, but it will surely lead to software development in the coming years.
There are the following applications of Quantum Computing :

Cybersecurity : Personal information is stored in computers in the current era of digitization. So, we need a very strong system of cybersecurity to protect data from stealing. Classical computers are good enough for cybersecurity, but the vulnerable threats and attacks weaken it. Scientists are working with quantum computers in this field. It is also found that it is possible to develop several techniques to deal with such cybersecurity threats via machine learning.

Cryptography is also a field of security where quantum computers are helping to develop encryption methods to deliver the packets onto the network safely. Such creation of encryption methods is known as Quantum Cryptography.

Weather Forecasting : Sometimes, the process of analyzing becomes too long to forecast the weather using classical computers. On the other hand, Quantum Computers have enhanced power to analyze, recognize the patterns, and forecast the weather in a short period and with better accuracy. Even quantum systems are able to predict more detailed climate models with perfect timings.

AI and Machine Learning : AI has become an emerging area of digitization. Many tools, apps, and features have been developed via AI and ML. As the days passing by, numerous applications are being developed. As a result, it has challenged the classical systems to match up accuracy and speed. But, Quantum computers can help to process such complex problems in less time for which a classical computer will take hundreds of years to solve those problems.

Drug Design and Development : Drug designing and development is a typical job to be done. It is because the development of drugs is based on trial and error method, which is expensive as well as risky tasks. It is also a challenging task for quantum computers too. It is the researcher's hope and belief that quantum computing can become an effective way of knowing the drugs and their reactions over human beings. The day when quantum computing will successfully become capable of drug development, it will save a lot of time and money for drug industries. Also, more drug discoveries could be made with better results for the pharmaceutical industries.

Finance Marketing : A finance industry can survive in the market only if it provides fruitful results to its customers. Such industries need unique and effective strategies to get growth. Although in conventional computers, the technique of Monte Carlo simulations is being used, in turn, it consumes a lot of time on the computer. However, if such complex calculations are performed by a quantum system, it will improve the quality of solutions and decrease development time.

Computational Chemistry : The superposition and entanglement properties of a quantum computer may provide superpowers to machines for successfully mapping the molecules. As a result, it opens several opportunities in the field of pharmaceuticals research. More massive problems that a quantum computer can handle include creating room-temperature superconductor, creating ammonia-based fertilizer, creating solid-state batteries, and removing CO2 (carbon dioxide) for a better climate, etc. Quantum computing will be the most prominent in the field of computational chemistry.

Logistics Optimization : Conventional Computing is being used for improving data analysis and robust modeling by enabling various industries to optimize their logistics and scheduling workflows associated with their supply-chain management. Such operating models continuously perform the calculations and recalculations for finding the optimal routes of fleet operations, air traffic control, and traffic management. Some of these operations can become complex and difficult for classical computers to solve. Thus, quantum computing can become an ideal computing solution to solve such complex problems. In quantum computing, two approaches are used, which are :

* Quantum Annealing : It is an advanced optimization technique that can surpass the classical computers.

* Universal Quantum Computers : It is capable of finding solutions for all types of computational problems. But, such a type of quantum system will take time to be commercially available. Researchers are hopefully working to enhance the system,
A host of new computer technologies has emerged within the last few years, and quantum computing is arguably the technology requiring the greatest paradigm shift on the part of developers. Quantum computers were proposed in the 1980s by Richard Feynman and Yuri Manin. The intuition behind quantum computing stemmed from what was often seen as one of the greatest embarrassments of physics: remarkable scientific progress faced with an inability to model even simple systems.

Quantum mechanics was developed between 1900 and 1925 and it remains the cornerstone on which chemistry, condensed matter physics, and technologies ranging from computer chips to LED lighting ultimately rests. Yet despite these successes, even some of the simplest systems seemed to be beyond the human ability to model with quantum mechanics. This is because simulating systems of even a few dozen interacting particles requires more computing power than any conventional computer can provide over thousands of years!
A quantum bit (qubit) is the smallest unit of quantum information, which is the quantum analog of the regular computer bit, used in the field of quantum computing. A quantum bit can exist in superposition, which means that it can exist in multiple states at once. Compared to a regular bit, which can exist in one of two states, 1 or 0, the quantum bit can exist as a 1, 0 or 1 and 0 at the same time. This allows for very fast computing and the ability to do multitudes of calculations at once, theoretically.
Q# is Microsoft’s open-source programming language for developing and running quantum algorithms. It’s part of the Quantum Development Kit (QDK), an SDK which offers a set of tools that will assist you in the quantum software development process.

The Quantum Development Kit provides :
 
* Python packages to submit Qiskit and Cirq applications to the Azure Quantum service
* The Q# programming language and libraries
* The IQ# kernel for running Q# on Jupyter Notebooks
* Azure CLI extension to manage the Azure Quantum service and submit Q# applications
* APIs for using Python and .NET languages (C#, F#, and VB.NET) with Q#
* Extensions for Visual Studio Code and Visual Studio

With the Quantum Development Kit, you can build programs that run on quantum hardware or formulate problems that run on quantum-inspired solvers in Azure Quantum, an open cloud ecosystem with a diverse set of quantum solutions and technologies. The QDK offers support for Q#, Qiskit, and Cirq for quantum computing, so if you are already working in other development languages you can also run your circuits on Azure Quantum.
Q# is a standalone language offering a high level of abstraction. There is no notion of a quantum state or a circuit; instead, Q# implements programs in terms of statements and expressions, much like classical programming languages. Distinct quantum capabilities (such as support for functors and control-flow constructs) facilitate expressing, for example, phase estimation and quantum chemistry algorithms.
 
For example, the following Q# program constructs a Hamiltonian describing the Hydrogen molecule, and obtains estimates of its energy levels by simulating the quantum phase estimation algorithm.
namespace Microsoft.Quantum.Chemistry.Samples.Hydrogen {
    open Microsoft.Quantum.Intrinsic;
    open Microsoft.Quantum.Canon;
    open Microsoft.Quantum.Chemistry.JordanWigner;
    open Microsoft.Quantum.Simulation;
    open Microsoft.Quantum.Characterization;
    open Microsoft.Quantum.Convert;
    open Microsoft.Quantum.Math;

    operation GetEnergyByTrotterization (qSharpData : JordanWignerEncodingData, nBitsPrecision : Int, trotterStepSize : Double, trotterOrder : Int) : (Double, Double) {

        // The data describing the Hamiltonian for all these steps is contained in
        // `qSharpData`
        let (nSpinOrbitals, fermionTermData, statePrepData, energyOffset) = qSharpData!;

        // Using a Product formula, also known as `Trotterization` to
        // simulate the Hamiltonian.
        let (nQubits, (rescaleFactor, oracle)) = TrotterStepOracle(qSharpData, trotterStepSize, trotterOrder);

        // The operation that creates the trial state is defined below.
        // By default, greedy filling of spin-orbitals is used.
        let statePrep = PrepareTrialState(statePrepData, _);

        // Using the Robust Phase Estimation algorithm of Kimmel, Low and Yoder.
        let phaseEstAlgorithm = RobustPhaseEstimation(nBitsPrecision, _, _);

        // This runs the quantum algorithm and returns a phase estimate.
        let estPhase = EstimateEnergy(nQubits, statePrep, oracle, phaseEstAlgorithm);

        // Obtaining the energy estimate by rescaling the phase estimate with the trotterStepSize. We also add the constant energy offset
        // to the estimated energy.
        let estEnergy = estPhase * rescaleFactor + energyOffset;

        // This returns both the estimated phase, and the estimated energy.
        return (estPhase, estEnergy);
    }
}
Q# is hardware agnostic, meaning that it provides the means to express and leverage powerful quantum computing concepts independently of how hardware evolves in the future. To be useable across a wide range of applications, Q# allows you to build reusable components and layers of abstractions. To achieve performance with growing quantum hardware size, the Q# quantum programming language ensures the scalability of both applications and development effort. Even though the full complexity of such computations requires further hardware development, Q# programs can be targeted to run on various quantum hardware backends in Azure Quantum.
The Quantum Development Kit is a full-featured development kit for Q# that you can use with common tools and languages to develop quantum applications that you can run in various environments. A Q# program can compile into a standalone application, or be called by a host program that is written either in Python or a .NET language.
 
When you compile and run the program, it creates an instance of the quantum simulator and passes the Q# code to it. The simulator uses the Q# code to create qubits (simulations of quantum particles) and apply transformations to modify their state. The results of the quantum operations in the simulator are then returned to the program. Isolating the Q# code in the simulator ensures that the algorithms follow the laws of quantum physics and can run correctly on quantum computers.
 
The following diagram shows the stages through which a quantum program goes from idea to complete implementation on Azure Quantum, and the tools offered by the QDK for each stage.

Quantum Development Kit
Source : docs.microsoft.com