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 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

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 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

**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,

**CO2 (carbon dioxide)**

for a better climate, etc. Quantum computing will be the most prominent in the field of computational chemistry.
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!

Quantum mechanics was developed between

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 **Q#**

programming language and libraries**IQ#**

kernel for running **Q#**

on Jupyter Notebooks**CLI**

extension to manage the Azure Quantum service and submit **Q#**

applications**.NET**

languages (**C#, F#, and VB.NET**

) with **Q#**

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 **Azure Quantum**.

**Q#**

, **Qiskit**

, and **Cirq**

for quantum computing, so if you are already working in other development languages you can also run your circuits on **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.**Q#**

program constructs a Hamiltonian describing the ```
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 **Python** or a **.NET** language.

**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 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

**Source : docs.microsoft.com**

**QDK**

for each stage.Learn to write a basic quantum program in

** Q#**

that leverages the nature of quantum mechanics to produce a random number.In this tutorial, you'll learn how to

** Q#**

project.**Q#**

.** Q#**

programs are structured.**Q#**

standalone (Set up a Q# standalone environment)**Q# **

and Python (Set up a Q# and Python environment)**Q#**

and **C#**

(Set up a Q# and .NET environment)The first thing you need to do is to create a new

**Q#(Q Sharp)**

project. This tutorial uses the environment based on **Q#**

applications with VS Code, but you can use your preferred IDE.To create a new project in Visual Studio Code :

This generates two files : the project file,

**Qrng.csproj**

, and a **Q#**

application template, **Program.qs**

, that you will use to write your application.Now, replace the contents of the

**Program.qs**

file with the following code :```
namespace Qrng {
open Microsoft.Quantum.Convert;
open Microsoft.Quantum.Math;
open Microsoft.Quantum.Measurement;
open Microsoft.Quantum.Canon;
open Microsoft.Quantum.Intrinsic;
operation SampleQuantumRandomNumberGenerator() : Result {
// Allocate a qubit
use q = Qubit();
// Put the qubit to superposition
// It now has a 50% chance of being measured 0 or 1
H(q);
// Measure the qubit value
return M(q);
}
}
```

Now take a look at new code.

**open**

the necessary namespaces from the **SampleQuantumRandomNumberGenerator**

operation, which takes no input and produces a value of type **Result**

. The Result type represents the result of a measurement and can have two possible values: **Zero**

or **One**

.**qubit**

with the **use**

keyword.**H (Hadamard)**

operation to place the qubit in an equal **superposition**

.**M**

operation to measure the qubit and return the measured value (Linear algebra is the language of quantum computing. Although you don’t need to know it to implement or write quantum programs, it is widely used to describe qubit states, quantum operations, and to predict what a quantum computer does in response to a sequence of instructions.

Just like being familiar with the basic concepts of quantum physics can help you understand quantum computing, knowing some basic linear algebra can help you understand how quantum algorithms work. At the least, you’ll want to be familiar with vectors and matrix multiplication.

To build multi-qubit states out of single-qubit states and discusses the gate operations needed to include in a gate set to form a universal many-qubit quantum computer. These tools are absolutely necessary to understand the gate sets that are commonly used in

code, and also to gain intuition about why quantum effects such as entanglement or interference render quantum computing more powerful than classical computing.**Q#**

The true power of quantum computing only becomes evident as we increase the number of qubits. Single-qubit gates possess some counter-intuitive features, such as the ability to be in more than one state at a given time. However, if all we had in a quantum computer were single-qubit gates, then a calculator and certainly a classical supercomputer would dwarf its computational power.

Quantum computing power arises, in part, because the dimension of the vector space of quantum state vectors grows exponentially with the number of qubits. This means that while a single qubit can be trivially modeled, simulating a fifty-qubit quantum computation would arguably push the limits of existing supercomputers. Increasing the size of the computation by only one additional qubit doubles the memory required to store the state and roughly doubles the computational time. This rapid doubling of computational power is why a quantum computer with a relatively small number of qubits can far surpass the most powerful supercomputers of today, tomorrow, and beyond for some computational tasks.

As you work with

, **Q#***Pauli measurements* are a common kind of measurement, which generalize computational basis measurements to include measurements in other bases and of parity between different qubits. In such cases, it is common to discuss measuring a Pauli operator, in general an operator such as

or **X,Y,Z **

, and so forth.**Z⊗Z,X⊗X,X⊗Y**

Discussing measurement in terms of Pauli operators is especially common in the subfield of quantum error correction.

guide follows a similar convention; this article explains this alternative view of measurements.**Q#**

Some quantum algorithms are easier to understand in a circuit diagram than in the equivalent written matrix representation once you understand the visual conventions.

With Azure Quantum, you can use the

Python package to submit quantum circuits with Qiskit, Cirq, and also provider-specific formatted circuits.**azure-quantum**

**Quantum circuit diagram conventions : **In a circuit diagram, each solid line depicts a qubit, or more generally, a qubit register. By convention, the top line is qubit register and the remainder are labeled sequentially.

Operations are represented by *quantum gates*. The term quantum gate is analogous to classical logic gates. Gates acting on one or more qubit registers are denoted as a box. For example, the symbol

is a Hadamard operation acting on a single-qubit register.

In a quantum circuit, time flows from left to right. Quantum gates are ordered in chronological order with the left-most gate as the gate first applied to the qubits. In other words, if you picture the wires as holding the quantum state, the wires bring the quantum state through each of the gates in the diagram from left to right. That is to say

is the unitary matrix .

The previous examples have had precisely the same number of wires (qubits) input to a quantum gate as the number of wires out from the quantum gate. It may at first seem reasonable that quantum circuits could have more, or fewer, outputs than inputs in general. This is impossible, however, because all quantum operations, save measurement, are unitary and hence reversible. If they did not have the same number of outputs as inputs they would not be reversible and hence not unitary, which is a contradiction. For this reason any box drawn in a circuit diagram must have precisely the same number of wires entering it as exiting it.

Multi-qubit circuit diagrams follow similar conventions to single-qubit ones. As a clarifying example, a two-qubit unitary operation can be defined to be , so the equivalent quantum circuit is:

You can also view as having an action on a single two-qubit register rather than two one-qubit registers depending on the context in which the circuit is used.

Perhaps the most useful property of such abstract circuit diagrams is that they allow complicated quantum algorithms to be described at a high level without having to compile them down to fundamental gates. This means that you can get an intuition about the data flow for a large quantum algorithm without needing to understand all the details of how each of the subroutines within the algorithm work.

The action of a quantum singly controlled gate, denoted Λ(G)">, where a single qubit's value controls the application of G">, can be understood by looking at the following example of a product state input:

That is to say, the controlled gate applies G"> to the register containing ψ"> if and only if the control qubit takes the value 1">. In general, such controlled operations are described in circuit diagrams as

Here the black circle denotes the quantum bit on which the gate is controlled and a vertical wire denotes the unitary that is applied when the control qubit takes the value . For the special cases where

and **G=X**

, the following notation is used to describe the controlled version of the gates (note that the c**G=Z**

is the CNOT gate):**ontrolled-X gate**

Quantum systems that are isolated from their environments such that no other system interacts with the qubits are called closed quantum systems. By contrast, a device that is subject to some amount of interaction, or noise, from its environment is an open quantum system. In general, these interactions between the system and the environment significantly change the dynamics of the system and result in quantum dissipation, such that the information contained in the system is lost to its environment.

The Quantum Development Kit provides a noise simulator for simulation of open quantum systems. This feature allows for simulating the behavior of Q# programs under the influence of noise, and also for using the stabilizer representation (also known as CHP simulation) of quantum algorithms, that is, algorithms consisting solely of CNOT, Hadamard, and phase gates.

There are two credit programs for Azure Quantum :

The default credit grant is **USD 500 equivalent per provider**, expressed in hardware credits. For more information on how to use these, see the Azure Quantum credits section in Create an Azure Quantum workspace.

Microsoft also offers up to U**SD 10,000** in credits for use on quantum hardware. To receive this credit, you can apply for the Azure Quantum Credits program.

You can find the terms of use for the Azure Quantum Credits program at this link: aka.ms/aq/credits/terms.

Note that to use Azure Quantum Credits you must also accept the relevant usage terms for Microsoft Azure, as well as the terms from the providers you would like access to.

Azure Quantum offers various quantum solutions, such as different quantum hardware devices and quantum simulators that you can use to run your quantum computing programs. This topic has the latest capabilities available through Azure Quantum.

A Quantum Processing Unit (QPU) is a physical or simulated processor that contains a number of interconnected qubits that can be manipulated to compute quantum algorithms. It's the central component of a quantum computer or quantum simulator.

Quantum devices are still an emerging technology, and not all of them can run all**QPUs**:

** Q#**

code. As such, you need to keep some restrictions in mind when developing programs for different targets. Currently, Azure Quantum and the **QDK**

manage three different profiles for **Q#**

program within the limits of memory for simulators or the number of qubits for physical quantum computers.**Q#**

program that doesn't require the use of the results from qubit measurements to control the program flow. Within a **Q#**

program targeted for this kind of **QPU**

, values of type Result don't support equality comparison.**Q#**

program targeted for this kind of **QPU**

, you can only compare values of type Result as part of conditions within if statements in operations. The corresponding conditional blocks might not contain return or set statements.Quantum computers are more elegant than supercomputers, as they are smaller and use less energy. Multidimensional quantum algorithms are run on them using qubits (CUE-bits).

The Quantum Hardware system is quite large and mostly comprises cooling systems to keep the superconducting processor at its ultra-cold operational temperature.

Scientists and engineers use supercomputers to solve challenging issues. These are extremely powerful traditional computers with thousands of CPU and GPU cores. Even supercomputers, however, have difficulty solving some problems. If a supercomputer becomes stumped, it's most likely because it was asked to handle a problem with a high level of complexity. However, complexity is frequently the cause of failure with traditional computers.

And here comes Quantum Computers, which are designed to handle more complex problems much easier and faster than any other classic computer or supercomputer.