Bits processing in Quantum Computing

By: 1Sahil Garg, 1Shaurya Katna

1Department of CSE, Chandigarh College of Engineering and Technology, Chandigarh, India.


Since quantum computing allows us to compute at previously unheard-of speeds, it represents a new and more intriguing paradigm. These have challenged existing cloud security protocols as well. Thus, this article aims at discussing how the quantum bits (qubits) in quantum computing work under the hood. Understanding this in detail will yield better research in this field and hence, the aim of the article. Discussing from what the qubits are to how they are physically made and then expanding upon the gates and circuit formation with the help of qubits and then finally ending with the challenges being faced and the applications in the modern world.


The world is progressing more and more towards sustainable development[1-6] and the smart city concept[7][8], at the core of it is computers. The digital world presents solutions to many problems such as reducing paper, easy access to information, and collaboration on information. But with the advent of computers, the need for increased performance of computers is necessary. For this, more and more research is being presented. Two things that have brought immense speed to computing are parallel computing and quantum computing. This article discusses quantum computing in detail and parallel computing is shown in research [9-12].

As the science of quantum computing develops, it becomes more and more crucial to comprehend the basic components of these machines. Gaining a deeper understanding of quantum computing through practical application provides a more concise overview of the field, its applications, and its prospects.

Understanding the meaning of classical bits is crucial before delving into quantum bits. The classical bit, a binary representation of a state that can be either 0 or 1, is the fundamental unit of information in classical computing. The fundamental building blocks of traditional digital information processing and storage are these bits. Physical systems that can exist in two distinct states, such as electrical voltages, magnetic orientations, or optical polarisations, are used to construct classical bits.

The classical bit is the cornerstone of classical digital logic, where information is processed using classical logic gates. Classical logic gates perform operations on one or more bits, producing an output based on predefined rules. These operations include basic functions such as AND, OR, NOT, and XOR, which can be combined to perform complex computations. Classical computers, which are prevalent in everyday technology, use vast arrays of classical bits and logical operations to execute algorithms and process data.

Classical bits, being binary, make it easier to represent and process information using Boolean algebra. Data in classical computing is usually encoded as sequences of 0s and 1s in binary code, stored and transmitted through memory units, processors, and communication channels following classical bit principles.

Certain problems are exceptionally well-suited for classical computing, particularly if they can be broken down into a set of logical operations that are well-defined. However, the inability of classical computers to perform complicated issues like factoring big numbers or simulating quantum systems led to the investigation of other computing paradigms, such as quantum computing.

To gain knowledge of bit processing, it is essential to have a grasp of quantum bits. A qubit, known as a quantum bit as well, serves as the basic unit of quantum information in quantum computing. Qubits may express both 0 and 1 simultaneously by existing in a superposition of states, which sets them apart from classical bits, which can only exist in one of two states (0 or 1). A qubit’s state is represented by a quantum state vector, usually represented as |ψ⟩. This vector consists of the linear combination of the basic states |0⟩ and |1⟩. This can be stated mathematically as:


Here, α and β are complex probability amplitudes, and ||2 and ||2 respectively reflect the probabilities of measuring the qubit in the states |0⟩ or |1⟩. The conservation of the total probability is guaranteed by the normalization requirement + =1

Qubits can also undergo a phase shift, represented by a global phase factor, which does not affect the probabilities. The qubit state expressed in the most general representation is:

α∣0⟩+e (iφ)β∣1⟩ = ∣ψ⟩

Here, φ is the phase factor.

Another essential feature of qubits is their entanglement, which enables correlations between them that are not possible with classical bits. One qubit’s state cannot be characterized independently when they are entangled, with the entangled state of a two-qubit system often represented as:


Here, φ is the phase factor.

Entanglement is another crucial characteristic of qubits, allowing correlations between them that would not be conceivable with classical bits. An entangled qubit cannot have its state independently described from the other. The entangled state of a two-qubit system is commonly represented as follows:


​This is an example of a maximally entangled state.

Unitary matrices are used to represent qubits, which are manipulated via quantum gates. As an illustration, a qubit can be put through the Hadamard gate (H) to produce a superposition state:


The CNOT (controlled-NOT) gate is another essential quantum gate that introduces entanglement between qubits:





In these equations, subscripts a and b indicate different qubits.

The physical implementation of qubits:

Quantum bits, or qubits, can be realized using various physical systems, each with its own set of advantages and challenges. Here this article will examine the 3 prominent qubit implementations. Table 1 shows the 3 implementations with examples of each where they are implemented.

Table 1. Implementations of qubits with their examples



Superconducting Qubits[13]

IBM’s superconducting qubits, Google’s Sycamore processor.

Trapped Ions[14]

IonQ, Honeywell

Topological Qubits[15]

Microsoft’s StationQ project

Each of the implementations referred in table 1 is expanded upon in this section.

  1. Superconducting Qubits:
Fig 1. Superconducting Qubits

Using the principles of superconductivity—a quantum mechanical phenomenon that some materials show at very low temperatures—superconducting qubits are a particular kind of qubit. Electrons in superconductors create Cooper pairs, which flow through the material without encountering any resistance. Super Currents can flow endlessly in a closed loop due to the lack of resistance, which creates a special setting for encoding and modifying quantum information.

Quantum information is encoded in the superconducting circuits by exploiting the quantum-mechanical nature of the superconducting wave function. The Josephson junction allows the creation of a phase difference between the superconducting order parameters on either side of the barrier.

The core building block of superconducting qubits is the Josephson junction, a device formed by placing a thin insulating barrier (typically an oxide layer) between two superconducting materials.

The Josephson junction, when integrated into a superconducting loop, creates a two-level quantum system akin to a classical bit, where the phase difference can be in one of two states.

An implementation is shown in figure 1.

  1. Trapped Ions:

In this, we use electromagnetic fields to trap individual ions and exploit their quantum properties for quantum information processing. Trapped ions are held in place using electromagnetic fields, typically generated by applying electric potentials to a set of electrodes. The shape and configuration of these electric fields determine the trapping geometry. The qubit is encoded in the internal quantum states of the trapped ions. Typically, these states involve the electronic energy levels of the ions.

Quantum gates and operations are performed by using laser beams that interact with the trapped ions. The ions absorb and emit photons, leading to changes in their internal states.

Trapped ions are often arranged in linear or 2D arrays to facilitate quantum computing operations. The scalability of trapped-ion systems is a critical consideration for building larger and more powerful quantum computers.

  1. Topological Qubits:

Topological qubits are a type of qubit that relies on the principles of topological quantum computation, which involves using anyons and their braiding statistics to perform quantum operations. The physical basis of topological qubits is rooted in the properties of certain exotic particles called anyons and their behavior in two-dimensional materials with nontrivial topological characteristics.

Anyons are exotic particles with fractional statistics. Unlike fermions (e.g., electrons with half-integer spin) and bosons (e.g., photons with integer spin), anyons have fractional spin and obey non-Abelian statistics.

The unique feature of anyons is their braiding statistics. When anyons are braided around each other in two-dimensional space, the resulting quantum state depends on the braiding path. This noncommutative behavior is the foundation of topological quantum computation.

Quantum information is encoded in the nontrivial braiding of anyons. The state of the system depends on how canyons are braided and manipulated in the two-dimensional space.

Topological qubits are inherently more robust against local errors because the quantum information is encoded in the global properties of the system, providing a form of error correction.

Creating gates and circuits with qubits:

As with traditional circuits and logic gates in classical computers, developing circuits and gates utilizing qubits requires modifying the quantum states of qubits to conduct quantum operations.Nevertheless, quantum circuits can have unique characteristics like quantum parallelism, entanglement, and superposition because of the laws of quantum physics that govern their operation.

Quantum gates are used to qubits in a sequential manner to represent quantum circuits. Every gate symbolizes a quantum operation that modifies the qubits’ quantum states.

Table 2 lists a few varieties of quantum gates along with a description of each.

Table 2. Types of quantum gates



Single-Qubit Gates[16]

These gates adjust a single qubit by carrying out rotations and other computations. The Hadamard gate (H), Pauli-X gate (X), Pauli-Y gate (Y), and Pauli-Z gate (Z) are a few examples of common single-qubit gates.

Controlled Gates

These gates involve two or more qubits and are dependent on the state of one or more control qubits. The prevalent kind of controlled gate is the Controlled-NOT (CNOT) gate.

Quantum Circuits with Entanglement[17]

Entangled states, in which the states of two qubits are reliant upon one another, can be produced by quantum gates. An essential tool for quantum computing techniques is entanglement.

As gates are added to qubits, quantum circuits develop over time. In this circuit structure, quantum algorithms are developed by taking advantage of quantum oracles that are represented by particular gates and by applying the ideas of quantum parallelism. Multiple possibilities can be processed at once thanks to quantum parallelism, which could lead to a speed increase over classical algorithms.

The final step in a quantum circuit is a measurement, which converts the qubits’ superposition states into classical states. The probabilities in the probabilistic measurement results are determined by the squared magnitudes of the coefficients in the superposition state. Quantum circuits may incorporate error correction methods, such as error-detecting and error-correcting coding, to reduce the impact of errors and noise on qubit states. Researchers need to be well-versed in the quantum gates that are accessible on a particular quantum computing platform to take advantage of the unique features of qubits for a variety of quantum computations.

How Quantum Bits process information

In contrast to classical bits, qubits fundamentally alter how data is processed by harnessing quantum mechanics laws, allowing them to concurrently exist in multiple states. Their unique superposition property enables faster calculations and entanglement across distances, facilitating interrelated operations and improving efficiency in quantum systems.

Quantum gates serve as the computational building blocks for qubits, analogous to classical logic gates. However, quantum gates have the unique capability to manipulate qubits in superposition, allowing for parallel processing of multiple inputs. When a qubit is measured, it collapses from its superposition state to a definite state (either 0 or 1) with a certain probability, determining the outcome of the measurement. This measurement process is crucial for extracting the result of a quantum computation.

Furthermore, quantum interference plays a pivotal role in quantum computing. Interference occurs when different paths or states of qubits interfere constructively or destructively, leading to the amplification or cancellation of certain outcomes. Quantum algorithms take advantage of this phenomenon to increase the probability of getting the right response while reducing the impact of wrong answers.In essence, qubits harness the principles of superposition, entanglement, quantum gates, measurement, and interference to process information in a manner that promises to revolutionize computing capabilities, paving the way for the next generation of computational advancements.

Challenges in Quantum Bits Processing:

Quantum computing holds immense promise for revolutionizing various industries by solving complex problems at unprecedented speeds. However, the field faces formidable obstacles in qubit processing. Decoherence, the loss of quantum characteristics due to environmental interactions, threatens the integrity of quantum states, necessitating strategies to maintain coherence for accurate processing. Error correction is essential to mitigate errors arising from noise and hardware imperfections, ensuring the reliability of quantum computations. Engineering difficulties in creating, manipulating, and incorporating qubits into large-scale quantum systems while preserving high connectivity and low error rates impede scaling. Implementing multi-qubit operations and minimizing undesired interactions between qubits remain significant challenges for quantum algorithms. Accurate readout and measurement methods are crucial for discriminating between quantum states without compromising coherence. Establishing dependable qubit initialization and reset protocols is essential for effective quantum computation. Addressing the limitations of various physical platforms for qubit implementation, such as coherence times and scaling issues, is crucial for practical quantum computing. Moreover, ongoing research is focused on developing efficient quantum algorithms that outperform classical counterparts, underscoring the importance of algorithm design and optimization for realizing the full potential of quantum computing.

Quantum Computing Applications:

Due to its remarkable properties, quantum computing holds the potential to fundamentally change a wide range of industries and fields.Regarding encryption and security, quantum computing presents a challenge as well as an opportunity. Based on quantum mechanical principles, quantum cryptography[18] promises total security through secure communication channels, even if it may be able to break popular cryptographic methods like RSA and ECC. Quantum computers are also very good at simulation and optimization jobs, like supply chain management and delivery truck route optimization. Their remarkable accuracy in simulating quantum systems gives researchers vital insights into intricate processes in physics, chemistry, and materials science.

Moreover, quantum computing shows great promise in accelerating drug discovery and materials science research. By precisely simulating molecular interactions, quantum computers facilitate the search for new drugs and the development of novel materials with tailored characteristics. In financial modeling and risk analysis, quantum computers offer significant advantages over classical computers, enabling faster and more accurate calculations for tasks like portfolio optimization and option pricing. This capability has the potential to transform financial markets by enabling quicker decision-making and more precise forecasts.

Furthermore, quantum computing can enhance supply chain management by optimizing production schedules, transportation routes, and inventory management, leading to cost savings, reduced waste, and increased productivity across various industries. In the realm of cryptanalysis and code-breaking, quantum computers possess the ability to factor large numbers rapidly, raising concerns about data privacy and national security.

By replicating the behavior of molecules and materials with unparalleled accuracy, quantum computers in quantum chemistry and materials science allow researchers to develop new drugs, catalysts, and materials with particular properties. Lastly, quantum computing can improve traffic flow, schedule deliveries, and route vehicles in the transportation and logistics sectors. This might lead to fewer traffic jams, less fuel usage, and quicker travel times. All things considered, quantum computing has a wide range of possible applications that might completely transform a lot of different fields and industries in the years to come.


To sum up, this research paper has offered a thorough examination of the important factors related to bits processing in quantum computing. After providing an overview of classical bits and their shortcomings in handling intricate situations, the discussion turned to the novel idea of quantum bits, or qubits, and its special attributes like entanglement and superposition. The talk covered a range of physical realizations of qubits, such as topological qubits, trapped ions, and superconducting circuits, demonstrating the variety of methods used to achieve quantum information processing.

The study examined the significance of quantum gates and how they differ from classical logic gates as the fundamental components of quantum circuits. The difficulties with qubit decoherence and the requirement for error correction in quantum computing were discussed, highlighting the continuous efforts to get over these obstacles.

The study also described the useful uses of quantum computing and demonstrated how it might affect machine learning, cryptography, and optimization. Experiments and real-world examples were given to illustrate how quantum computing can revolutionize certain domains.

The revolutionary potential of quantum computing and the continuous research and development needed to properly achieve this potential are mainly highlighted in this essay. As quantum technologies advance, solving problems like decoherence will be necessary to fully realize the potential of quantum bits processing. Because of its revolutionary potential to transform many disciplines, quantum computing is a topic of ongoing interest and inquiry in the rapidly evolving field of information processing.


  1. Peñalvo, F. J., Sharma, A., Chhabra, A., Singh, S. K., Kumar, S., Arya, V., & Gaurav, A. (2022). Mobile Cloud Computing and Sustainable Development. International Journal of Cloud Applications and Computing, 12(1), 1–20.
  2. Peñalvo, F. J., Maan, T., Singh, S. K., Kumar, S., Arya, V., Chui, K. T., & Singh, G. P. (2022). Sustainable stock market prediction framework using machine learning models. International Journal of Software Science and Computational Intelligence, 14(1), 1–15.
  3. Singh, M., Singh, S. K., Kumar, S., Madan, U., & Maan, T. (2023). Sustainable Framework for metaverse security and privacy: Opportunities and challenges. Lecture Notes in Networks and Systems, 329–340.
  4. Kumar, S., Singh, S. K., & Aggarwal, N. (2023). Sustainable Data Dependency Resolution Architectural Framework to achieve energy efficiency using speculative parallelization. 2023 3rd International Conference on Innovative Sustainable Computational Technologies (CISCT).
  5. Chhabra, A., Singh, S. K., Sharma, A., Kumar, S., Gupta, B. B., Arya, V., & Chui, K. T. (2024). Sustainable and intelligent time-series models for epidemic disease forecasting and analysis. Sustainable Technology and Entrepreneurship, 3(2), 100064.
  6. SINGH, S., KARTIK, J. A. S. G., & KUMAR, S. The Role of Site Reliability Engineering in Sustainable Development. space, 2(10), 11.
  7. Singh, R., Singh, S. K., Kumar, S., & Gill, S. S. (2022). SDN-Aided Edge Computing-Enabled AI for IoT and Smart Cities. SDN-Supported Edge-Cloud Interplay for Next Generation Internet of Things, 41-70.
  8. Chopra, M., Kumar, S., Madan, U., & Sharma, S. (2021, December). Influence and establishment of smart transport in smart cities. In International Conference on Smart Systems and Advanced Computing (Syscom-2021).
  9. Kumar, S., Singh, S. Kr., Aggarwal, N., & Aggarwal, K. (2021). Evaluation of automatic parallelization algorithms to minimize speculative parallelism overheads: An experiment. Journal of Discrete Mathematical Sciences and Cryptography, 24(5), 1517–1528.
  10. Kumar, S., Singh, S. K., Aggarwal, N., Gupta, B. B., Alhalabi, W., & Band, S. S. (2022). An efficient hardware supported and parallelization architecture for Intelligent Systems to overcome speculative overheads. International Journal of Intelligent Systems, 37(12), 11764–11790.
  11. Kumar, S., Singh, S. K., & Aggarwal, N. (2023). Speculative parallelism on multicore chip architecture strengthens the green computing concept: A survey. In Advanced computer science applications (pp. 3-16). Apple Academic Press.
  12. Kumar, S. S., Singh, S. K., Aggarwal, N., & Aggarwal, K. (2021). Efficient speculative parallelization architecture for overcoming speculation overheads. In International Conference on Smart Systems and Advanced Computing (Syscom-2021) (Vol. 3080, pp. 132-138).
  13. Kjaergaard, M., Schwartz, M. E., Braumüller, J., Krantz, P., Wang, J. I.-J., Gustavsson, S., & Oliver, W. D. (2020). Superconducting qubits: Current State of Play. Annual Review of Condensed Matter Physics, 11(1), 369–395.
  14. HAFFNER, H., ROOS, C., & BLATT, R. (2008). Quantum computing with trapped ions. Physics Reports, 469(4), 155–203.
  15. Planat, M., Chester, D., Amaral, M. M., & Irwin, K. (2022). Fricke topological qubits. Quantum Reports, 4(4), 523–532.
  16. Chow, J. M., DiCarlo, L., Gambetta, J. M., Motzoi, F., Frunzio, L., Girvin, S. M., & Schoelkopf, R. J. (2010). Optimized driving of superconducting artificial atoms for improved single-qubit gates. Physical Review A, 82(4).
  17. Potter, A. C., & Vasseur, R. (2022). Entanglement dynamics in hybrid quantum circuits. Quantum Science and Technology, 211–249.
  18. Bennett, C. H., Brassard, G., & Ekert, A. K. (1992). Quantum Cryptography. Scientific American, 267(4), 50–57.

Cite As

Garg S, Katna S (2024) Bits processing in Quantum Computing, Insights2Techinfo, pp.1

68690cookie-checkBits processing in Quantum Computing
Share this:

Leave a Reply

Your email address will not be published.