A Quantum computer is a device that is used in quantum mechanics to perform various parallel computing tasks through different techniques such as superposition, entanglement, and decoherence.
Quantum computing is a technology that overcomes the limitations of conventional classical computer technology for high performance in the domain of application.
The history of quantum computing and basic concepts of quantum computing is necessary to understand. It is believed that within the span of the next 10 years, the clock frequency of the computer device will reach around 40GHz.
Table of Contents
Brief History of Quantum Computing
- In 1981 Richard Feyman proposed the use of quantum computing to make a model of the quantum system. In addition to this he also added a theoretical model for the quantum device.
- In the year 1985 david introduced the first quantum computer that is also known as the universal computer.
- In 1994 peter shor developed the first and very important algorithm which is prominent in computer science and even in computing known as shor’s algorithm for calculating factor of large numbers.
- In 1995 schumacher proposed “quantum bit” as a one resource.
- In 1996 grover developed one algorithm which is used to search and find the values in unsorted database structure.
- In 1998 the first quantum computer with two bits was developed by anonymous industry experts.
- In 2000 a 7-bit quantum computer was developed by an anonymous group.
- In 2001 7 qubits quantum computer was developed by ibm.
- From 2007-2017 D wave proposed many computers with 16 qubits,84,2000.
- In 2018 google announced and proposed their version containing 72 qubits with chip.
- In 2019 the global giant in the industry IBM revealed 53 qubits computers and research on it is still going on to improve and to make faster computers.
Basic concept of quantum computers
Quantum bit or what is Qubit: Qubit is the smallest unit of information in a quantum computer. It represents the state of the wave function when executed. It represents two states o (off) and 1 (on).
Superposition: superposition is a state where every quantum state represents itself as a sum of two or more distinct states. A single qubit can be forced into a superposition of two states by the addition of vectors. A qubit places the quantum information it holds in superposition.
This refers to the combination of all conceivable configurations of the qubit. “Groups of qubits in superposition can create complex, multidimensional computational spaces.”
Entanglement: when a pair of qubits particles are generated, and intersect, in such a way that the state of each particle of the pair cannot be described separately is called entanglement. In simple terms, entanglement is a joint characteristic of two quantum particles.
An entangled pair is a single quantum system that exists in a superposition of equally probable states. The entangled state provides no information about individual particles, merely that they are in opposing states. When one state changes, the other automatically adjusts to follow quantum mechanical norms.
Decoherence: The speedy interaction between quantum particles and the environment due to this condition causes a loss of superposition and is called decoherence in quantum computers. Preventing decoherence is the biggest challenge in building quantum computers.
Quantum gate: Quantum gate is a basic circuit that operates on qubits. It is a building block of quantum circuits as logic gates in traditional circuits. quantum gates are reversible.
How does a Quantum computer work?
Quantum computers use the qubits as the basis of information to communicate or any type of processing work. The main characteristic of quantum computers is superposition: A single qubit can be forced into a superposition of two states by addition of vectors. A qubit places the quantum information it holds in superposition.
This means qubits can be both zero and one at the same time.
Programming language and functionality: quantum computers do not have a programming language but it requires an implementation to build specific algorithms.in terms of functionality, it is not developed for daily use.
This is the supercomputer and is used in corporations to process large amounts of data. IBM uses quantum computation language for the development of algorithms.
System architecture: Quantum computers have a simple architecture that does not have memory or a processor rather it only has qubits.
Design and hardware
- Initial state preparation (Qubits)
- Measurement of final state
- Liquid state NMR
- NMR spin lattices
- Neutral atom
- Nitrogen vacancies in diamond
- Quantum hall qubits
- Qubit implementation
Classical Vs Quantum computers
Quantum computing can process large amounts of data in parallel processing better than traditional computing.
Unlike classical computers that depend on bits to represent data in the form of 0 and 1, quantum computers use quantum bits that are available in multiple states simultaneously. Due to this fundamental difference quantum computers can perform complex optimization tasks with 100% accuracy.
One of the prominent features of quantum computing in optimization problems is its ability to execute tasks in parallel processing methods while normal optimization algorithms often depend on the sequential processing that is after completion of one task the another task can be executed, until it is in the waiting state so this will be time consuming and not an effective approach.
Quantum computers work on principles from quantum mechanics such as superposition and entanglement to solve a different kind of problem to get optimized outcomes.
Application of quantum computers
- Large scale simulation: Ai can be used to predict outcomes for different actions with the help of simulation. For example, google maps used in self-driving cars or normal cars can simulate the best route of the destination location. Quantum computers can make this even faster that results in quick decisions in real time.
Quantum computers are highly adept at simulation due to their ability to concurrently process numerous potential outcomes. For example, in the field of autonomous driving, quantum computing has the potential to elevate instantaneous decision-making by running simulations of millions of potential scenarios simultaneously.
This capacity could notably enhance the safety and effectiveness of self-driving vehicles, enabling them to respond more rapidly to intricate traffic situations.
- Optimization in supply chain: In the supply chain industry one of the important things is to decide the best routes of delivering goods in minimal time and cost. Quantum computers can analyse various factors affecting and choosing the best solution in a short time.
The speed of processing large datasets can be accelerated by quantum computers, allowing for more efficient identification of patterns. This is particularly advantageous in areas such as fraud detection, where AI algorithms must rapidly spot suspicious behaviours within extensive transaction data.
Quantum-boosted AI has the potential to enhance the accuracy and real-time detection of fraudulent patterns, thereby thwarting fraud in advance.
- Data processing: To train models we need to feed huge amounts of data to the training model to identify patterns and predict the next outcome.
Quantum computers significantly speed up this process by handling a larger amount of complex datasets than normal computers. - Machine learning algorithms: support vector machines are a type of algorithm used by machine learning for classification-related problems for example phishing links, or spamming of email, etc. Quantum computers can handle large datasets to classify the problem in comparison to normal computers.
- Quantum Neural Networks: neural networks are one of the applications of AI which is designed the way the human brain processes information in day-to-day life while thinking. Quantum neural networks can handle large dataset patterns, and also it can improve the ability to learn and make decisions as fast as possible. For example, in the medical field with the help of quantum computers detection of cancer in the early stage could be beneficial in the future.
QNNs have the potential to transform deep learning through more efficient data processing compared to classical ANNs. By leveraging quantum mechanics, QNNs can process large datasets and carry out intricate calculations simultaneously, which could accelerate the training process and enhance model accuracy. - Data security: Data security is a major concern in today’s digital data as cyber-attacks frequently occur. Quantum computers help in improving security with quantum cryptography for strong encryption techniques.
AI systems could benefit from quantum cryptography, as it would enhance their security and safeguard sensitive data from cyberattacks.
With the ongoing advancement of quantum computing technology, the significance of quantum-safe encryption techniques in protecting digital information will continue to grow.
- Natural language processing: NLP is a branch of AI that deals with machines to understand human language. Examples include chatbots, virtual assistants, etc.
Quantum computers improve nlp in several ways. For example, it could process and analyze large amounts of text and data quickly leading to accurate results - Speech Recognition: Image and speech recognition are key areas of automation vehicles, Virtual assistant. Quantum computers can improve these technologies by enabling faster processing and accurate recognition.
Quantum algorithms have the potential to speed up the analysis of speech signals, leading to better performance of voice-controlled devices like Amazon Alexa and Google Assistant. Quantum computing could improve the precision of facial recognition systems in image recognition, making them more dependable in security applications.
- Recommendation Systems: Nowadays there is a lot of use of Ott platforms like netflix, amazon, and many others completely rely on AI for suggestion of product based on user choice and liking the content. Quantum computing can help input large amounts of data fast and quickly by identifying patterns.
The processing of large amounts of user data could be accelerated and made more accurate through quantum computing, which could lead to significant improvements in recommendation systems.
Quantum-enhanced recommendation algorithms have the potential to offer more precise and personalized recommendations by identifying patterns in user behavior.
Robotics: Quantum algorithms can help a robot to understand better by analyzing sensors and data quicker to make decisions and perform tasks.
In fields like manufacturing, healthcare, and logistics, the combination of quantum technology and AI has the potential to advance robotic capabilities, enabling them to handle complex decision-making and adapt to changing situations.
For example, in a medical environment, robots powered by quantum technology could support surgeons by analysing live data from medical devices and making accurate adjustments during surgical procedures.