Quantum computing is miles ahead of conventional computing when it comes to speed and performance. The IBM Qiskit SDK is a free, open-source quantum computing toolkit and provides a set of useful tools for developers working with quantum computing.
Quantum computing uses principles of quantum mechanics to perform certain tasks faster than traditional computers. Although quantum computing is still in its infancy, it has the potential to transform industries such as cryptography, materials research, and drug discovery.
In a quantum computer, the basic unit of information is called a qubit (short for “quantum bit”). Unlike traditional bits, which are constrained to have a value of 0 or 1, qubits can occur in a superposition of 0s and 1s. This superposition property allows quantum computers to outperform conventional computers in terms of exponential speed. Qubits have another unique quantum feature known as entanglement. This is a phenomenon where two or more qubits become coupled in a way that their state can be correlated, even though they are physically separate. Superconducting circuits, trapped ions, and quantum dots are just a few of the physical systems that can be used to construct qubits. In terms of scale, coherence time (how long a qubit can maintain its quantum state), and error rates, these physical systems have various strengths and limitations.
The need for quantum computers stems from the fact that some computational problems are extremely difficult to solve with traditional computers. These problems often involve complicated systems such as molecules, substances, and subatomic particles, and therefore require a large amount of computational power to solve them.
By using quantum mechanical concepts, quantum computing can solve many of these problems much faster than classical computers. For example, quantum computing can be used in drug development, materials design, and other fields to model the behavior of molecules and materials. Quantum computers are also able to decipher some encryption algorithms, which could have implications for strong encryption and financial transactions.
The exponential expansion of data in current computing systems is another argument for the need for quantum computing. The time required to solve large problems in classical computing increases exponentially with the complexity of the problem. Quantum computing may provide a solution to this problem by allowing calculations to be performed 10 times faster than traditional computers, which may have implications for data analysis and machine learning.
So, the demand for quantum computing stems from the need to tackle complicated problems that are beyond the capacity of traditional computers, as well as to provide a more efficient way to process and analyze large amounts of data.
Important Use Cases and Application Areas of Quantum Computing Figure 1: Use cases and applications of Quantum Computing
Cryptography: Quantum computing is capable of decrypting today’s most commonly used encryption algorithms, such as RSA and elliptic curve cryptography. It can also be used to create novel encryption mechanisms that are immune to quantum attacks.
Materials Science: Quantum computing can be used to model the behavior of molecules and materials, which can be beneficial in drug development, materials design, and other areas. Quantum computers can perform some simulations ten times faster than classical computers, allowing researchers to study intricate chemical interactions and model the behavior of materials at the quantum level.
Optimization: Several real-world problems, including planning, transportation, and logistics, can be expressed as optimization problems. Quantum computers have the potential to solve some of these problems much faster than traditional computers, which could impact industries like finance and aerospace.
Machine Learning: Some machine learning techniques, such as B. support vector machines and clustering algorithms can benefit from quantum computing. Quantum machine learning has the ability to tackle problems that traditional machine learning cannot.
Quantum simulation: Quantum computers can mimic the behavior of quantum systems, including chemical processes and superconductivity. This can reveal details about the behavior of quantum systems that are difficult or impossible to detect experimentally.
Portfolio Allocation and Optimization: Quantum computing can be used to optimize risk and return in portfolio allocations. This can assist investors in making more informed investment decisions and risk management. Quantum computing has the potential to transform the financial industry by enabling faster and more accurate calculations and simulations.
Risk Management: Quantum computing can be used to model the behavior of complicated financial systems, potentially aiding financial organizations in managing risk. This may include simulating market collapse behavior, assessing the impact of legislative reforms and improving risk management measures.
Fraud detection: Quantum computing can be used to detect suspicious transactions by analyzing huge amounts of financial data. This could help prevent financial crime.
Free and open source tools for quantum computing
A number of tools and platforms are available for simulating quantum computing. Excellent options for researchers and practitioners, these open-source and free tools provide valuable resources for creating, modeling, and running quantum computing programs.
The main free and open source tools available for implementing quantum computing scenarios are listed below.
Figure 2: IBM Qiskit platform IBM Qiskit
https://qiskit.org/
IBM Qiskit is a comprehensive platform under free and open source distribution for quantum computing. It provides tools to create and simulate quantum circuits and run experiments on IBM’s quantum computers. Many libraries and plugins are available in Qiskit to work with high-performance quantum scenarios for a range of applications.
PyQuil
https://github.com/rigetti/pyquil
Rigetti Computing’s PyQuil is an open-source Python toolkit for building quantum applications and interacting with quantum processors. It provides tools to develop and simulate quantum programs and interact with Rigetti’s quantum computers.
Googlecirc
https://quantumai.google/cirq
Google Cirq is an open source Python library for building quantum algorithms. It provides tools to create, simulate and interact with quantum programs with Google’s quantum computers.
Figure 3: IBM Quantum Lab for simulations of quantum implementations ProjectQ
https://projectq.ch/
ETH Zurich has developed ProjectQ, an open-source Python library for quantum computing. It provides tools for creating quantum programs, simulating them, and interacting with quantum computers.
OpenQASM
https://github.com/openqasm/openqasm
OpenQASM is a free, open-source platform for building and running quantum applications. It works in tandem with IBM’s Qiskit platform, providing tools for creating quantum circuits, running simulations, and running experiments on IBM’s quantum computers.
Qiskit SDK for simulating quantum scenarios
As mentioned earlier, Qiskit is an open-source quantum computing platform developed by IBM that includes tools to create quantum circuits, perform calculations, and perform operations on IBM’s quantum computers. The Qiskit SDK provides a variety of quantum simulation resources that enable researchers and developers to explore the possibilities of quantum computing and develop novel algorithms and applications.
Powerful tools and libraries built into the Qiskit SDK Qiskit Aer
Qiskit Ignis
Qiskit Aqua
Qiskit nature
circuit composer
Qiskit Terra
Qiskit Pulse
Machine Learning by Qiskit
Installing and working with IBM Qiskit
After installing Python in the system, the IBM Qiskit library can be integrated by running the following command in command prompt or terminal:
$ pip installs qiskit
This command will install the latest version of Qiskit with the dependencies.
To ensure that the Qiskit platform is properly installed in the system, the following command is run from the command prompt or terminal:
$ qiskit version
After installing Qiskit, designing, simulating and running quantum circuits can be done very effectively. Qiskit includes a variety of tools for dealing with quantum circuits. Circuit Composer helps implement this technology, and Aer is used to simulate circuit designs and run circuits on quantum computers.
Figure 4: Generation of a quantum circuit after code execution
Here is an example code snippet for creating a quantum circuit:
import numpy as np from qiskit.providers.aer import QasmSimulator from qiskit import QuantumCircuit, transpile simulator1 = QasmSimulator() circuit1 = QuantumCircuit(2, 2) circuit1.h(0) circuit1.cx(0, 1) circuit1.measure([0,1], [0,1]) compiled_circuit1 = transpile(circuit1, simulator1) job1 = simulator1.run(compiled_circuit1, shots1=1000) result1 = job1.result() counts1 = result.get_counts(compiled_circuit) print(“\nCount 00 and 11 :”,counts1) circuit1 .pull()
This code can be run on the IBM Qiskit platform and the output circuit will be generated. The code snippet can be run on the IBM Quantum Lab to visualize the results.
Quantum computing is a continuously evolving technology with enormous research and development potential. New quantum-based algorithms can be developed that use the unique capabilities of quantum computers to solve problems faster and more effectively than classical computers. Quantum error correction is another area of study that can develop methods to detect and correct errors in quantum computations, which is essential for the development of reliable large-scale quantum computers. The study and development opportunities in this subject will only increase if quantum computers continue to develop and become more powerful.
