Quantum computing promises an exponential speedup improvement in the time required to solve certain computational problems by leveraging the principles of quantum mechanics.While the potential power of quantum computers is well-established, their practical implementation poses significant challenges ranging from scalability to decoherence and noise.This paper presents QPy, a Python-based quantum circuit simulator that I programmed and designed to track the quantum state of a model quantum computer through the application of quantum gates to qubits.By performing the requisite matrix calculations, the simulator facilitates the understanding of quantum algorithms through the visualization of the underlying mathematics.This tool enables researchers to explore and implement various quantum protocols efficiently to test and build algorithms.
Quantum computing promises an exponential speedup improvement in the time required to solve certain computational problems by leveraging the principles of quantum mechanics.While the potential power of quantum computers is well-established, their practical implementation poses significant challenges ranging from scalability to decoherence and noise.This paper presents QPy, a Python-based quantum circuit simulator that I programmed and designed to track the quantum state of a model quantum computer through the application of quantum gates to qubits.By performing the requisite matrix calculations, the simulator facilitates the understanding of quantum algorithms through the visualization of the underlying mathematics.This tool enables researchers to explore and implement various quantum protocols efficiently to test and build algorithms.
n the field of quantum computing, the properties of quantum superposition offer a promising potential for solving complex problems in a few minutes, while classical computers would take years to solve the same problem. Therefore, many institutions have introduced courses related to quantum mechanics and quantum computing into their programs. However, there is currently no known web-based tool or PC application dedicated to the study of quantum computing. Quantum computers are not available for educational purposes. In the early stages of learning, students may benefit from a way to practice quantum computing with a visual tool. Quantum computing is usually expressed by a quantum circuit with its sequences of quantum gates. The article talks about the creation of an educational simulator for the study of quantum circuits with various gates, functions and transformation rules. This quantum simulator will help students learn the principles of quantum circuitry in their undergraduate and graduate courses. This tool will offer functions that allow you to build a quantum circuit, add and remove quantum gates, apply transformation rules, and observe the operation of a quantum circuit with its unitary matrix. By practicing with the quantum circuit simulator, students can strengthen their theoretical knowledge base about quantum computing and linear algebraic functions
Quantum computation is a radical new candidate for a technology that is capable to make a paradigm shift in information processing. However, current promising devices are limited in performance due to many decoherence mechanisms. Quantum computer simulators are thus important for understanding and solving existing problems of the current noisy intermediate-scale quantum (NISQ) processors. In this work, we present Psitrum – a universal gate-model based quantum computer simulator implemented on classical hardware. The simulator allows to emulate and debug quantum algorithms in form of quantum circuits for many applications with the choice of adding quantum noise that limit coherence of quantum circuits. Psitrum allows to keep track of quantum operations and density matrices, and provides variety of unique visualisation tools. The simulator allows to trace out all possible quantum states at each stage M of an N-qubit implemented quantum circuit. The design of Psitrum is flexible and allows users later to add their special quantum gates and variety of noise modules.
Recent advancements in computing speed and capacity of Artificial Intelligence (AI) algorithms have reached a saturation level in performance due to the continuous application of Moore's law which resulted in the memory wall phenomenon. Quantum computing (QC), based on the principles of quantum mechanics which include superposition and entanglement, has the potential to contribute to the advancements of AI by providing exponential speedup. Quantum computers use quantum bits, also known as qubits, as the metric to demonstrate the power of quantum technology. Qubits, the fundamental unit of quantum information can exist in two states, 0 and 1, simultaneously, leveraging the principle of superposition. Quantum gates are the building blocks of quantum circuits, operating on qubits to transform one quantum state into another, and are comparative to classical logic gates. Designing and simulating quantum circuits can be completed using the Qiskit library design by IBM. Qiskit is a cloud-based, open access platform to real quantum computers with 5 to 16 qubits. This paper offers a straightforward tutorial on implementing one, two and three-qubit quantum gates, as well as designing quantum circuits using the Qiskit library, illustrated with an example of a quantum half adder implementation.
Analysis and verification of quantum circuits are highly challenging, given the exponential dependence of the number of states on the number of qubits. For analytical derivation, we propose a new quantum polynomial representation (QPR) to facilitate the analysis of massively parallel quantum computation and detect subtle errors. For the verification of quantum circuits, we introduce Quantum-Chiplet, a hierarchical quantum behavior modeling methodology that facilitates rapid integration and simulation. Each chiplet is systematically transformed into quantum gates. For circuits involving n qubits and k quantum gates, the design complexity is reduced from "greater than O(2^n)" to O(k). This approach provides an open-source solution, enabling a highly customized solution for quantum circuit simulation within the native Python environment, thereby reducing reliance on traditional simulation packages. A quantum amplitude estimation example demonstrates that this method significantly improves the design process, with more than 10x speed-up compared to IBM Qiskit at 14 qubits.
Quantum computing is a promising approach of computation that is based on equations from Quantum Mechanics. A simulator for quantum algorithms must be capable of performing heavy mathematical matrix transforms. The design of the simulator itself takes one of three forms: Quantum Turing Machine, Network Model or circuit model of connected gates or, Quantum Programming Language, yet, some simulators are hybrid. We studied previous simulators and then we adopt features from three simulators of different implementation languages, different paradigms, and for different platforms. They are Quantum Computing Language (QCL), QUASI, and Quantum Optics Toolbox for Matlab 5. Our simulator for quantum algorithms takes the form of a package or a programming library for Quantum computing, with a case study showing the ability of using it in the circuit model. The .NET is a promising platform for computing. VB.NET is an easy, high productive programming language with the full power and functionality provided by the .NET framework. It is highly readable, writeable, and flexible language, compared to another language such as C#.NET in many aspects. We adopted VB.NET although its shortage in built-in mathematical complex and matrix operations, compared to Matlab. For implementation, we first built a mathematical core of matrix operations. Then, we built a quantum core which contains: basic qubits and register operations, basic 1D, 2D, and 3D quantum gates, and multi-view visualization of the quantum state, then a window for demos to show you how to use and get the most of the package.
Context: Grover's algorithm is a powerful tool in the realm of quantum computing because it offers exponential speedup over classical linear searching for unstructured / unsorted search problems. However, it is challenging to construct efficient and scalable circuits for a multi-input control gate in the algorithm, particularly with a huge number of qubit inputs. Problem: Traditional circuit construction methods often require manual composition for the multi-input gate from basic quantum gates, which can be time consuming and error prone, thus hindering practical implementation. Moreover, the number of simple gates in the composition grows exponentially with the addition of each qubit. Method: This research provides an approach to recursively generate the oracle using Toffoli gates as the fundamental units to enhance scalability and reusability while maintaining effectiveness. The methodology leverages IBM Qiskit and Quantum Lab to simulate quantum computation to prove the practicality and feasibility. Result: The findings from the experiments between recursively and non-recursively generated circuits suggest that our recursive approach can offer comparable accuracy and computational complexity compared to the non-recursive approach. However, it tends to require a larger number of basic quantum gates due to a lack of circuit optimization. Conclusion: The results showcase the applicability of utilizing recursion as a speedy and economical alternative to generate circuits when implementing the Grover's algorithm. It not only presents potential scalability benefits, but also creates an avenue for further research on test verification and performance improvement to the computing community when running in actual quantum hardware.