The quantum Fourier transform (QFT);Similarities and differences of coherent optical FFT to the QFT. Grover’s search algorithm implemented with a coherent optical correlator. Quantum algorithms requiring bit entanglement -Shor’s algorithm for large number factorisation. Spatial Light Modulator (SLM) pixels placed in a binary superposition state. Addressed with an “interaction free” measurement. Allows exponential increase in processing power. Quantum search algorithm for a decryption problem based on superposition state of coherent wavefront.
The quantum Fourier transform (QFT);Similarities and differences of coherent optical FFT to the QFT. Grover’s search algorithm implemented with a coherent optical correlator. Quantum algorithms requiring bit entanglement -Shor’s algorithm for large number factorisation. Spatial Light Modulator (SLM) pixels placed in a binary superposition state. Addressed with an “interaction free” measurement. Allows exponential increase in processing power. Quantum search algorithm for a decryption problem based on superposition state of coherent wavefront.
Previously we have examined the similarities of the quantum Fourier transform to the classical coherent optical implementation of the Fourier transform (R. Young et al, Proc SPIE Vol 87480, 874806-1, -11). In this paper, we further consider how superposition states can be generated on coherent optical wave fronts, potentially allowing coherent optical processing hardware architectures to be extended into the quantum computing regime. In particular, we propose placing the pixels of a Spatial Light Modulator (SLM) individually in a binary superposition state and illuminating them with a coherent wave front from a conventional (but low intensity) laser source in order to make a so-called ‘interaction free’ measurement. In this way, the quantum object, i.e. the individual pixels of the SLM in their superposition states, and the illuminating wavefront would become entangled. We show that if this were possible, it would allow the extension of coherent processing architectures into the quantum computing regime and we give an example of such a processor configured to recover one of a known set of images encrypted using the well-known coherent optical processing technique of employing a random Fourier plane phase encryption mask which classically requires knowledge of the corresponding phase conjugate key to decrypt the image. A quantum optical computer would allow interrogation of all possible phase masks in parallel and so immediate decryption.
This survey presents a comprehensive analysis of the quantum Fourier transform (QFT), a fundamental tool in quantum computing, in comparison to its classical counterpart, the fast Fourier transform (FFT). The study begins with an introduction to the classical Fourier transform, which is widely used in different disciplines such as signal and image processing. The article introduces the QFT as a quantum version of the Fourier transform, detailing how it leverages quantum parallelism and superposition to reduce the time complexity of the operation, and highlighting its crucial role in quantum algorithms like Shor’s algorithm for integer factorization. The analysis also addresses the mathematical foundations of the QFT, its implementation in quantum circuits, and the key advantages and challenges associated with its use, such as measurement precision and quantum decoherence. Finally, it concludes with an exploration of the current and potential applications of QFT in quantum computing.
Ever since the realisation of the potential computational power of quantum-natured circuits, multiple quantum algorithms have been proposed, exploiting quantum superposition or quantum entanglement features to simulate quantum systems that classical computers cannot efficiently probe. The most notable among these strategies is Shor’s algorithm, which takes advantage of the quantum Fourier transform and has been shown capable of solving integer factorisation problems within polynomial time. This article’s focus is on one type of quantum algorithm based on amplitude amplification, namely Grover’s algorithm. This algorithm’s working principle is explained, and a discussion of its recent developments and possible direction of future reach is provided. In addition, implementations of Grover’s algorithm have been performed using the IBM Quantum Lab, followed by a reference to its connection to life sciences.
Quantum computing represents a revolutionary leap in computational power, leveraging the principles of quantum mechanics to process information in fundamentally new ways. Unlike classical computers, which use bits to represent data as 0s or 1s, quantum computers use quantum bits or qubits, enabling the phenomena of superposition and entanglement to perform complex calculations more efficiently (Nielsen & Chuang, 2010). This article explores the basic principles of quantum computing, the different models and algorithms that have been developed, and the current state of quantum hardware. The power of quantum computing lies in its ability to solve certain types of problems exponentially faster than classical computers. For instance, Shor's algorithm can factorize large integers in polynomial time, posing a significant threat to classical encryption methods (Shor, 1997). Similarly, Grover's algorithm provides a quadratic speedup for unstructured search problems, showcasing the potential for dramatic improvements in database search capabilities (Grover, 1996). These algorithms exemplify how quantum computing can revolutionize fields that rely on complex computations. Quantum hardware, however, is still in its nascent stage, with significant challenges to be overcome. Current quantum computers, such as those developed by IBM and Google, have demonstrated quantum supremacy but are limited by issues of qubit coherence and error rates (Arute et al., 2019). Quantum error correction and the development of more stable qubit technologies are critical areas of ongoing research (Preskill, 2018). Despite these challenges, recent advancements in quantum hardware and algorithms continue to drive the field forward, promising transformative impacts on industries such as cryptography, drug discovery, and artificial intelligence. As quantum computing continues to advance, it holds the promise of solving problems that are currently intractable for classical computers, paving the way for unprecedented technological and scientific advancements (Shor, 1997; Grover, 1996). This article provides a comprehensive overview of the current landscape of quantum computing, discussing its foundational principles, key algorithms, hardware challenges, and potential applications. By understanding these aspects, we can better appreciate the profound implications of quantum computing for the future of technology and science.
We demonstrated in the previous chapter that there are some quantum algorithms superior to their classical counterparts. It is, however, rather difficult to find any practical use of these algorithms. There are two quantum algorithms, known to date, which are potentially useful: Grover’s search algorithm and Shor’s prime number factorization algorithm. Both of them depend on quantum integral transforms, which will be introduced in the present chapter. We mainly follow [1] in our presentation.
Quantum computation provides algorithms that exploit superposition, interference, and entanglement to solve certain computational problems more efficiently than known classical algorithms. Two fundamental examples are Shor’s factoring algorithm and Grover’s search algorithm. In practical computational settings, however, information associated with quantum states, oracle responses, and measurements may be uncertain or indeterminate. In this paper, we introduce a mathematical framework for neutrosophic quantum algorithms in which quantum states are supplemented by three independent information components representing truth, indeterminacy, and falsity. We define neutrosophic quantum states, neutrosophic quantum measurements, neutrosophic quantum oracles, and scalarization functions. Using these concepts, we formulate neutrosophic versions of Grover’s search algorithm and Shor’s factoring algorithm. For Grover’s algorithm, the classical binary oracle is generalized to an oracle whose response may carry truth, indeterminacy, and falsity degrees. For Shor’s algorithm, neutrosophic information is incorporated into modular exponentiation, quantum Fourier transformation, measurement, and period estimation. We prove that the proposed construction contains the ordinary quantum algorithms as a special case. We also discuss complexity and indeterminacy propagation. The proposed theory is an information-theoretic mathematical extension of quantum algorithms. It is not intended to modify the physical postulates of quantum mechanics.