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International Journal of Image Graphics and Signal Processing

Transformation of Classical to Quantum Image, Representation, Processing and Noise Mitigation

Shyam R. Sihare

Published Oct 8, 2022• 6 citations
Publisher

Abstract

Quantum and classical computers have drastically different image representations.In a classical computer, bits are used.However, in a quantum computer, qubits are used.In this paper, the quantum image representation is the similar as the classical image representation.To represent quantum images, qubits and their associated properties have been used.Quantum imaging has previously been done via superposition.As a result, quantum imaging implemented using the superposition feature.Unitary matrices are then used to represent quantum circuits.For the quantum representation, we've gone with a modest image.To create quantum circuits, IBM's Qiskit software and Anaconda Python was used.On an IBM real time computer and an Aer simulator, a quantum circuit with 10,000 shots runs.Noise has been reduced more in the IBM real time computer than in the IBM Aer simulator.As a result, the Aer simulator's noise and qubit errors are higher than the IBM real time computer's.Quantum circuit design and image processing are both done with Qiskit programming, which is an appendix at the end of the paper.As the number of shots raise, the noise level decreases even further.Noise and qubit errors increase when the image operates at a low number of shots.Quantum image processing, noise reduction, and error correction done by circuit computation shots increase.Quantum image processing, representation, noise reduction, and error correction all make use of the quantum superposition concept.

DOI
10.5815/ijigsp.2022.05.02

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Quantum and classical computers have drastically different image representations.In a classical computer, bits are used.However, in a quantum computer, qubits are used.In this paper, the quantum image representation is the similar as the classical image representation.To represent quantum images, qubits and their associated properties have been used.Quantum imaging has previously been done via superposition.As a result, quantum imaging implemented using the superposition feature.Unitary matrices are then used to represent quantum circuits.For the quantum representation, we've gone with a modest image.To create quantum circuits, IBM's Qiskit software and Anaconda Python was used.On an IBM real time computer and an Aer simulator, a quantum circuit with 10,000 shots runs.Noise has been reduced more in the IBM real time computer than in the IBM Aer simulator.As a result, the Aer simulator's noise and qubit errors are higher than the IBM real time computer's.Quantum circuit design and image processing are both done with Qiskit programming, which is an appendix at the end of the paper.As the number of shots raise, the noise level decreases even further.Noise and qubit errors increase when the image operates at a low number of shots.Quantum image processing, noise reduction, and error correction done by circuit computation shots increase.Quantum image processing, representation, noise reduction, and error correction all make use of the quantum superposition concept.
Quantum image representation is an essential component of quantum image processing and plays a critical role in quantum information processing. Flexible Representation of Quantum Images (FRQI) presents pixel colors and associated locations as a state to represent images on quantum computers. A fundamental part of the quantum image processing system is quantum image compression (QIC), which is utilized to maintain and retrieve binary images. This compression allows us to minimize the number of controlled rotation gates in the quantum circuits. This paper designed optimized quantum circuits and simulated them using Qiskit on a real quantum computer based on minimum boolean expressions to retrieve the 8×4 binary single-digit images. To demonstrate the feasibility and efficacy of quantum image representation, quantum circuits for images were developed using FRQI, and quantum image representation experiments were done on IBM Quantum Experience (IBMQ). We were able to visualize quantum information by doing the quantum measurement on the image information that we had prepared. Without utilizing this method, the number of controlled rotation gates is equal to the number of pixels in the image; however, we showed that by using the QIC algorithm, we could decrease the number of gates significantly. On these images, the maximum and minimum compression ratios of QIC are 90.63% and 68.75%, respectively.
AbstractThe quantum computing has been proven to provide the solutions to most complex computations in the world today. Quantum computers can solve the problems with polynomial resources. Quantum information can be encoded in a number of ways, such as spin components of basic particles like photons, electrons, and polarization of photons. The quantum computer uses quantum bits which are superposition of states called as qubits, which is the classical fundamental unit of information. A quantum computer is a regular classical computer with additional ability to control the quantum bits. It is a regular computer connected to a special peripherals and a set of quantum bits simulated to build controller architecture. Two simulators are used for the quantum programming, one is Microsoft Q# and other is IBM Qiskit. Classical language Q # is used to program the quantum controller and its operations such that initializing a qubit, applying a gate on a qubit and measuring a qubit. IBM Qiskit programs simulates the functionality of quantum circuit and creates it’s drawing using a graphical interface with qubits, gates, and measurements. Qiskit provides simulators that run quantum circuits using visualization tools that help to understand the effect of our quantum circuits on the quantum states. The teleportation technique allows the quantum information to be communicated at a distance. An unbreakable encryption protocol is built for polarizing photons through the quantum channel. The combination of quantum computing and AI has the potential to drive significant advances across many sectors.
Quantum image processing represents a transformative approach to visual data analysis, leveraging the principles of quantum computing to overcome classical limitations. This work explores two prominent quantum image encoding methods: FRQI (Flexible Representation of Quantum Images) and NEQR (Novel Enhanced Quantum Representation). FRQI excels in qubit efficiency, making it suitable for hardware implementation, while NEQR offers superior precision in pixel intensity representation, ideal for complex image processing tasks. We detail the implementation of these algorithms, including preprocessing, quantum circuit design, and simulation, using platforms like Qiskit. The study highlights the potential of quantum image processing in fields such as medicine, industry, and environmental monitoring, while addressing challenges like qubit limitations and noise sensitivity. This research contributes to advancing quantum computing applications, paving the way for innovative and sustainable technological solutions.
Abstract Quantum Image Representation (QIR) is a fundamental concept in quantum image processing. It describes how classical images are converted into quantum states for processing on quantum computers. Researchers proposed several QIR models, with FRQI and NEQR being the most popular ones. These models show the possibilities of the quantum image encoding. Nevertheless, they are also limited in some way. Mostly, pixel intensity accuracy, the required number of qubits, and circuit complexity have trade-offs. Their application to Noisy Intermediate-Scale Quantum (NISQ) devices becomes challenging, the quantum resources being small in such devices, due to this. In the following paper, we introduce our own approach, which is the Intensity-Preserving Quantum image Representation (IP-QIR). The key aim of IP-QIR is to maintain the information about the grayscale intensities and decrease the amount of quantum resources. IP-QIR quantifies pixel intensities by a controlled rotation scheme. The measurement statistics of a single qubit hold the intensity information and the position qubits represent the spatial information. The strategy uses small patches of images rather than complete images to make it easy to implement. This will decrease the depth of circuit design and makes the approach less advanced to the near-term quantum hardware. IBM Qiskit simulations are used in assessing the performance of IP-QIR. The three types of grayscale images of which experiments are run are the synthetic image patches, SAR images and medical TB chest X-ray images. It is revealed that IP-QIR maintains the intensity information compared to the FRQI and NEQR. SAR and medical data can reach values of fidelity of 84.12\%. The other notable point is that the size of a 4 x 4 image patch only needs five qubits which are much lower than in NEQR. Meanwhile, good reconstruction accuracy is achieved. These findings demonstrate the fact that IP-QIR is an effective and realistic approach to quantum image representation on the NISQ-era quantum devices.
Quantum Image Processing (QIP)is an exciting new field showing a lot of promise as a powerful addition to the arsenal of Image Processing techniques. Representing image pixel by pixel using classical information requires an enormous amount of computational resources. Hence, exploring methods to represent images in a different paradigm of information is important. In this work, we study the representation of images in Quantum Information. The main motivation for this pursuit is the ability of storing N bits of classical information in only log(2N) quantum bits (qubits). The promising first step was the exponentially efficient implementation of the Fourier transform in quantum computers as compared to Fast Fourier Transform in classical computers. In addition, images encoded in quantum information could obey unique quantum properties like superposition or entanglement.
This paper represents an accessible overview of quantum program development with IBM's Qiskit. Current quantum computers are still making a lot of mistakes since we are in the NISQ era, where noise is the main problem. Qiskit developers are assisted by means of which they can readily plan their circuits and then the versions which are to be run on the actual hardware are automatically created by the Transpiler. Simulation of the circuit along with the check of how the noise can change the result is done by Qiskit Aer before the program is sent to the real quantum computer. As noise is the cause of the major problem, there is also the provision of error-reduction methods like Readout Error Mitigation and Zero Noise Extrapolation (ZNE) in Qiskit to eliminate the noise. Hybrid algorithms such as VQE and QAOA are increasingly becoming instrumental in solving real problems in chemistry, finance, and machine learning, the paper says, too. The study sums up that Qiskit has become the most popular open-source platform that is leading the way to the next quantum computing era and making the quantum advantage feasible