Quantum image representation is a widely researched area of quantum computing. Currently developed methods use angle parameter of the rotation gate (e.g., the FRQI method), sequences of qubits (ex. NEQR method) or phase shift (ex. LPIQE method) for storing color information of pixels. All of those methods are affected by decoherence and other classical and quantum noise, which is an inseparable part of quantum computing in a NISQ (Noisy Intermediate Scale Quantum) era. These all phenomenons influence the measurements, its probability distribution over the measurement basis and, as the result, makes the extracted images not even similar to those, which was stored in quantum computers. Since this process is, in its foundation, quantum as well, the computational reversal of this process is possible. There are a lot of methods for error correction, mitigation and reduction, but all of them use quantum computer time, or additional qubits to achieve desired result. We report a successful use of the Generative Adversarial Network tuned for image-to-image translation in conjunction with PDU method, for error reduction in images encoded using LIPQE (Local Phase Image Quantum Encoding) method.
Quantum image representation is a widely researched area of quantum computing. Currently developed methods use angle parameter of the rotation gate (e.g., the FRQI method), sequences of qubits (ex. NEQR method) or phase shift (ex. LPIQE method) for storing color information of pixels. All of those methods are affected by decoherence and other classical and quantum noise, which is an inseparable part of quantum computing in a NISQ (Noisy Intermediate Scale Quantum) era. These all phenomenons influence the measurements, its probability distribution over the measurement basis and, as the result, makes the extracted images not even similar to those, which was stored in quantum computers. Since this process is, in its foundation, quantum as well, the computational reversal of this process is possible. There are a lot of methods for error correction, mitigation and reduction, but all of them use quantum computer time, or additional qubits to achieve desired result. We report a successful use of the Generative Adversarial Network tuned for image-to-image translation in conjunction with PDU method, for error reduction in images encoded using LIPQE (Local Phase Image Quantum Encoding) method.
Despite the limited availability and quantum volume of quantum computers, quantum image representation is a widely researched area. Currently developed methods use quantum entanglement to encode information about pixel positions. These methods range from using the angle parameter of the rotation gate (e.g., the Flexible Representation of Quantum Images, FRQI), sequences of qubits (e.g., Novel Enhanced Quantum Representation, NEQR), or the angle parameter of the phase shift gates (e.g., Local Phase Image Quantum Encoding, LPIQE) for storing color information. All these methods are significantly affected by decoherence and other forms of quantum noise, which is an inseparable part of quantum computing in the noisy intermediate-scale quantum era. These phenomena can highly influence the measurements and result in extracted images that are visually dissimilar to the originals. Because this process is at its foundation quantum, the computational reversal of this process is possible. There are many methods for error correction, mitigation, and reduction, but all of them use quantum computer time or additional qubits to achieve the desired result. We report the successful use of a generative adversarial network trained for image-to-image translation, in conjunction with Phase Distortion Unraveling error reduction method, for reducing overall error in images encoded using LPIQE.
Quantum Image Representation is a well-studied area in quantum informatics. The first proposed algorithm was the qubit lattice method introduced in 2003. This method was not harnessing the full potential of the quantum system - using as many qubits as there were pixels in the image. The most commonly used methods right now are Flexible Representation of Quantum Images (FRQI) proposed in 2011, and Novel Enhanced Quantum Representation (NEQR) proposed in 2013, which uses superposition to encode a monochrome image on a quantum computer using a reduced number of qubits. In this paper, we want to compare these two methods, alongside Local Phase Image Quantum Encoding (LPIQE) method proposed in 2020, to see which method produces images with minimal data loss. Another comparison will be done with image encoded using the LPIQE method with Phase Distortion Unraveling (PDU) error mitigation method, which is done entirely on a classical computer. LPIQE implementation was intended for using the photonic quantum system since the phase-shift is easy to produce, and it will be implemented on real, photonic quantum computers after further development.
We report a successful results in the quantum image encoding - reconstruction experiments with the usage of local phase for pixel representation. This implementation was intended for using the photonic quantum system, since the phase shift is easy to produce. Our method will be implemented in real photonic quantum computers, after further development.Quantum Image Representation is well studied area in quantum informatics. The first proposed algorithm was the qubit-lattice method introduced in 2003. This method was not harnessing the full potential of quantum system - using as many qubits as there were pixel in the image.Most commonly used methods right now are Flexible Representation of Quantum Images (FRQI) proposed in 2011, and Novel Enhanced Quantum Representation (NEQR) proposed in 2013, which uses superposition to encode a monochrome image on a quantum computer using reduced number of qubits.In this paper we want to propose the combination of Local Phase Image Quantum Encoding method and Phase Distortion Unraveling (PDU) error mitigating method as an alternative method of quantum image encoding, producing satisfying results.
Quantum image processing (QIP) is still a promising area as the noise of qubit, decoherence and limitations of encoding make it difficult for the current quantum hardware.This paper presents a combined Intelligent Quantum Image Denoising Framework, which uses FRQI and deep learning-based restoration, to enhance the quality and trustworthiness of images in quantum-classical hybrid areas.The short overview of related studies points out the classical filters and shallow neural models denoising methods, which are not able to generalize to quantum noise.To fill this void, a Convolutional Neural Network (CNN) is trained on the FRQI-encoded images simulated with Gaussian, saltand-pepper, and depolarizing noise.The approach uses confidence-based thresholding for restoration of adaptive pixels and measures the results with PSNR, SSIM, and MSE metrics.The experimental outcomes exhibit a considerable rise in the signal-to-noise ratio and the integrity of the structure when compared to the standard denoising techniques.The research indicates that the suggested combination of different methods has the capability to get rid of quantum noise yet at the same time allow the main image features to be seen.The steps for the next research will be applying the model to actual quantum simulators like Qiskit, creating better qubit usage, and widening the application of the framework to high-resolution images for quantum vision in real-world scenarios.
Quantum computers, currently in the noisy intermediate-scale quantum (NISQ) era, have started to provide scientists with a novel tool to explore quantum physics and chemistry. While several electronic systems have been extensively studied, Frenkel excitons, as prototypical optical excitations, remain among the less-explored applications. Here, we first use variational quantum deflation to calculate the eigenstates of the Frenkel Hamiltonian and evaluate the observables based on the oscillator strength for each eigenstate. Furthermore, using NISQ quantum computers requires performing error mitigation techniques alongside simulations. To deal with noisy qubits, we developed a deep-learning-based framework combined with a post-selection technique to learn the noise pattern and mitigate the error. Our mitigation methods work well and outperform the conventional post-selection and remain valid on real hardware.
The domain of image classification has been seen to be dominated by high-performing deep-learning (DL) architectures. However, the success of this field, as seen over the past decade, has resulted in the complexity of modern methodologies scaling exponentially, commonly requiring millions of parameters. Quantum computing (QC) is an active area of research aimed toward greatly reducing problems of complexity faced in classical computing. With growing interest toward quantum machine learning (QML) for applications of image classification, many proposed algorithms require usage of numerous qubits. In the noisy intermediate-scale quantum (NISQ) era, these circuits may not always be feasible to execute effectively; therefore, we should aim to use each qubit as effectively and efficiently as possible, before adding additional qubits. This article proposes a new single-qubit-based deep quantum neural network for image classification that mimics traditional convolutional neural network (CNN) techniques, resulting in a reduced number of parameters compared with previous works. Our aim is to prove the concept of the initial proposal by demonstrating classification performance of the single-qubit-based architecture, as well as to provide a tested foundation for further development. To demonstrate this, our experiments were conducted using various datasets including MNIST, Fashion-MNIST, and ORL face datasets. To further our proposal in the context of the NISQ era, our experiments were intentionally conducted in noisy simulation environments. Initial test results appear promising, with classification accuracies of 94.6%, 89.5%, and 82.5% achieved on the subsets of MNIST, FMNIST, and ORL face datasets, respectively. In addition, proposals for further investigation and development were considered, where it is hoped that these initial results can be improved.