Quantum Image Processing: the truth, the whole truth, and nothing but
the truth about its problems on internal image representation and outcomes
recovering
- URL: http://arxiv.org/abs/2002.04394v3
- Date: Wed, 24 Jun 2020 17:09:52 GMT
- Title: Quantum Image Processing: the truth, the whole truth, and nothing but
the truth about its problems on internal image representation and outcomes
recovering
- Authors: Mario Mastriani
- Abstract summary: Three techniques of internal image-representation in a quantum computer are compared.
This study demonstrated the practical infeasibility in the implementation of FRQI and NEQR on a physical quantum computer.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, three techniques of internal image-representation in a quantum
computer are compared: Flexible Representation of Quantum Images (FRQI), Novel
Enhanced Quantum Representation of digital images (NEQR), and Quantum Boolean
Image Processing (QBIP). All conspicuous technical items are considered in this
comparison for complete analysis: i) performance as Classical-to-Quantum
(Cl2Qu) interface, ii) characteristics of the employed qubits, iii) sparsity of
the used internal registers, iv) number and size of the required registers, v)
quality in the outcomes recovering, vi) number of required gates and its
consequent accumulated noise, vi) decoherence, and vii) fidelity. These
analyses and demonstrations are automatically extended to all variants of FRQI
and NEQR. This study demonstrated the practical infeasibility in the
implementation of FRQI and NEQR on a physical quantum computer (QPU), while
QBIP has proven to be extremely successful on a) the four main quantum
simulators on the cloud, b) two QPUs, and c) optical circuits from three labs.
Moreover, QBIP also demonstrated its economy regarding the required resources
needed for its proper function and its great robustness (immunity to noise),
among other advantages, in fact, without any exceptions.
Related papers
- Supervised Quantum Image Processing [1.0499611180329806]
Quantum image processing (QIP) is an interdisciplinary field between quantum information science and image processing.<n>We compare and examine the compression properties of four different Quantum Image Representations (QImRs)<n>Our results indicate that quantum kernels provide comparable classification average accuracy but require exponentially fewer resources for image storage.
arXiv Detail & Related papers (2025-07-29T17:40:59Z) - VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
arXiv Detail & Related papers (2025-06-12T01:38:15Z) - QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations [1.874615333573157]
This paper introduces Quantum Fourier Gaussian Network (QFGN), a quantum-based machine learning model for better signal representations.
The results demonstrate that with minimal parameters, QFGN outperforms the current state-of-the-art (SOTA) models.
Despite noise on hardware, the model achieves accuracy comparable to that of SIREN, highlighting the potential applications of quantum machine learning in this field.
arXiv Detail & Related papers (2025-04-26T23:40:33Z) - Image Denoising with Machine Learning: A Novel Approach to Improve Quantum Image Processing Quality and Reliability [3.8704324110545767]
Quantum Image Processing (QIP) aims to utilize the benefits of quantum computing for manipulating and analyzing images.
QIP faces two challenges: the limitation of qubits and the presence of noise in a quantum machine.
We propose a novel approach to address the issue of noise in QIP by training and employing a machine learning model that identifies and corrects the noise in quantum-processed images.
arXiv Detail & Related papers (2024-02-18T16:55:54Z) - Hybrid quantum transfer learning for crack image classification on NISQ
hardware [62.997667081978825]
We present an application of quantum transfer learning for detecting cracks in gray value images.
We compare the performance and training time of PennyLane's standard qubits with IBM's qasm_simulator and real backends.
arXiv Detail & Related papers (2023-07-31T14:45:29Z) - Quantum Imitation Learning [74.15588381240795]
We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL.
We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL)
Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
arXiv Detail & Related papers (2023-04-04T12:47:35Z) - Delegated variational quantum algorithms based on quantum homomorphic
encryption [69.50567607858659]
Variational quantum algorithms (VQAs) are one of the most promising candidates for achieving quantum advantages on quantum devices.
The private data of clients may be leaked to quantum servers in such a quantum cloud model.
A novel quantum homomorphic encryption (QHE) scheme is constructed for quantum servers to calculate encrypted data.
arXiv Detail & Related papers (2023-01-25T07:00:13Z) - QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
Self-Attention Mechanism (SAM) is good at capturing the internal connections of features.
A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
arXiv Detail & Related papers (2022-07-14T12:22:51Z) - Quantum circuit architecture search on a superconducting processor [56.04169357427682]
Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry.
However, the ansatz exploited in modern VQAs is incapable of balancing the tradeoff between expressivity and trainability.
We demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique to enhance VQAs on an 8-qubit superconducting quantum processor.
arXiv Detail & Related papers (2022-01-04T01:53:42Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - Analysis of five techniques for the internal representation of a digital
image inside a quantum processor [0.0]
Five techniques for the representation of a digital image inside a quantum processor are compared.
The paper will be based on implementations on the Quirk simulator, and on the IBM Q Experience processors.
arXiv Detail & Related papers (2020-08-03T14:06:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.