Quantum Ciphertext Dimension Reduction Scheme for Homomorphic Encrypted
Data
- URL: http://arxiv.org/abs/2011.09692v2
- Date: Tue, 24 Nov 2020 07:25:57 GMT
- Title: Quantum Ciphertext Dimension Reduction Scheme for Homomorphic Encrypted
Data
- Authors: Changqing Gong and Zhaoyang Dong and Abdullah Gani and Han Qi
- Abstract summary: Proposed quantum principal component extraction algorithm (QPCE)
A quantum homomorphic ciphertext dimension reduction scheme (QHEDR)
A quantum ciphertext dimensionality reduction scheme implemented in the quantum cloud.
- Score: 4.825895794318393
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: At present, in the face of the huge and complex data in cloud computing, the
parallel computing ability of quantum computing is particularly important.
Quantum principal component analysis algorithm is used as a method of quantum
state tomography. We perform feature extraction on the eigenvalue matrix of the
density matrix after feature decomposition to achieve dimensionality reduction,
proposed quantum principal component extraction algorithm (QPCE). Compared with
the classic algorithm, this algorithm achieves an exponential speedup under
certain conditions. The specific realization of the quantum circuit is given.
And considering the limited computing power of the client, we propose a quantum
homomorphic ciphertext dimension reduction scheme (QHEDR), the client can
encrypt the quantum data and upload it to the cloud for computing. And through
the quantum homomorphic encryption scheme to ensure security. After the
calculation is completed, the client updates the key locally and decrypts the
ciphertext result. We have implemented a quantum ciphertext dimensionality
reduction scheme implemented in the quantum cloud, which does not require
interaction and ensures safety. In addition, we have carried out experimental
verification on the QPCE algorithm on IBM's real computing platform, and given
a simple example of executing hybrid quantum circuits in the cloud to verify
the correctness of our scheme. Experimental results show that the algorithm can
perform ciphertext dimension reduction safely and effectively.
Related papers
- Quantum delegated and federated learning via quantum homomorphic encryption [0.5939164722752263]
We present a general framework that enables quantum delegated and federated learning with atheoretical data privacy guarantee.
We show that learning and inference under this framework feature substantially lower communication complexity compared with schemes based on blind quantum computing.
arXiv Detail & Related papers (2024-09-28T14:13:50Z) - The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - Quantum Truncated Differential and Boomerang Attack [10.853582091917236]
In this article, we concentrate on truncated differential and boomerang cryptanalysis.
We first present a quantum algorithm which is designed for finding truncated differentials of symmetric ciphers.
We prove that, with a overwhelming probability, the truncated differentials output by our algorithm must have high differential probability for the vast majority of keys in key space.
arXiv Detail & Related papers (2024-07-21T11:34:29Z) - Quantum Resonant Dimensionality Reduction and Its Application in Quantum Machine Learning [2.7119354495508787]
We propose a quantum resonant dimension reduction (QRDR) algorithm based on the quantum resonant transition to reduce the dimension of input data.
After QRDR, the dimension of input data $N$ can be reduced into desired scale $R$, and the effective information of the original data will be preserved.
Our algorithm has the potential to be utilized in a variety of computing fields.
arXiv Detail & Related papers (2024-05-21T09:26:18Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - Designing Hash and Encryption Engines using Quantum Computing [2.348041867134616]
We explore quantum-based hash functions and encryption to fortify data security.
The integration of quantum and classical methods demonstrates potential in securing data in the era of quantum computing.
arXiv Detail & Related papers (2023-10-26T14:49:51Z) - Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem.
The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
arXiv Detail & Related papers (2023-04-18T11:57:15Z) - 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) - Iterative Qubits Management for Quantum Index Searching in a Hybrid
System [56.39703478198019]
IQuCS aims at index searching and counting in a quantum-classical hybrid system.
We implement IQuCS with Qiskit and conduct intensive experiments.
Results demonstrate that it reduces qubits consumption by up to 66.2%.
arXiv Detail & Related papers (2022-09-22T21:54:28Z) - Facial Expression Recognition on a Quantum Computer [68.8204255655161]
We show a possible solution to facial expression recognition using a quantum machine learning approach.
We define a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states.
arXiv Detail & Related papers (2021-02-09T13:48:00Z) - A practical quantum encryption protocol with varying encryption
configurations [0.0]
We propose a quantum encryption protocol that utilizes a quantum algorithm to create blocks oftext ciphers based on quantum states.
The main feature of our quantum encryption protocol is that the encryption configuration of each block is determined by the previous blocks.
arXiv Detail & Related papers (2021-01-22T20:09:03Z)
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.