Quantum-inspired Hash Function Based on Parity-dependent Quantum Walks
with Memory
- URL: http://arxiv.org/abs/2308.05357v1
- Date: Thu, 10 Aug 2023 05:54:32 GMT
- Title: Quantum-inspired Hash Function Based on Parity-dependent Quantum Walks
with Memory
- Authors: Qing Zhou, Xueming Tang, Songfeng Lu, Hao Yang
- Abstract summary: We construct a quantum-inspired hash function (called QHFM-P) based on this model.
Numerical simulation shows that QHFM-P has near-ideal statistical performance.
Stability test illustrates that the statistical properties of the proposed hash function are robust with respect to the coin parameters.
- Score: 25.487508611436425
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we develop a generic controlled alternate quantum walk model
(called CQWM-P) by combining parity-dependent quantum walks with distinct
arbitrary memory lengths and then construct a quantum-inspired hash function
(called QHFM-P) based on this model. Numerical simulation shows that QHFM-P has
near-ideal statistical performance and is on a par with the state-of-the-art
hash functions based on discrete quantum walks in terms of sensitivity of hash
value to message, diffusion and confusion properties, uniform distribution
property, and collision resistance property. Stability test illustrates that
the statistical properties of the proposed hash function are robust with
respect to the coin parameters, and theoretical analysis indicates that QHFM-P
has the same computational complexity as that of its peers.
Related papers
- Reducing the sampling complexity of energy estimation in quantum many-body systems using empirical variance information [45.18582668677648]
We consider the problem of estimating the energy of a quantum state preparation for a given Hamiltonian in Pauli decomposition.
We construct an adaptive estimator using the state's actual variance.
arXiv Detail & Related papers (2025-02-03T19:00:01Z) - Quantum Latent Diffusion Models [65.16624577812436]
We propose a potential version of a quantum diffusion model that leverages the established idea of classical latent diffusion models.
This involves using a traditional autoencoder to reduce images, followed by operations with variational circuits in the latent space.
The results demonstrate an advantage in using a quantum version, as evidenced by obtaining better metrics for the images generated by the quantum version.
arXiv Detail & Related papers (2025-01-19T21:24:02Z) - Quantum Computing for Partition Function Estimation of a Markov Random Field in a Radar Anomaly Detection Problem [0.0]
In probability theory, the partition function is a factor used to reduce any probability function to a density function with total probability of one.
We propose a quantum algorithm for partition function estimation in the one clean qubit model.
arXiv Detail & Related papers (2025-01-02T09:14:14Z) - Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML)
At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
arXiv Detail & Related papers (2024-11-13T23:56:20Z) - Importance sampling for stochastic quantum simulations [68.8204255655161]
We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients.
We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage.
Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
arXiv Detail & Related papers (2022-12-12T15:06:32Z) - 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) - On Quantum Circuits for Discrete Graphical Models [1.0965065178451106]
We provide the first method that allows one to provably generate unbiased and independent samples from general discrete factor models.
Our method is compatible with multi-body interactions and its success probability does not depend on the number of variables.
Experiments with quantum simulation as well as actual quantum hardware show that our method can carry out sampling and parameter learning on quantum computers.
arXiv Detail & Related papers (2022-06-01T11:03:51Z) - Quantum Local Differential Privacy and Quantum Statistical Query Model [0.7673339435080445]
Quantum statistical queries provide a theoretical framework for investigating the computational power of a learner with limited quantum resources.
In this work, we establish an equivalence between quantum statistical queries and quantum differential privacy in the local model.
We consider the task of quantum multi-party computation under local differential privacy.
arXiv Detail & Related papers (2022-03-07T18:38:02Z) - Numerical Simulations of Noisy Quantum Circuits for Computational
Chemistry [51.827942608832025]
Near-term quantum computers can calculate the ground-state properties of small molecules.
We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
arXiv Detail & Related papers (2021-12-31T16:33:10Z) - Hash function based on controlled alternate quantum walks with memory [16.247079214644796]
We propose a new hash function QHFM based on controlled alternate quantum walks with memory on cycles.
The proposed hash function has near-ideal statistical performance and is at least on a par with the state-of-the-art hash functions based on quantum walks.
arXiv Detail & Related papers (2021-05-31T08:30:08Z) - Parametric Probabilistic Quantum Memory [1.412197703754359]
Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary pattern stored in superposition on the memory.
In this work, we propose an improved parametric version of the PQM to perform pattern classification.
We also present a PQM quantum circuit suitable for Noisy Intermediate Scale Quantum (NISQ) computers.
arXiv Detail & Related papers (2020-01-11T11:41:05Z)
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.