Quantum-Circuit-Based Visual Fractal Image Generation in Qiskit and Analytics
- URL: http://arxiv.org/abs/2508.18835v1
- Date: Tue, 26 Aug 2025 09:14:19 GMT
- Title: Quantum-Circuit-Based Visual Fractal Image Generation in Qiskit and Analytics
- Authors: Hillol Biswas,
- Abstract summary: In Quantum systems, the probability density or wavefunction may exhibit recurring interference patterns at various energy or length scales.<n>This paper outlines the generation of a Julia set dataset using an approach coupled with building quantum circuit.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As nature is ascribed as quantum, the fractals also pose some intriguing appearance which is found in many micro and macro observable entities or phenomena. Fractals show self-similarity across sizes; structures that resemble the entire are revealed when zoomed in. In Quantum systems, the probability density or wavefunction may exhibit recurring interference patterns at various energy or length scales. Fractals are produced by basic iterative rules (such as Mandelbrot or Julia sets), and they provide limitless complexity. Despite its simplicity, the Schr\"odinger equation in quantum mechanics produces incredibly intricate patterns of interference and entanglement, particularly in chaotic quantum systems. Quantum computing, the root where lies to the using the principles of quantum-mechanical phenomenon, when applied in fractal image generation, what outcomes are expected? The paper outlines the generation of a Julia set dataset using an approach coupled with building quantum circuit, highlighting the concepts of superposition, randomness, and entanglement as foundational elements to manipulate the generated dataset patterns. As Quantum computing is finding many application areas, the possibility of using quantum circuits for fractal Julia image generation posits a unique direction of future research where it can be applied to quantum generative arts across various ecosystems with a customised approach, such as producing an exciting landscape based on a quantum art theme.
Related papers
- Efficient Quantum Pseudorandomness from Hamiltonian Phase States [41.94295877935867]
We introduce a quantum hardness assumption called the Hamiltonian Phase State (HPS) problem.<n>We show that our assumption is plausibly fully quantum; meaning, it cannot be used to construct one-way functions.<n>We show that our assumption and its variants allow us to efficiently construct many pseudorandom quantum primitives.
arXiv Detail & Related papers (2024-10-10T16:10:10Z) - Quantum Wave Function Collapse for Procedural Content Generation [0.0]
Quantum computers exhibit an inherent randomness, so it seems natural to consider them for procedural content generation.
This quantum wave function collapse algorithm is based on the idea that a quantum circuit can be prepared in such a way that it acts as a special-purpose random generator for content of a desired form.
arXiv Detail & Related papers (2023-12-21T13:50:53Z) - Hidden tensor structures [0.0]
A single quantum mechanical system is automatically equipped with infinitely many hidden tensor-like structures.
These hidden structures are at the roots of some well known theoretical constructions.
The discussed structures explain why it is possible to emulate a quantum computer by classical analog circuit devices.
arXiv Detail & Related papers (2023-08-08T12:08:15Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Towards Bundle Adjustment for Satellite Imaging via Quantum Machine
Learning [2.660348668799655]
We focus on quantum methods for keypoint extraction and feature matching.
It is explained how these methods can be re-formulated for quantum annealers and gate-based quantum computers.
arXiv Detail & Related papers (2022-04-23T19:33:14Z) - Quantum simulation using noisy unitary circuits and measurements [0.0]
Noisy quantum circuits have become an important cornerstone of our understanding of quantum many-body dynamics.
We give an overview of two classes of dynamics studied using random-circuit models, with a particular focus on the dynamics of quantum entanglement.
We consider random-circuit sampling experiments and discuss the usefulness of random quantum states for simulating quantum many-body dynamics on NISQ devices.
arXiv Detail & Related papers (2021-12-13T14:00:06Z) - Variational Quantum Anomaly Detection: Unsupervised mapping of phase
diagrams on a physical quantum computer [0.0]
We propose variational quantum anomaly detection, an unsupervised quantum machine learning algorithm to analyze quantum data from quantum simulation.
The algorithm is used to extract the phase diagram of a system with no prior physical knowledge.
We show that it can be used with readily accessible devices nowadays and perform the algorithm on a real quantum computer.
arXiv Detail & Related papers (2021-06-15T06:54:47Z) - The Hintons in your Neural Network: a Quantum Field Theory View of Deep
Learning [84.33745072274942]
We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles.
On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
arXiv Detail & Related papers (2021-03-08T17:24:29Z) - Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems.
We propose an algorithm to implement imaginary time propagation on a quantum computer.
arXiv Detail & Related papers (2021-02-24T12:48:00Z) - Experimental Quantum Generative Adversarial Networks for Image
Generation [93.06926114985761]
We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
arXiv Detail & Related papers (2020-10-13T06:57:17Z) - Quantum Hall phase emerging in an array of atoms interacting with
photons [101.18253437732933]
Topological quantum phases underpin many concepts of modern physics.
Here, we reveal that the quantum Hall phase with topological edge states, spectral Landau levels and Hofstadter butterfly can emerge in a simple quantum system.
Such systems, arrays of two-level atoms (qubits) coupled to light being described by the classical Dicke model, have recently been realized in experiments with cold atoms and superconducting qubits.
arXiv Detail & Related papers (2020-03-18T14:56:39Z) - Quantum Random Number Generation using a Solid-State Single-Photon
Source [89.24951036534168]
Quantum random number generation (QRNG) harnesses the intrinsic randomness of quantum mechanical phenomena.
We demonstrate QRNG with a quantum emitter in hexagonal boron nitride.
Our results open a new avenue to the fabrication of on-chip deterministic random number generators.
arXiv Detail & Related papers (2020-01-28T22:47:43Z)
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.