We aim to apply a quantum computing technique to compose artworks. The main idea is to revisit three paintings of different styles and historical periods: ''Narciso'', painted circa 1597-1599 by Michelangelo Merisi (Caravaggio), ''Les fils de l'homme'', painted in 1964 by Rene Magritte and ''192 Farben'', painted in 1966 by Gerard Richter. We utilize the output of a quantum computation to change the composition in the paintings, leading to a paintings series titled ''Quantum Transformation I, II, III''. In particular, the figures are discretized into square lattices and the order of the pieces is changed according to the result of the quantum simulation. We consider an Ising Hamiltonian as the observable in the quantum computation and its time evolution as the final outcome. From a classical subject to abstract forms, we seek to combine classical and quantum aesthetics through these three art pieces. Besides experimenting with hardware runs and circuit noise, our goal is to reproduce these works as physical oil paintings on wooden panels. With this process, we complete a full circle between classical and quantum techniques and contribute to rethinking Art practice in the era of quantum computing technologies.
We aim to apply a quantum computing technique to compose artworks. The main idea is to revisit three paintings of different styles and historical periods: ''Narciso'', painted circa 1597-1599 by Michelangelo Merisi (Caravaggio), ''Les fils de l'homme'', painted in 1964 by Rene Magritte and ''192 Farben'', painted in 1966 by Gerard Richter. We utilize the output of a quantum computation to change the composition in the paintings, leading to a paintings series titled ''Quantum Transformation I, II, III''. In particular, the figures are discretized into square lattices and the order of the pieces is changed according to the result of the quantum simulation. We consider an Ising Hamiltonian as the observable in the quantum computation and its time evolution as the final outcome. From a classical subject to abstract forms, we seek to combine classical and quantum aesthetics through these three art pieces. Besides experimenting with hardware runs and circuit noise, our goal is to reproduce these works as physical oil paintings on wooden panels. With this process, we complete a full circle between classical and quantum techniques and contribute to rethinking Art practice in the era of quantum computing technologies.
Quantum computing is a revolutionary approach to computation that leverages the principles of quantum mechanics, enabling computers to process information in fundamentally different ways than classical computers. Unlike traditional bits, which represent data as either 0s or 1s, quantum bits, or qubits, can exist in multiple states simultaneously due to superposition. Additionally, qubits can be entangled, allowing them to be interconnected in ways that enhance computational power and efficiency. This unique behavior has the potential to run certain types of computation tasks much faster than classical computers can, marking a significant leap forward in technology. When building actual quantum computers to run practical quantum algorithms, one needs efficient ways to map the algorithms to the hardware components, similar to compiling classical algorithms onto CPU/GPU hardware. This thesis aims to tackle some of the important problems in the mapping process. First, can we address hardware errors and algorithm errors at the same time? Second, can we build a better circuit complexity metric to inspire better quantum architecture design, and can we design better quantum algorithms and compilation techniques using this improved metric? Lastly, can we use physics-level simulation results to inspire better circuit-level optimization techniques? We hope to provide positive answers to all three questions above through three separate architectural innovations.
Developing state-of-the-art classical simulators of quantum circuits is of utmost importance to test and evaluate early quantum technology and understand the true potential of full-blown error-corrected quantum computers. In the past few years, multiple theoretical and numerical advances have continuously pushed the boundary of what is classically simulable, hence the development of a plethora of tools which are often limited to a specific purpose or designed for a particular hardware (e.g. CPUs vs. GPUs). Moreover, such tools are typically developed using tailored languages and syntax, which makes it hard to compare results from, and create hybrid approaches using, different simulation techniques. To support unified and optimized use of these techniques across platforms, we developed HybridQ , a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to run on a variety of hardware. The philosophy behind its development has been driven by three main pillars: Easy to Use, Easy to Extend, and Use the Best Available Technology. The powerful tools of HybridQ allow users to manipulate, develop, and extend noiseless and noisy circuits for different hardware architectures. HybridQ supports large-scale high-performance computing (HPC) simulations, automatically balancing workload among different processor nodes and enabling the use of multiple backends to maximize parallel efficiency. Everything is then glued together by a simple and expressive language that allows seamless switching from one technique to another as well as from one hardware to the next, without the need to write lengthy translations, thus greatly simplifying the development of new hybrid algorithms and techniques.
Abstract The continuous progress of the network and big data has brought far-reaching influence and potential opportunities to traditional painting design, and at the same time, it continues to shape the content and form of its artistic style. This paper employs the quantum image enhancement algorithm to transform the traditional painting image into a quantum pure state. Subsequently, it segments each pixel to extract edge information, thereby constructing the traditional painting image state. Next, we employ the quantum image color enhancement algorithm to amplify the color aesthetics of the extracted image state, thereby fostering innovation. Finally, this paper proposes an innovative approach to traditional painting styles using quantum information technology. This paper proposes a quantum image enhancement algorithm that outperforms the classical program in all quality indicators after processing traditional painting images, thereby establishing a theoretical foundation for future research. The study found that traditional paintings made with the help of a quantum image enhancement algorithm have a higher quality of art style innovation and artistic value. The study also found that both types of works, plants (4.37 points) and myths (4.50 points), got higher scores on the tests. This paper brings a fresh perspective to the creation of traditional painting art, paving the way for a more intelligent and diverse future for this genre.
Art is often a question of representation. And representation of life is at the heart of artistic creation. If one dives into very small size scales (beyond the famous Planck constant), we can work on the representation of life at the atomic scale. As everyone knows, at this scale, it is not the principles of classical physics that apply, but those of quantum mechanics. If one is interested in living in this type of dimension, he is faced with a population of protons, photons, electrons and other particles, which form a strange ecosystem, and whose quantum behavior is difficult to understand. The behavior of the elementary particles of our life defies our usual sense of space and time. Also, these particles form a living world in a higher number of dimensions. There are at least four dimensions, and probably more. [1]. As a digital and generative artist, it is quite exciting to be interested in this type of representation, and the use of a computer helps us in this way. Quantum Computers are not yet available, but many simulation tools exist and allow us to make quantum calculations that are no longer based on the classic use of bits, but are instead based on qubits.
The use of quantum computing in the creation of art is proving to be very interesting since it allows both the exploration of digital work using new algorithms and of artistic creation based on new concepts. These new algorithms mainly rely on the use of qubits instead of bits to perform simple or complex operations, which are applied to the components of digital works: namely the pixels in an image, the frames of an animation, the vertices of a 3D object, the words of a text or the notes of a musical score. This article reviews the use of quantum algorithms, using various examples and particularly the new methods being applied in computer graphics.
As customer demands become increasingly diverse, the colors and styles of vehicles offered by automotive companies have also grown substantially. It poses great challenges to design and management of automotive manufacturing system, among which is the proper sequencing of vehicles in everyday operation of the paint shop. With typically hundreds of vehicles in one shift, the paint shop sequencing problem is intractable in classical computing. In this paper, we propose to solve a general paint shop sequencing problem using state-of-the-art quantum computing algorithms. Most existing works are solely focused on reducing color changeover costs, i.e., costs incurred by different colors between consecutive vehicles. This work reveals that different sequencing of vehicles also significantly affects the quality performance of the painting process. We use a machine learning model pretrained on historical data to predict the probability of painting defect. The problem is formulated as a combinational optimization problem with two cost components, i.e., color changeover cost and repair cost. The problem is further converted to a quantum optimization problem and solved with Quantum Approximation Optimization Algorithm (QAOA). As a matter of fact, current quantum computers are still limited in accuracy and scalability. However, with a simplified case study, we demonstrate how the classic sequencing problem in paint shop can be formulated and solved using quantum computing and demonstrate the potential of quantum computing in solving real problems in manufacturing systems.