Expressive “Quantum Art”

Amber Scoon

Paul Thomas

The article explores parallels between quantum evolution and developments taking place in the fine arts. Chance, uncertainty, probability, and intuition are all hallmarks of expression across the fields of both art and scientific practice. The role of the observer in art and science is explored through a comparative study between the act of fine art drawing and quantum measurement. What is meant by the role of the observer to the very act of seeing. The observer draws away a veil of inadequacy in looking for reality. The article reflects on the process of drawing’s comparative relationship the role of the observer in art and science. This role of the observer is analysed in relation to delayed choice quantum erasure, probability, indeterminacy, counterfactual definitiveness, entanglement, superposition, and the classical quantum divide, through the artist’s act of mark-making. There is a need for an attentive gaze when confronting so-called truths. Intentional repositioning and reframing of science practices through art can promote exploration of different ways of visualising, perceiving, understanding, communicating, and acting in the material world. In so doing the article describes a “quantum drawing” workshop created for art and science practitioners. The workshop is a new model for facilitating a transdisciplinary development of alternative domains and discourses that garner insights from the act of seeing and understanding. The workshop explores visual expression, material interaction and embodied practices speculating on quantum intuition, quantum computing and infinite speed. The article defines the quantisation of drawing as a tool for unpacking the role of the observer in art and science to embrace uncertainty.

A. Goswami

Sergio Sanchez, Jofre Vallès-Muns, Artur García-Sáez

Tensor Networks are graph representations of summation expressions in which vertices represent tensors and edges represent tensor indices or vector spaces. In this work, we present EinExprs.jl, a Julia package for contraction path optimization that offers state-of-art optimizers. We propose a representation of the contraction path of a Tensor Network based on symbolic expressions. Using this package the user may choose among a collection of different methods such as Greedy algorithms, or an approach based on the hypergraph partitioning problem. We benchmark this library with examples obtained from the simulation of Random Quantum Circuits (RQC), a well known example where Tensor Networks provide state-of-the-art methods.

Davis Callaway

Karthikeyan Rengasamy, Piyush Joshi, V.V.S. Raveendra

Abstract Facial expressions bring human interactions to life with nonverbal cues that convey hues of emotions, feelings, and cultural intent. Analyzing these expressions is essential in the age of digital transformation. Traditional approaches to real-time facial expression analysis have limitations in capturing the complexity of these expressions and consume enormous computational power. The promising computing capacity of quantum computers is poised to solve current problems and set new standards by meeting growing demands. We have created a hybrid model that leverages the capabilities of the classical and quantum domains. The proposed model uses a quantum distance-based classifier along with classical artificial neural networks to perform real-time facial expression analysis and classification. Based on a thorough review of the literature, this novel and comprehensive work is, to the best of our knowledge, one of the few that addresses eight different emotions in the quantum domain. The implementation of a novel quantum error correction method has improved the accuracy of this hybrid model. Our model was trained on the CK+ facial expression database. To ensure fairness of the study, we tested our model on the largest facial expression database, AffectNet-8, and compared the performance with state-of-the-art models. Our model accuracy is 10.83% higher than that of the state-of-the-art models. From a holistic perspective, our proposed novel hybrid model appears to have universal value for all kinds of real-time image analysis and classification problems. As we move forward, we planned to focus on quantum neural networks for image processing and facial expression analysis.