Refik Anadol

Landscapes — 00001011101110100011111011011101100001111010110110100

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0x0006969

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Landscapes — 01010111010010110010010001110111010100000100001011000

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Fractamonic

X_Men

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Refik Anadol created the Quantum AI Data Paintings using quantum bit strings generated from Google Quantum AI’s Beyond Classical experiment. These data sets were processed through a Generative Adversarial Network (GAN) machine learning algorithm, originally developed during the production of Quantum Memories.

The dataset comprises 200 million raw images of landscapes from around the world, including all U.S. national parks.

By incorporating custom procedural coherent noise implementations and Beyond Classical measurements of computing surflets, 1,000 unique Quantum AI Data Paintings were generated, each computed with a distinct quantum bit string.

These images blend organic pigments, natural forms, and familiar patterns, evoking sensory experiences while paying tribute to the Earth’s poetic vastness and sublime beauty. This fusion of quantum data, artificial intelligence, and natural aesthetics represents a pioneering exploration at the intersection of advanced computing and artistic expression.

Quantum Bit Strings

A quantum bit string (or qubit string) refers to sequences of data generated using quantum computing principles. Unlike traditional binary bits (0s and 1s), qubits can exist in multiple states simultaneously due to quantum superposition, meaning they can hold more complex data representations. These qubits were extracted from Google's quantum experiment and serve as the foundational data input for Anadol’s artwork.

Google Quantum AI’s Beyond Classical Experiment

Google’s Beyond Classical experiment was a groundbreaking test demonstrating quantum supremacy, where a quantum computer performed a calculation that a classical supercomputer would take thousands of years to complete. This experiment provided quantum-generated data, which was then transformed into visual compositions in Quantum AI Data Paintings.

Generative Adversarial Network (GAN)

A Generative Adversarial Network (GAN) is a type of artificial intelligence (AI) that generates new, unique data by learning from a dataset. GANs work by pitting two neural networks against each other—one that creates images (the generator) and another that evaluates their accuracy (the discriminator). Through this process, GANs refine their outputs, making them more realistic.