Part of the Parallel Worlds collection, the first ever limited series of quantum-AI-generated NFT collectibles, generated using a real quantum computer.

Each of the 8 one-of-a-kind pieces in the Parallel Worlds collection represents a unique branch of the multiversal superposition of quantum field fluctuations which followed the Big Bang and seeded the macroscopic structures of matter and energy we see in our cosmos today.

Each piece was generated using independent samples from a quantum-neural-net-based quantum simulation of the early universe, run on an IBM quantum computer. They each represent a different possible collapsed outcome of the early quantum field fluctuations, i.e. each a separate branch of the Everettian multiversal wavefunction.

These were algorithmically generated using the algorithm pioneered in the very first Quantum AI based NFT (Everettian Vibrations), which can be considered world #0.

Don't miss your chance to own a piece of tech history.

Unlockable:

You will gain access to the full resolution (6040 x 6040) image, as well as a runnable notebook with the quantum neural network used to generate the Gaussian wavefunction samples.

Further technical details:

By leveraging our control over the multiverse via quantum computers, we can mimic the Everettian wavefunction collapse which occurred in the inflationary era of the universe following the Big Bang. The collapse of this wavefunction usually occurs due to mode freezing from the rapid expansion of the fabric of spacetime, the frozen field configuration then acts as the anisotropic seed for matter density; pockets of matter which later get clumped together by gravity into the galactic superclusters we see and live in today. Here instead of having mode freezing pick a branch of the Everettian wavefunction, we use the measurement of a carefully-prepared superpositon generated by a quantum computer to determine the shape and configuration of the simulated density fluctuations amidst the multiverse of possibilities.

To achieve this simulation, a set of quantum neural networks were trained to output Gaussian wavefunctions. The trained networks were subsequently run on an IBM quantum computer, the resulting samples were then used as the amplitudes for the quantum field Fourier eigenmodes. Finally, this collection of sampled amplitudes were transformed to the spatial functional basis to generate the spatial field configuration visualization depicted here.