Spiking neuromorphic chip learns entangled quantum states
This work provides an energy-efficient platform for quantum simulations, which could benefit researchers in quantum computing and neuromorphic engineering, though it is incremental as it builds on existing neuromorphic hardware for a new application.
The authors tackled the problem of simulating quantum systems by using a spiking neuromorphic chip to represent maximally entangled quantum states, achieving high fidelities and accurately capturing Bell correlations for both pure and mixed two-qubit states.
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, analog neuromorphic chips, inspired by structural and dynamical properties of the biological brain, show a high energy efficiency in running artificial neural-network architectures for the profit of generative applications. This encourages employing such hardware systems as platforms for simulations of quantum systems. Here we report on the realization of a prototype using the latest spike-based BrainScaleS hardware allowing us to represent few-qubit maximally entangled quantum states with high fidelities. Bell correlations of pure and mixed two-qubit states are well captured by the analog hardware, demonstrating an important building block for simulating quantum systems with spiking neuromorphic chips.