Hybrid quantum-classical reservoir computing of thermal convection flow

TL;DR

This paper demonstrates that a small number of entangled qubits in a hybrid quantum-classical reservoir computing model can effectively simulate complex chaotic thermal convection flows, matching classical models' capabilities.

Contribution

It introduces a hybrid quantum-classical reservoir computing approach that uses few entangled qubits to replicate classical chaotic dynamics, showing quantum advantage in efficiency.

Findings

Quantum reservoir with few qubits matches classical reservoir performance.

Entanglement enhances the prediction accuracy of the quantum reservoir.

Successful implementation on a noisy IBM quantum computer with up to 7 qubits.

Abstract

We simulate the nonlinear chaotic dynamics of Lorenz-type models for a classical two-dimensional thermal convection flow with 3 and 8 degrees of freedom by a hybrid quantum--classical reservoir computing model. The high-dimensional quantum reservoir dynamics are established by universal quantum gates that rotate and entangle the individual qubits of the tensor product quantum state. A comparison of the quantum reservoir computing model with its classical counterpart shows that the same prediction and reconstruction capabilities of classical reservoirs with thousands of perceptrons can be obtained by a few strongly entangled qubits. We demonstrate that the mean squared error between model output and ground truth in the test phase of the quantum reservoir computing algorithm increases when the reservoir is decomposed into separable subsets of qubits. Furthermore, the quantum reservoir computing model is implemented on a real noisy IBM quantum computer for up to 7 qubits. Our work thus opens the door to model the dynamics of classical complex systems in a high-dimensional phase space effectively with an algorithm that requires a small number of qubits.