Circuit compression for 2D quantum dynamics

TL;DR

This paper introduces a method to compress quantum circuits simulating 2D quantum systems, enabling longer simulations with fewer resources and higher accuracy, validated on a real quantum processor.

Contribution

The authors develop a Pauli propagation-based circuit compression technique for large 2D quantum systems, surpassing standard methods in accuracy at the same circuit depth.

Findings

Successfully compressed 30x30 qubit system dynamics.

Achieved higher fidelity than Trotterization methods.

Validated on Quantinuum's H1 quantum processor.

Abstract

The study of out-of-equilibrium quantum many-body dynamics remains one of the most exciting research frontiers of physics, standing at the crossroads of our understanding of complex quantum phenomena and the realization of quantum advantage. Quantum algorithms for the dynamics of quantum systems typically require deep quantum circuits whose accuracy is compromised by noise and imperfections in near-term hardware. Thus, reducing the depth of such quantum circuits to shallower ones while retaining high accuracy is critical for quantum simulation. Variational quantum compilation methods offer a promising path forward, yet a core difficulty persists: ensuring that a variational ansatz $V$ faithfully approximates a target unitary $U$. Here we leverage Pauli propagation techniques to develop a strategy for compressing circuits that implement the dynamics of large two-dimensional (2D) quantum systems and beyond. As a concrete demonstration, we compress the dynamics of systems up to $30 \times 30$ qubits and achieve accuracies that surpass standard Trotterization methods by orders of magnitude at identical circuit depths. To experimentally validate our approach, we execute the compiled ansatz on Quantinuum's H1 quantum processor and observe that it tracks the system's dynamics with significantly higher fidelity than Trotterized circuits without optimization. Our circuit compression scheme brings us one step closer to a practical quantum advantage by allowing longer simulation times at reduced quantum resources and unlocks the exploration of large families of hardware-friendly ans\"atze.