Combining Matrix Product States and Noisy Quantum Computers for Quantum Simulation

Abstract

Matrix Product States (MPS) and Operators (MPO) have been proven to be a powerful tool to study quantum many-body systems but are restricted to moderately entangled states as the number of parameters scales exponentially with the entanglement entropy. While MPS can efficiently find ground states of 1D systems, their capacities are limited when simulating their dynamics, where the entanglement can increase ballistically with time. On the other hand, quantum devices appear as a natural platform to encode and perform the time evolution of correlated many-body states. However, accessing the regime of long-time dynamics is hampered by quantum noise. In this study we use the best of worlds: the short-time dynamics is efficiently performed by MPSs, compiled into short-depth quantum circuits, and is performed further in time on a quantum computer thanks to efficient MPO-optimized quantum circuits. We quantify the capacities of this hybrid classical-quantum scheme in terms of fidelities taking into account a noise model. We show that using classical knowledge in the form of tensor networks provides a way to better use limited quantum resources and lowers drastically the noise requirements to reach a practical quantum advantage. Finally we successfully demonstrate our approach with an experimental realization of the technique. Combined with efficient circuit transpilation we simulate a 10-qubit system on an actual quantum device over a longer time scale than low-bond-dimension MPSs and purely quantum Trotter evolution.

References in corpus (6)

• The density-matrix renormalization group in the age of matrix product states
• Matrix Product Density Operators: Simulation of finite-T and dissipative systems
• Efficient tensor network simulation of IBM's Eagle kicked Ising experiment
• Fast and converged classical simulations of evidence for the utility of quantum computing before fault tolerance
• Efficient tensor network simulation of IBM's largest quantum processors
• Mitigating crosstalk errors by randomized compiling: Simulation of the BCS model on a superconducting quantum computer

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