Mitigating many-body quantum crosstalk with tensor-network robust control

TL;DR

This paper introduces a tensor-network-based robust control method to suppress many-body quantum crosstalk, significantly improving fidelity in large-scale quantum operations and state preparations.

Contribution

It develops a scalable control technique combining tensor networks and the GRAPE algorithm to mitigate quantum crosstalk in large qubit systems.

Findings

Achieved high-fidelity parallel gates on 50 qubits

Realized a 30-qubit GHZ state with improved fidelity

Demonstrated order-of-magnitude fidelity enhancement in large systems

Abstract

Quantum crosstalk poses a major challenge to scaling up quantum computations as its strength is typically unknown and its effect accumulates exponentially as system size grows. Here, we show that many-body robust control can be utilized to suppress unwanted couplings during multi-qubit gate operations and state preparation. By combining tensor network simulations with the GRAPE algorithm, and leveraging an efficient random sampling over noise ensembles, our method overcomes the exponential scaling of the Hilbert space. We demonstrate its effectiveness for designing control solutions for high-fidelity implementations of parallel X gates and parallel CNOT on a chain of 50 qubits, and for realizing a 30-qubit GHZ state and the ground state of a 20-qubit Heisenberg model. In the presence of many-body quantum crosstalk due to parasitic interaction between neighboring qubits, robust control results in order-of magnitude improvement in fidelity for large system sizes. These findings pave the way for more reliable operations on near-term quantum processors.