Experimental sample-efficient quantum state tomography via parallel measurements
arXiv:2409.12614 · doi:10.1103/PhysRevLett.133.160801
Abstract
Quantum state tomography (QST) via local measurements on reduced density matrices (LQST) is a promising approach but becomes impractical for large systems. To tackle this challenge, we developed an efficient quantum state tomography method inspired by quantum overlapping tomography [Phys. Rev. Lett. 124, 100401(2020)], which utilizes parallel measurements (PQST). In contrast to LQST, PQST significantly reduces the number of measurements and offers more robustness against shot noise. Experimentally, we demonstrate the feasibility of PQST in a tree-like superconducting qubit chip by designing high-efficiency circuits, preparing W states, ground states of Hamiltonians and random states, and then reconstructing these density matrices using full quantum state tomography (FQST), LQST, and PQST. Our results show that PQST reduces measurement cost, achieving fidelities of 98.68\% and 95.07\% after measuring 75 and 99 observables for 6-qubit and 9-qubit W states, respectively. Furthermore, the reconstruction of the largest density matrix of the 12-qubit W state is achieved with the similarity of 89.23\% after just measuring parallel observables, while complete observables are needed for FQST. Consequently, PQST will be a useful tool for future tasks such as the reconstruction, characterization, benchmarking, and properties learning of states.
To appear in PRL(2024)
References in corpus (9)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- The density-matrix renormalization group in the age of matrix product states
- Experimental Quantum State Tomography of Optical Fields and Ultrafast Statistical Sampling
- Efficient quantum state tomography
- Direct Fidelity Estimation from Few Pauli Measurements
- A Race Track Trapped-Ion Quantum Processor
- Efficient tensor network simulation of IBM's largest quantum processors
- Improved machine learning algorithm for predicting ground state properties
- N-Qubit W States are Determined by their Bipartite Marginals