Experimental demonstration of reconstructing quantum states with generative models
arXiv:2407.15102 · doi:10.1016/j.scib.2025.03.007
Abstract
Quantum state tomography, a process that reconstructs a quantum state from measurements on an ensemble of identically prepared copies, plays a crucial role in benchmarking quantum devices. However, brute-force approaches to quantum state tomography would become impractical for large systems, as the required resources scale exponentially with the system size. Here, we explore a machine learning approach and report an experimental demonstration of reconstructing quantum states based on neural network generative models with an array of programmable superconducting transmon qubits. In particular, we experimentally prepare the Greenberger-Horne-Zeilinger states and random states up to five qubits and demonstrate that the machine learning approach can efficiently reconstruct these states with the number of required experimental samples scaling linearly with system size. Our results experimentally showcase the intriguing potential for exploiting machine learning techniques in validating and characterizing complex quantum devices, offering a valuable guide for the future development of quantum technologies.
References in corpus (13)
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum sensing
- Quantum secret sharing
- The density-matrix renormalization group
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Strong quantum computational advantage using a superconducting quantum processor
- Logical quantum processor based on reconfigurable atom arrays
- Efficient quantum state tomography
- Quantum Entanglement in Neural Network States
- Observation of multi-component atomic Schrödinger cat states of up to 20 qubits
- Reconstructing quantum states with generative models
- Latent Space Purification via Neural Density Operators