Quantum process tomography of structured optical gates with convolutional neural networks
arXiv:2402.16616 · doi:10.1088/2632-2153/ad9ba8
Abstract
The characterization of a unitary gate is experimentally accomplished via Quantum Process Tomography, which combines the outcomes of different projective measurements to reconstruct the underlying operator. The process matrix is typically extracted from maximum-likelihood estimation. Recently, optimization strategies based on evolutionary and machine-learning techniques have been proposed. Here, we investigate a deep-learning approach that allows for fast and accurate reconstructions of space-dependent SU(2) operators, only processing a minimal set of measurements. We train a convolutional neural network based on a scalable U-Net architecture to process entire experimental images in parallel. Synthetic processes are reconstructed with average fidelity above 90%. The performance of our routine is experimentally validated on complex polarization transformations. Our approach further expands the toolbox of data-driven approaches to Quantum Process Tomography and shows promise in the real-time characterization of complex optical gates.
References in corpus (31)
- Quantum computational advantage using photons
- On the Measurement of Qubits
- Optical spin-to-orbital angular momentum conversion in inhomogeneous anisotropic media
- Prescription for experimental determination of the dynamics of a quantum black box
- Quantum process tomography of a controlled-NOT gate
- Experimental reconstruction of the Berry curvature in a topological Bloch band
- Ancilla-assisted quantum process tomography
- Deep learning approach to Fourier ptychographic microscopy
- On the use of deep learning for phase recovery
- Realization of quantum process tomography in NMR
- Measuring topology by dynamics: Chern number from linking number
- Process tomography of ion trap quantum gates
- Complete Characterization of Quantum-Optical Processes
- Photon spin-to-orbital angular momentum conversion via an electrically tunable -plate
- Diagnosis, prescription and prognosis of a Bell-state filter by quantum process tomography
- Direct characterization of linear-optical networks
- Two-dimensional topological quantum walks in the momentum space of structured light
- Efficient quantum state tomography with convolutional neural networks
- Experimental quantum process tomography of non trace-preserving maps
- Inverse-design of high-dimensional quantum optical circuits in a complex medium
- Maximum-likelihood estimation prevents unphysical Mueller matrices
- Extracting the Quantum Geometric Tensor of an Optical Raman Lattice by Bloch State Tomography
- Ultra-long quantum walks via spin-orbit photonics
- Quantum-enhanced tomography of unitary processes
- General Lossless Spatial Polarization Transformations
- Tomography of optical polarization rotation induced by a quantum dot-cavity device
- Quantum process tomography of a high-dimensional quantum communication channel
- Adaptive Quantum State Tomography with Active Learning
- Quantum process tomography of linear and quadratically nonlinear optical systems
- Fourier Quantum Process Tomography
- Retrieving space-dependent polarization transformations via near-optimal quantum process tomography