Efficient learning of mixed-state tomography for photonic quantum walk
arXiv:2411.03640 · doi:10.1126/sciadv.adl4871
Abstract
Noise-enhanced applications in open quantum walk (QW) have recently seen a surge due to their ability to improve performance. However, verifying the success of open QW is challenging, as mixed-state tomography is a resource-intensive process, and implementing all required measurements is almost impossible due to various physical constraints. To address this challenge, we present a neural-network-based method for reconstructing mixed states with a high fidelity (~97.5%) while costing only 50% of the number of measurements typically required for open discrete-time QW in one dimension. Our method uses a neural density operator that models the system and environment, followed by a generalized natural gradient descent procedure that significantly speeds up the training process. Moreover, we introduce a compact interferometric measurement device, improving the scalability of our photonic QW setup that enables experimental learning of mixed states. Our results demonstrate that highly expressive neural networks can serve as powerful alternatives to traditional state tomography.
References in corpus (60)
- Machine learning and the physical sciences
- On the Measurement of Qubits
- Quantum Walk in Position Space with Single Optically Trapped Atoms
- Photonic quantum information processing: a review
- Observation of topologically protected bound states in a one dimensional photonic system
- Quantum transport simulations in a programmable nanophotonic processor
- Decoherence and disorder in quantum walks: From ballistic spread to localization
- Efficient Representation of Quantum Many-body States with Deep Neural Networks
- Reconstructing quantum states with generative models
- Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
- Decoherence in quantum walks - a review
- Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems
- Variational neural network ansatz for steady states in open quantum systems
- Quantum Tomography
- Quantum walks and wavepacket dynamics on a lattice with twisted photons
- Photonic quantum walk in a single beam with twisted light
- The quantum to classical transition for random walks
- Experimental Machine Learning of Quantum States
- Learning Quantum Systems
- Observation of non-Hermitian topological Anderson insulator in quantum dynamics
- Machine learning-based classification of vector vortex beams
- Integrating Neural Networks with a Quantum Simulator for State Reconstruction
- Latent Space Purification via Neural Density Operators
- Universal unitary gate for single-photon 2-qubit states
- Fast Escape from Quantum Mazes in Integrated Photonics
- Experimental engineering of arbitrary qudit states with discrete-time quantum walks
- Quantum Neural Network States: A Brief Review of Methods and Applications
- Tweezer-programmable 2D quantum walks in a Hubbard-regime lattice
- Efficient quantum state tomography with convolutional neural networks
- Neural network quantum state tomography in a two-qubit experiment
- Measuring Topological Invariants in Disordered Discrete Time Quantum Walks
- Measuring a Dynamical Topological Order Parameter in Quantum Walks
- Indirect Quantum Tomography of Quadratic Hamiltonians
- Experimental quantum homodyne tomography via machine learning
- Measuring the winding number in a large-scale chiral quantum walk
- Driven quantum dynamics: will it blend?
- Neural-network quantum state tomography
- Optimal two-qubit tomography based on local and global measurements: Maximal robustness against errors as described by condition numbers
- Universally Optimal Noisy Quantum Walks on Complex Networks
- Quantum state engineering using one-dimensional discrete-time quantum walks
- Quantum walks on graphs representing the firing patterns of a quantum neural network
- Probing Measurement Induced Effects in Quantum Walks via Recurrence
- Experimental Simultaneous Learning of Multiple Non-Classical Correlations
- Dynamic-Disorder-Induced Enhancement of Entanglement in Photonic Quantum Walks
- Eigenstate extraction with neural-network tomography
- Ultra-long quantum walks via spin-orbit photonics
- Quantum walks of two correlated photons in a 2D synthetic lattice
- Experimental investigation of superdiffusion via coherent disordered Quantum Walks
- Experimental demonstration of quantum walks with initial superposition states
- Quantum Reinforcement Learning: the Maze problem
- Quantum state tomography of large nuclear spins in a semiconductor quantum well: Optimal robustness against errors as quantified by condition numbers
- Experimental quantum stochastic walks simulating associative memory of Hopfield neural networks
- Supersymmetric polarization anomaly in photonic discrete-time quantum walks
- Gradient-descent quantum process tomography by learning Kraus operators
- Generating Haar-uniform Randomness using Stochastic Quantum Walks on a Photonic Chip
- Enhanced detection techniques of Orbital Angular Momentum states in the classical and quantum regimes
- Regression of high dimensional angular momentum states of light
- Efficient tomography of quantum-optical Gaussian processes probed with a few coherent states
- Decoherence enhances performance of quantum walks applied to graph isomorphism testing
- Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification