On the experimental feasibility of quantum state reconstruction via machine learning
arXiv:2012.09432 · doi:10.1109/TQE.2021.3106958
Abstract
We determine the resource scaling of machine learning-based quantum state reconstruction methods, in terms of inference and training, for systems of up to four qubits when constrained to pure states. Further, we examine system performance in the low-count regime, likely to be encountered in the tomography of high-dimensional systems. Finally, we implement our quantum state reconstruction method on an IBM Q quantum computer, and compare against both unconstrained and constrained MLE state reconstruction.
arXiv comment9 pages
References in corpus (20)
- Scalable multi-particle entanglement of trapped ions
- Many-body quantum state tomography with neural networks
- Synthesis of Quantum Logic Circuits
- Reconstructing quantum states with generative models
- Maximum Likelihood, Minimum Effort
- Minimal qubit tomography
- Experimental neural network enhanced quantum tomography
- Integrating Neural Networks with a Quantum Simulator for State Reconstruction
- Choice of Measurement Sets in Qubit Tomography
- Recursively Adaptive Quantum State Tomography: Theory and Two-qubit Experiment
- A practical and efficient approach for Bayesian quantum state estimation
- Knowledge and ignorance in incomplete quantum state tomography
- Full reconstruction of a 14-qubit state within four hours
- Machine learning assisted quantum state estimation
- Neural network quantum state tomography in a two-qubit experiment
- Experimental quantum homodyne tomography via machine learning
- A New Strategy of Quantum-State Estimation for Achieving the Cramer-Rao Bound
- Accuracy of quantum-state estimation utilizing Akaike's information criterion
- Neural network state estimation for full quantum state tomography
- Quantum State Tomography with Conditional Generative Adversarial Networks
Cited by in corpus (6)
- Quantum Machine Learning: from physics to software engineering
- Regression of high dimensional angular momentum states of light
- Improving application performance with biased distributions of quantum states
- Data-Centric Machine Learning in Quantum Information Science
- Neural networks for quantum state tomography with constrained measurements
- Demonstration of machine-learning-enhanced Bayesian quantum state estimation