A practical and efficient approach for Bayesian quantum state estimation
arXiv:2002.10354 · doi:10.1088/1367-2630/ab8efa
Abstract
Bayesian inference is a powerful paradigm for quantum state tomography, treating uncertainty in meaningful and informative ways. Yet the numerical challenges associated with sampling from complex probability distributions hampers Bayesian tomography in practical settings. In this Article, we introduce an improved, self-contained approach for Bayesian quantum state estimation. Leveraging advances in machine learning and statistics, our formulation relies on highly efficient preconditioned Crank--Nicolson sampling and a pseudo-likelihood. We theoretically analyze the computational cost, and provide explicit examples of inference for both actual and simulated datasets, illustrating improved performance with respect to existing approaches.
References in corpus (23)
- On the Measurement of Qubits
- Prescription for experimental determination of the dynamics of a quantum black box
- Complete Characterization of a Quantum Process: the Two-Bit Quantum Gate
- Quantum state estimation
- Optimal, reliable estimation of quantum states
- Security Proof for Quantum Key Distribution Using Qudit Systems
- Dimension-independent likelihood-informed MCMC
- Adaptive Bayesian Quantum Tomography
- Geometric MCMC for Infinite-Dimensional Inverse Problems
- Controllable two-photon interference with versatile quantum frequency processor
- Entangled Bloch Spheres: Bloch Matrix and Two Qubit State Space
- Practical Bayesian Tomography
- Quantum secret sharing with polarization-entangled photon pairs
- Experimental Adaptive Bayesian Tomography
- A controlled-NOT gate for frequency-bin qubits
- Experimental Adaptive Quantum Tomography of Two-Qubit States
- Proposals which speed-up function-space MCMC
- Practical adaptive quantum tomography
- Monte Carlo sampling from the quantum state space. II
- Experimental adaptive process tomography
- Pseudo-Bayesian Quantum Tomography with Rank-adaptation
- Quantum state estimation when qubits are lost: A no-data-left-behind approach
- Accelerated dimension-independent adaptive Metropolis
Cited by in corpus (50)
- Quantum Machine Learning for Chemistry and Physics
- A Reconfigurable Quantum Local Area Network Over Deployed Fiber
- Experimental single-setting quantum state tomography
- Bayesian tomography of high-dimensional on-chip biphoton frequency combs with randomized measurements
- Adaptive bandwidth management for entanglement distribution in quantum networks
- Fully Arbitrary Control of Frequency-Bin Qubits
- Advanced Architectures for High-Performance Quantum Networking
- A Bayesian analysis of classical shadows
- High-dimensional discrete Fourier transform gates with the quantum frequency processor
- Broadband polarization-entangled source for C+L-band flex-grid quantum networks
- Generation and characterization of ultrabroadband polarization-frequency hyperentangled photons
- On the experimental feasibility of quantum state reconstruction via machine learning
- CMOS photonic integrated source of ultrabroadband polarization-entangled photons
- A Practical Introduction to Benchmarking and Characterization of Quantum Computers
- Machine-Learning-Derived Entanglement Witnesses
- Simple, reliable and noise-resilient continuous-variable quantum state tomography with convex optimization
- Complete frequency-bin Bell basis synthesizer
- Convergence of reconstructed density matrix to a pure state using maximal entropy approach
- Bayesian homodyne and heterodyne tomography
- Improving application performance with biased distributions of quantum states
- Quantum State Tomography for Matrix Product Density Operators
- Variational Approach to Quantum State Tomography based on Maximal Entropy Formalism
- Optimal resource allocation for flexible-grid entanglement distribution networks
- Operational, gauge-free quantum tomography
- Characterization of Quantum Frequency Processors
- Multi-frequency control and measurement of a spin-7/2 system encoded in a transmon qudit
- Data-Centric Machine Learning in Quantum Information Science
- Atomic clock locking with Bayesian quantum parameter estimation: scheme and experiment
- Classification and reconstruction of optical quantum states with deep neural networks
- Lessons Learned on the Interface between Quantum and Conventional Networking
- Building a controlled-NOT gate between polarization and frequency
- A Tailor-made Quantum State Tomography Approach
- Demonstrating efficient and robust bosonic state reconstruction via optimized excitation counting
- Procrustean entanglement concentration in quantum-classical networking
- Demonstration of machine-learning-enhanced Bayesian quantum state estimation
- Efficient factored gradient descent algorithm for quantum state tomography
- Experimental demonstration of Quantum Overlapping Tomography
- Quantum tomography of Rydberg atom graphs by configurable ancillas
- Resilient Entanglement Distribution in a Multihop Quantum Network
- Quantum Steering on IBMQ
- Deployed quantum link characterization via Bayesian ancilla-assisted process tomography
- An efficient adaptive MCMC algorithm for Pseudo-Bayesian quantum tomography
- Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
- Experimental demonstration of enhanced quantum tomography via quantum reservoir processing
- Fourier-style Quantum State Tomography and Purity Measurement of a Multi-qubit System from Bloch Rotations
- Unorthodox parallelization for Bayesian quantum state estimation
- Stochastic Schrödinger Equations for Quantum Reverse Diffusion
- Quantum Machine Learning for State Tomography Using Classical Data
- In situ quantum verification of polarization-stabilized optical channels
- Estimating Bell Diagonal states with separable measurements