Quantum tomography benchmarking
arXiv:2012.15656 · doi:10.1007/s11128-021-03285-9
Abstract
Recent advances in quantum computers and simulators are steadily leading us towards full-scale quantum computing devices. Due to the fact that debugging is necessary to create any computing device, quantum tomography (QT) is a critical milestone on this path. In practice, the choice between different QT methods faces the lack of comparison methodology. Modern research provides a wide range of QT methods, which differ in their application areas, as well as experimental and computational complexity. Testing such methods is also being made under different conditions, and various efficiency measures are being applied. Moreover, many methods have complex programming implementations; thus, comparison becomes extremely difficult. In this study, we have developed a general methodology for comparing quantum state tomography methods. The methodology is based on an estimate of the resources needed to achieve the required accuracy. We have developed a software library (in MATLAB and Python) that makes it easy to analyze any QT method implementation through a series of numerical experiments. The conditions for such a simulation are set by the number of tests corresponding to real physical experiments. As a validation of the proposed methodology and software, we analyzed and compared a set of QT methods. The analysis revealed some method-specific features and provided estimates of the relative efficiency of the methods.
References in corpus (40)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Probing many-body dynamics on a 51-atom quantum simulator
- Continuous-variable optical quantum state tomography
- Experimental Quantum State Tomography of Optical Fields and Ultrafast Statistical Sampling
- Unknown Quantum States and Operations, a Bayesian View
- Many-body quantum state tomography with neural networks
- A programmable two-qubit quantum processor in silicon
- Benchmarking an 11-qubit quantum computer
- Induced measures in the space of mixed quantum states
- Fidelity and Concurrence of conjugated states
- Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity, and Efficient Estimators
- Fidelity benchmarks for two-qubit gates in silicon
- Maximum-likelihood estimation of the density matrix
- Maximum Likelihood, Minimum Effort
- State estimation for large ensembles
- Improving Quantum State Estimation with Mutually Unbiased Bases
- Experimental neural network enhanced quantum tomography
- Adaptive Bayesian Quantum Tomography
- Quantum fidelity measures for mixed states
- Silicon quantum processor with robust long-distance qubit couplings
- Choice of Measurement Sets in Qubit Tomography
- Self-guided quantum tomography
- Three ways to look at mutually unbiased bases
- Superfast maximum likelihood reconstruction for quantum tomography
- Standard forms of noisy quantum operations via depolarization
- Spin tomography
- Quantum process tomography via completely positive and trace-preserving projection
- Statistical Estimation of Quantum Tomography Protocols Quality
- Experimental Demonstration of Self-Guided Quantum Tomography
- Five measurement bases determine pure quantum states on any dimension
- Experimental Adaptive Quantum Tomography of Two-Qubit States
- A programmable two-qubit solid-state quantum processor under ambient conditions
- Statistical estimation of the quality of quantum-tomography protocols
- Experimentally exploring compressed sensing quantum tomography
- Adaptive compressive tomography with no a priori information
- Separable Measurement Estimation of Density Matrices and its Fidelity Gap with Collective Protocols
- Experimental adaptive process tomography
- Optimal Experiment Design for Quantum State and Process Tomography and Hamiltonian Parameter Estimation
- Adaptive quantum tomography
- Adaptive quantum tomography of high-dimensional bipartite systems