Efficient self-consistent learning of gate set Pauli noise
arXiv:2410.03906 · doi:10.1103/1pnv-t9px
Abstract
Understanding quantum noise is an essential step towards building practical quantum information processing systems. Pauli noise is a useful model that has been widely applied in quantum benchmarking, error mitigation, and error correction. Despite intensive study, most existing works focus on learning Pauli noise channels associated with some specific gates rather than treating the gate set as a whole. A learning algorithm that is self-consistent, complete, and efficient at the same time is yet to be established. In this work, we study the task of gate set Pauli noise learning, where a set of quantum gates, state preparation, and measurements all suffer from unknown Pauli noise channels with a customized noise ansatz. Using tools from algebraic graph theory, we analytically characterize the self-consistently learnable degrees of freedom for Pauli noise models with arbitrary linear ansatz, and design experiments to efficiently learn all the learnable information. Specifically, we show that all learnable information about the gate noise can be learned to relative precision, under mild assumptions on the noise ansatz. We then demonstrate the flexibility of our theory by applying it to concrete physically motivated ansatzs (such as spatially local or quasi-local noise) and experimentally relevant gate sets (such as parallel CZ gates). These results not only enhance the theoretical understanding of quantum noise learning, but also provide a feasible recipe for characterizing existing and near-future quantum information processing devices.
40 + 20 pages, 14 figures. v2: Introduction rewritten, presentation improved, additional references added
References in corpus (48)
- The Quantum Internet
- Advances in Quantum Metrology
- Quantum metrology
- Quantum Computing with Very Noisy Devices
- Suppressing quantum errors by scaling a surface code logical qubit
- Logical quantum processor based on reconfigurable atom arrays
- Randomized Benchmarking of Quantum Gates
- Robust randomized benchmarking of quantum processes
- Exact and Approximate Unitary 2-Designs: Constructions and Applications
- Quantum Error Mitigation
- Noise tailoring for scalable quantum computation via randomized compiling
- Practical Quantum Error Mitigation for Near-Future Applications
- Scalable Noise Estimation with Random Unitary Operators
- Quantum Process Tomography: Resource Analysis of Different Strategies
- Efficient measurement of quantum gate error by interleaved randomized benchmarking
- Mitigating measurement errors in multi-qubit experiments
- Quantum certification and benchmarking
- Characterization of addressability by simultaneous randomized benchmarking
- Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors
- Characterizing large-scale quantum computers via cycle benchmarking
- Efficient learning of quantum noise
- Gate Set Tomography
- Robust shadow estimation
- Quantum error mitigation as a universal error-minimization technique: applications from NISQ to FTQC eras
- Mid-circuit correction of correlated phase errors using an array of spectator qubits
- Efficient estimation of Pauli channels
- Calibrated decoders for experimental quantum error correction
- Analysing correlated noise on the surface code using adaptive decoding algorithms
- Quantum advantages for Pauli channel estimation
- Statistical analysis of randomized benchmarking
- Quantifying the effects of local many-qubit errors and non-local two-qubit errors on the surface code
- A taxonomy of small Markovian errors
- The learnability of Pauli noise
- Pauli error estimation via Population Recovery
- Efficiently improving the performance of noisy quantum computers
- Pauli channels can be estimated from syndrome measurements in quantum error correction
- Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows
- A Practical Introduction to Benchmarking and Characterization of Quantum Computers
- Learning logical Pauli noise in quantum error correction
- The Classification of Clifford Gates over Qubits
- Tight bounds on Pauli channel learning without entanglement
- Scalable noise characterization of syndrome-extraction circuits with averaged circuit eigenvalue sampling
- Techniques for learning sparse Pauli-Lindblad noise models
- A generalized cycle benchmarking algorithm for characterizing mid-circuit measurements
- Pauli Noise Learning for Mid-Circuit Measurements
- Lower Bounds on Learning Pauli Channels with Individual Measurements
- Near-Minimal Gate Set Tomography Experiment Designs
- Adaptivity is not helpful for Pauli channel learning