Fault-tolerant quantum computing fundamentally relies on the accurate characterization of circuit-level noise to optimize decoding algorithms. However, extracting complex multi-body error correlations remains challenging. Contemporary greedy inference algorithms can suffer from statistical distortion, discarding true physical mechanisms while introducing many unphysical false positives. Here, we introduce the Correlation-Analysis-based Hypergraph Reconstruction (CAHR) algorithm, a globally consistent framework to invert experimental syndrome statistics directly into discrete physical hypergraphs. By coupling exact algebraic correlation equations with a top-down concurrent-pruning strategy, CAHR recovers the fault topology without false positives for both $d=5$ rotated surface codes and dense 8-body 2D color codes in our benchmark settings. Furthermore, we show that exact continuous parameter extraction in dense codes is limited by a \textit{variance cascade}, where absolute statistical variance accumulates linearly from high- to low-degree mechanisms. This motivates a two-stage inference paradigm: utilizing CAHR to extract the fault topology, followed by continuous probability optimization. This provides a practical approach for characterizing and decoding highly correlated noise in realistic quantum hardware.Fault-tolerant quantum computing fundamentally relies on the accurate characterization of circuit-level noise to optimize decoding algorithms. However, extracting complex multi-body error correlations remains challenging. Contemporary greedy inference algorithms can suffer from statistical distortion, discarding true physical mechanisms while introducing many unphysical false positives. Here, we introduce the Correlation-Analysis-based Hypergraph Reconstruction (CAHR) algorithm, a globally consistent framework to invert experimental syndrome statistics directly into discrete physical hypergraphs. By coupling exact algebraic correlation equations with a top-down concurrent-pruning strategy, CAHR recovers the fault topology without false positives for both $d=5$ rotated surface codes and dense 8-body 2D color codes in our benchmark settings. Furthermore, we show that exact continuous parameter extraction in dense codes is limited by a \textit{variance cascade}, where absolute statistical variance accumulates linearly from high- to low-degree mechanisms. This motivates a two-stage inference paradigm: utilizing CAHR to extract the fault topology, followed by continuous probability optimization. This provides a practical approach for characterizing and decoding highly correlated noise in realistic quantum hardware.

quant-pharXiv:2606.16288Chinese Physics BJun 16, 20260 citations

DBCI on IBM Quantum: Real-Hardware Observation of Dimensional-Collapse Saturation Across Devices

Razo, Luis, Cohen, Eliahu

We report the first real-hardware implementation and verification of Dual Boundary Condition Inference (DBCI) on IBM Quantum systems. This work validates the predicted size-dependent transition in DBCI's information flow dynamics. Leveraging the ibm_brisbane (127-qubit) and ibm_fez processors, we conducted a systematic analysis using random-circuit sampling across an unprecedented range of 9–100 qubits. The experimental results established a robust, reproducible entropy-collapse signature characterized by three distinct regimes: Amplification (Small Circuits, < 13 qubits): Demonstrated significant multi-bit coherence gains ( bits). Saturation (Large Circuits, qubits): Consistently converged to the theoretical algebraic limit, bits. Bistability (Intermediate Circuits): A sharp crossover regime between the two limits. Matched noiseless simulations confirmed that this entire three-regime structure—amplification, bistability, and saturation—arises solely from the intrinsic algebraic properties of the DBCI update kernel, rather than from hardware noise. High-precision verification showed that the marginal hardware–simulator offset was statistically indistinguishable from zero. This evidence suggests that the saturation represents a hardware-stable invariance condition inherent to dual-boundary inference, indicating a deterministic crossover between finite-size coherence effects and fundamental boundary-limited information flow in quantum systems.We report the first real-hardware implementation and verification of Dual Boundary Condition Inference (DBCI) on IBM Quantum systems. This work validates the predicted size-dependent transition in DBCI's information flow dynamics. Leveraging the ibm_brisbane (127-qubit) and ibm_fez processors, we conducted a systematic analysis using random-circuit sampling across an unprecedented range of 9–100 qubits. The experimental results established a robust, reproducible entropy-collapse signature characterized by three distinct regimes: Amplification (Small Circuits, < 13 qubits): Demonstrated significant multi-bit coherence gains ( bits). Saturation (Large Circuits, qubits): Consistently converged to the theoretical algebraic limit, bits. Bistability (Intermediate Circuits): A sharp crossover regime between the two limits. Matched noiseless simulations confirmed that this entire three-regime structure—amplification, bistability, and saturation—arises solely from the intrinsic algebraic properties of the DBCI update kernel, rather than from hardware noise. High-precision verification showed that the marginal hardware–simulator offset was statistically indistinguishable from zero. This evidence suggests that the saturation represents a hardware-stable invariance condition inherent to dual-boundary inference, indicating a deterministic crossover between finite-size coherence effects and fundamental boundary-limited information flow in quantum systems.

Nov 11, 20250 citations

A physics-based machine learning approach to quantum device characterization

Daniel Hothem, Thomas Catanach, Timothy J Proctor +1 more

APS March Meeting AbstractsMar 1, 20230 citations

Pulse-level characterization of qubits on quantum devices

Quiroga Salamanca, David Balaguer Andrés

Jan 1, 20211 citation

Efficiently Learning Global Quantum Channels with Local Tomography

Zidu Liu, Dominik S. Wild

Scalable characterization of quantum processors is crucial for mitigating noise and imperfections. While randomized measurement protocols enable efficient access to local observables, inferring a globally consistent description of multi-qubit processes remains challenging. Here we introduce a local-to-global reconstruction framework for one-dimensional multi-qubit states and channels. The method is efficient provided that correlations, as quantified by the conditional mutual information, decay exponentially. In particular, we prove that under this assumption, the required number of samples scales polynomially with the system size and the desired global reconstruction error. Our approach is based on combining local shadow tomography with locally optimal recovery maps obtained by convex optimization. We supplement these rigorous guarantees by studying the performance of the protocol numerically for a system evolving under a local Lindbladian and a noisy, shallow circuit. By employing a tensor networ representation, we reconstruct channels acting on up to 50 qubits and accurately recover global diagnostics such as the process fidelity, the Choi state purity, and Pauli-weight-resolved process matrix elements. Our work thus extends the powerful toolbox local shadow tomography to scalable channel characterization with access to global properties.Scalable characterization of quantum processors is crucial for mitigating noise and imperfections. While randomized measurement protocols enable efficient access to local observables, inferring a globally consistent description of multi-qubit processes remains challenging. Here we introduce a local-to-global reconstruction framework for one-dimensional multi-qubit states and channels. The method is efficient provided that correlations, as quantified by the conditional mutual information, decay exponentially. In particular, we prove that under this assumption, the required number of samples scales polynomially with the system size and the desired global reconstruction error. Our approach is based on combining local shadow tomography with locally optimal recovery maps obtained by convex optimization. We supplement these rigorous guarantees by studying the performance of the protocol numerically for a system evolving under a local Lindbladian and a noisy, shallow circuit. By employing a tensor networ representation, we reconstruct channels acting on up to 50 qubits and accurately recover global diagnostics such as the process fidelity, the Choi state purity, and Pauli-weight-resolved process matrix elements. Our work thus extends the powerful toolbox local shadow tomography to scalable channel characterization with access to global properties.

quant-pharXiv:2603.07037Mar 10, 20260 citations

d=5

rotated surface codes and dense 8-body 2D color codes in our benchmark settings. Furthermore, we show that exact continuous parameter extraction in dense codes is limited by a \textit{variance cascade}, where absolute statistical variance accumulates linearly from high- to low-degree mechanisms. This motivates a two-stage inference paradigm: utilizing CAHR to extract the fault topology, followed by continuous probability optimization. This provides a practical approach for characterizing and decoding highly correlated noise in realistic quantum hardware.