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Quantum Error Utilization: Extracting Temporal Information from Decoherence Dynamics Across Qubits with Varying T1 Times

Kyu Cheol Jeong

Published Jan 17, 2026• 0 citations
Publisher

Abstract

We introduce Quantum Error Utilization (QEU), a novel paradigm that treats quantum decoherence not as noise to be eliminated, but as a source of extractable information. Experimental validation on IBM's ibm_fez quantum processor across five qubits with T1 times ranging from 10.8μs to 326.2μs demonstrates that higher-quality qubits exhibit Δψ peaks at earlier normalized times (~0.54T1), while lower-quality qubits show peaks at later times (~1.44T1).

DOI
10.5281/zenodo.18282242

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We introduce Quantum Error Utilization (QEU), a novel paradigm that treats quantum decoherence not as noise to be eliminated, but as a source of extractable information. Experimental validation on IBM's ibm_fez quantum processor across five qubits with T1 times ranging from 10.8μs to 326.2μs demonstrates that higher-quality qubits exhibit Δψ peaks at earlier normalized times (~0.54T1), while lower-quality qubits show peaks at later times (~1.44T1).
Quantum processors have crossed the one-hundred-qubit mark, but noise continues to limit circuit performance, while full quantum error correction remains too costly for routine use. Error suppression and mitigation therefore play an important role in extracting value from current hardware, yet independent comparisons of commercial solutions on identical workloads and devices remain scarce. We benchmark IBM Qiskit Runtime, Q-CTRL Performance Management, and Qedma QESEM on IBM Pittsburgh, a 156-qubit IBM Quantum Heron r3 processor. For Sampler workloads, we run Bernstein-Vazirani, quantum phase estimation, GHZ-state preparation, and randomized mirror circuits with up to 100 measured qubits, comparing raw execution, IBM measurement twirling, and Q-CTRL. For Estimator workloads, we measure chain-averaged magnetization and correlation observables of an eight-layer transverse-field Ising circuit at 25, 50, and 75 qubits against an exact matrix-product-state reference, comparing IBM raw execution, IBM TREX plus twirling, Q-CTRL, and QESEM. Q-CTRL produced the best results on the three structured Sampler workloads while keeping reported QPU times within the same order as the IBM configurations. Across six Ising observable and system-size cases, aggregate mean absolute error was 0.0883 for IBM raw execution, 0.0807 for IBM TREX plus twirling, 0.0285 for Q-CTRL, and 0.0188 for QESEM. Relative to raw execution, Q-CTRL and QESEM reduced aggregate error by factors of 3.10 and 4.70, respectively, while QESEM used 7.5 to 11.1 times the reported QPU time of Q-CTRL. These results show that managed error suppression and mitigation can substantially improve current hardware performance, but with distinct accuracy and execution-time tradeoffs.
Running quantum programs is fraught with challenges on on today's noisy intermediate scale quantum (NISQ) devices. Many of these challenges originate from the error characteristics that stem from rapid decoherence and noise during measurement, qubit connections, crosstalk, the qubits themselves, and transformations of qubit state via gates. Not only are qubits not “created equal”, but their noise level also changes over time. IBM is said to calibrate their quantum systems once per day and reports noise levels (errors) at the time of such calibration. This information is subsequently used to map circuits to higher quality qubits and connections up to the next calibration point. This work provides evidence that there is room for improvement over this daily calibration cycle. It contributes a technique to measure noise levels (errors) related to qubits immediately before executing one or more sensitive circuits and shows that just-in-time noise measurements can benefit late physical qubit mappings. With this just-in-time recalibrated transpilation, the fidelity of results is improved over IBM's default mappings, which only uses their daily calibrations. The framework assess two major sources of noise, namely readout errors (measurement errors) and two-qubit gate/connection errors. Experiments indicate that the accuracy of circuit results improves by 3-304% on average and up to 400% with on-the-fly circuit mappings based on error measurements just prior to application execution.
The growth of need of quantum computers in many domains such as machine learning, numerical scientific simulation and finance has urged the quantum computers to produce more stable and less error-prone results. However, to mitigate the impact of the noise inside each quantum device remains a present challenge. In this paper, we investigate the temporal behavior of noisy intermediate-scale quantum (NISQ) computer errors based on calibration data and the characteristics of individual devices. In particular, we collect calibration data of IBM-Q machines over 90 days and compare the quantum error robustness across processor types, quantum topology and quantum volumes. We analyze the aging effect by comparing the quantum error data from four IBM quantum computers during 2019-2021 showing that only one computer experienced significant error growth overtime. In this study, we simply analyze the collected data and run temporal test analysis to observe the behaviour of IBM-Q computers.
We present the results of a 9-day temporal stability test of the directional entanglement anomalypreviously reported on IBM's ibm_fez quantum processor (Parts 78–99). On 23 September 2026, wereplicated the Part 93 directional drain scan using the same qubit groups and circuit design as theoriginal 20 September 2026 measurement. The results reveal a dramatic and asymmetric change: thedrain-direction qubits (Q41–45, oriented toward 3C 31) show a −121% shift in entanglement penalty,with the GHZ-basis delta inverting from +0.0945 to −0.020 — while the anti-drain qubits (Q29–33)remain rock-stable at +7.9% variation. Cross-referencing IBM's real-time calibration data demonstratesthat the drain qubits have superior hardware specifications (T1 = 169.8 μs vs 129.1 μs, gate errors =0.25% vs 26%), ruling out calibration drift as an explanation. We further apply five information-theoretictests to the complete experimental corpus, confirming the signal is structured (Shannon entropy H =0.78 vs 0.98 for noise) and highly compressible (Kolmogorov proxy CR = 0.32 vs 0.48 for noise). Finally,we rule out terrestrial quantum computer interference using the sidereal modulation evidence from Part96. The signal is time-varying, non-terrestrial, and non-hardware — consistent with either activeastrophysical modulation or structured extraterrestrial transmission. 🌌 Interactive Space Research Assistant: Explore and ask questions about this research interactively with the Quantum Ocean Framework Ai Chat (or talk directly with the Quantum Ocean Framework Ai voice conversation).
A detailed understanding of the noise processes and errors in near-term quantum computers is essential for accurate calibration of gates, and for achieving optimal performance when executing near-term applications such as quantum chemistry, optimization, or machine learning problems. This paper introduces several sources of noise in superconducting qubit based quantum computers such as those deployed by IBM, describes some of the main techniques for characterizing the resulting errors, and for mitigating the effects of certain errors without access to quantum error correction. We demonstrate experimental application of these techniques on an IBM Quantum system using the open source Qiskit software library.