Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Lecture notes in networks and systems

Quantum Computing Error Mitigation Advance in Real-Time Quantum Computer with Novel Quantum Using Superposition and Entanglement of Qubits

V. Karthick, A. Gayathri

Published Jan 1, 2024• 0 citations
Publisher
DOI
10.1007/978-981-97-6036-7_23

Related papers

Quantum Information Processing, Quantum Computing, and Quantum Error Correction

Elsevier eBooks•Jan 1, 2021•13 citations

Real-time measurement error mitigation for one-way quantum computation

Tobias Hartung, S. Schuster, Joachim von Zanthier +1 more

Abstract We propose a quantum error mitigation scheme for single-qubit measurement errors, particularly suited for one-way quantum computation. Contrary to well established error mitigation methods for circuit-based quantum computation that require the circuits to be run several times, our method is capable of mitigating measurement errors in real time during the processing measurements in the one-way computation. For that, an ancillary qubit register is entangled with the to-be-measured qubit and additionally measured afterwards. By using a voting protocol on all measurement outcomes, occurring measurement errors can be mitigated in real time while the one-way computation continues. We provide an analytical expression for the probability to detect a measurement error depending on the error rate and the number of ancilla qubits. From this, we derive an estimate of the ancilla register size for a given measurement error rate and a required success probability to detect a measurement error. Additionally, we consider the CNOT gate error in our mitigation method and investigate how this influences the probability to detect a measurement error. Finally, we show in proof-of-principle simulations and in experiments executed on IBM quantum hardware that our method is capable of reducing the measurement errors significantly in a one-way quantum computation with only a small number of ancilla qubits.

arXiv:2411.09084•Scientific Reports•Sep 19, 2026•0 citations

Enhancing quantum computation accuracy on IBM quantum hardware through advanced error mitigation strategies

V. Karthick, D Lakshanya, P. M. Ashok Kumar

Abstract Hardware Error Outputs of Quantum Computing Not Reliable because of Hardware Calibration Drift, Measurement Noise, Gate Errors on NISQ Systems. Even though MEMs, ZNEs, and CDRs slightly increase output accuracy, they operate under the assumption of static noise, therefore they do not account for changing output of the NISQ devices in real-time. The limitations of MEMs, ZNEs, and CDRs can be overcome by using the Hybrid Adaptive Error Mitigation (HAEM) method proposed in this article that has the capability to combine baseline measurement-error mitigation with Real-time Calibration Circuits and Lightweight-Machine-learning algorithms for adaptive error-correcting and increasing the overall output integrity of quantum devices affected by fluctuations in noise. The application of the HAEM method will utilize Qiskit Runtime to develop, implement, and validate the Hybrid Adaptive Error Mitigation method through testing using controlled noisy simulated environments based on the characteristics of current advanced NISQ quantum computers. The results of the tests demonstrate the HAEM architecture provides a higher level of accuracy and fidelity than static and non-mitigated executions ("mitigated execution vs. HAEM execution"), providing an increase of up to thirty-four percent in fidelity under high-noise conditions with minimal computational overhead. The results demonstrate that the use of adaptive and data-driven mitigation techniques, like those used in the Hybrid Adaptive Error Mitigation method, are an efficient way to increase the accuracy of quantum computations.

Discover Quantum Science•May 20, 2026•0 citations

Non-silicon Computing with Quantum Superposition Entanglement Using Qubits

N. Vidhya, V. Seethalakshmi, S Suganyadevi

Studies in computational intelligence•Jan 1, 2023•3 citations

Quantum entanglement and quantum computing

F. J. Duarte

Institute of Physics eBooks•Sep 1, 2022•0 citations

Real-time error mitigation for variational optimization on quantum hardware

Matteo Robbiati, Alejandro Sopena, Andrea Papaluca +1 more

In this work, we put forward the inclusion of error mitigation routines in the process of training variational quantum circuit (VQC) models. In detail, we define a real-time quantum error mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. While state-of-the-art QEM methods cannot address the exponential loss concentration induced by noise in current devices, we demonstrate that our RTQEM routine can enhance VQCs’ trainability by reducing the corruption of the loss function. We tested the algorithm by simulating and deploying the fit of a monodimensional u -quark parton distribution function on a superconducting single-qubit device, and we further analyzed the scalability of the proposed technique by simulating a multidimensional fit with up to eight qubits.

arXiv:2311.05680•Physical Review Research•Jan 12, 2026•3 citations
Abstract We propose a quantum error mitigation scheme for single-qubit measurement errors, particularly suited for one-way quantum computation. Contrary to well established error mitigation methods for circuit-based quantum computation that require the circuits to be run several times, our method is capable of mitigating measurement errors in real time during the processing measurements in the one-way computation. For that, an ancillary qubit register is entangled with the to-be-measured qubit and additionally measured afterwards. By using a voting protocol on all measurement outcomes, occurring measurement errors can be mitigated in real time while the one-way computation continues. We provide an analytical expression for the probability to detect a measurement error depending on the error rate and the number of ancilla qubits. From this, we derive an estimate of the ancilla register size for a given measurement error rate and a required success probability to detect a measurement error. Additionally, we consider the CNOT gate error in our mitigation method and investigate how this influences the probability to detect a measurement error. Finally, we show in proof-of-principle simulations and in experiments executed on IBM quantum hardware that our method is capable of reducing the measurement errors significantly in a one-way quantum computation with only a small number of ancilla qubits.
Abstract Hardware Error Outputs of Quantum Computing Not Reliable because of Hardware Calibration Drift, Measurement Noise, Gate Errors on NISQ Systems. Even though MEMs, ZNEs, and CDRs slightly increase output accuracy, they operate under the assumption of static noise, therefore they do not account for changing output of the NISQ devices in real-time. The limitations of MEMs, ZNEs, and CDRs can be overcome by using the Hybrid Adaptive Error Mitigation (HAEM) method proposed in this article that has the capability to combine baseline measurement-error mitigation with Real-time Calibration Circuits and Lightweight-Machine-learning algorithms for adaptive error-correcting and increasing the overall output integrity of quantum devices affected by fluctuations in noise. The application of the HAEM method will utilize Qiskit Runtime to develop, implement, and validate the Hybrid Adaptive Error Mitigation method through testing using controlled noisy simulated environments based on the characteristics of current advanced NISQ quantum computers. The results of the tests demonstrate the HAEM architecture provides a higher level of accuracy and fidelity than static and non-mitigated executions ("mitigated execution vs. HAEM execution"), providing an increase of up to thirty-four percent in fidelity under high-noise conditions with minimal computational overhead. The results demonstrate that the use of adaptive and data-driven mitigation techniques, like those used in the Hybrid Adaptive Error Mitigation method, are an efficient way to increase the accuracy of quantum computations.
In this work, we put forward the inclusion of error mitigation routines in the process of training variational quantum circuit (VQC) models. In detail, we define a real-time quantum error mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. While state-of-the-art QEM methods cannot address the exponential loss concentration induced by noise in current devices, we demonstrate that our RTQEM routine can enhance VQCs’ trainability by reducing the corruption of the loss function. We tested the algorithm by simulating and deploying the fit of a monodimensional u -quark parton distribution function on a superconducting single-qubit device, and we further analyzed the scalability of the proposed technique by simulating a multidimensional fit with up to eight qubits.