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

Physical Review A

Understanding qubit trajectories in parametrized quantum circuits

Seungcheol Oh, Chaemoon Im, Soohyun Park, Joongheon Kim

Published Dec 29, 2025• 1 citation
Publisher
DOI
10.1103/1x3t-9kzq

Related papers

Establishing Convergence Thresholds for Pre-Trajectory Sampling with Batched Execution Across Random Quantum Circuits

Eskew, Taylor L., Gonthier, Jerome F., Patti, Taylor L. +1 more

A crucial aspect of validating quantum protocols is understanding the noise produced by quantum computing devices. Using simulations that can replicate this noise allows for a lower-cost alternative to hardware experiments. Stochastic, so-called "trajectory" methods are often used as a quadratically reduced approximation to density matrix simulations, but traditional implementations have limited sampling capacity and provide no error-based metadata. The Pre-Trajectory Sampling with Batched Execution (PTSBE) [Patti et al., 2025] algorithm provides a solution by combining fine-tuned, well-documented noise sampling with computational intermediate caching. While the original work is effective on quantum error correction circuits, its performance on general circuits has yet to be explored. This project gauges PTSBE on random quantum circuits to understand convergence behavior and accuracy compared to traditional methods. To accomplish this, an automated simulation pipeline was developed by using Apache Airflow and CUDA-Q to generate a collection of noisy circuits, run both exact and approximate simulations, and analyze error criteria across various qubit counts and circuit depths. Early tests have confirmed the system's ability to scale to 20-qubit circuits and produce accurate comparison data. Our work aims to provide guidance for researchers using PTSBE in noisy simulation environments, expanding its use beyond specific cases.

•Dec 4, 2025•0 citations

Quantum Mechanics for Qubit Systems

Masahito Hayashi, Satoshi Ishizaka, Akinori Kawachi +2 more

Graduate texts in physics•Aug 22, 2014•0 citations

Modeling quantum circuits

Margareta Vamos

•Jan 1, 2011•1 citation

Effect of information component of inaccurate continuous measurements of signal characteristics qubit-detectors with parametric transformation of energy

Sergiy Melnyk, Н. И. Слипченко, V. I. Shnyrkov

The model of an inaccurate continuous measurement of parametric energy conversion in the charge-phase qubit-detector of Rabi oscillation modes is presented. Apart from the inductive interaction of the qubit, the measuring circuit allows for the effect of continuous partial collapse of the qubit state with its inaccurate measurement. The present article considers the case of practically pure continuous inaccurate measurement of the signal qubit detector characteristics operating in a parametric energy conversion mode. To describe it we have used a previously developed [3] information approach to characterizing the dynamics of the afore-mentioned type of measurement based upon the the quantum Bayes theorem. We have also proposed a principle of invariance for a classical trajectory of the detector in the course of the continuous inaccurate measurement in relation to the mode of qubit quantomechanic description [4].

International Crimean Conference Microwave and Telecommunication Technology•Oct 22, 2012•0 citations

Quantum machine learning beyond kernel methods

Sofiène Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup +3 more

Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of quantum machine learning models have been introduced and studied extensively. Yet, our understanding of how these models compare, both mutually and to classical models, remains limited. In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models. In particular, we show using tools from quantum information theory how data re-uploading circuits, an apparent outlier of this framework, can be efficiently mapped into the simpler picture of linear models in quantum Hilbert spaces. Furthermore, we analyze the experimentally-relevant resource requirements of these models in terms of qubit number and amount of data needed to learn. Based on recent results from classical machine learning, we prove that linear quantum models must utilize exponentially more qubits than data re-uploading models in order to solve certain learning tasks, while kernel methods additionally require exponentially more data points. Our results provide a more comprehensive view of quantum machine learning models as well as insights on the compatibility of different models with NISQ constraints.

arXiv:2110.13162•Nature Communications•Jan 31, 2023•238 citations

Proposal of Quantum Circuits and Qubit Mapping Optimization

賢太 海老澤, 1477, Kenta Ebisawa

•Oct 1, 2025•0 citations
A crucial aspect of validating quantum protocols is understanding the noise produced by quantum computing devices. Using simulations that can replicate this noise allows for a lower-cost alternative to hardware experiments. Stochastic, so-called "trajectory" methods are often used as a quadratically reduced approximation to density matrix simulations, but traditional implementations have limited sampling capacity and provide no error-based metadata. The Pre-Trajectory Sampling with Batched Execution (PTSBE) [Patti et al., 2025] algorithm provides a solution by combining fine-tuned, well-documented noise sampling with computational intermediate caching. While the original work is effective on quantum error correction circuits, its performance on general circuits has yet to be explored. This project gauges PTSBE on random quantum circuits to understand convergence behavior and accuracy compared to traditional methods. To accomplish this, an automated simulation pipeline was developed by using Apache Airflow and CUDA-Q to generate a collection of noisy circuits, run both exact and approximate simulations, and analyze error criteria across various qubit counts and circuit depths. Early tests have confirmed the system's ability to scale to 20-qubit circuits and produce accurate comparison data. Our work aims to provide guidance for researchers using PTSBE in noisy simulation environments, expanding its use beyond specific cases.
The model of an inaccurate continuous measurement of parametric energy conversion in the charge-phase qubit-detector of Rabi oscillation modes is presented. Apart from the inductive interaction of the qubit, the measuring circuit allows for the effect of continuous partial collapse of the qubit state with its inaccurate measurement. The present article considers the case of practically pure continuous inaccurate measurement of the signal qubit detector characteristics operating in a parametric energy conversion mode. To describe it we have used a previously developed [3] information approach to characterizing the dynamics of the afore-mentioned type of measurement based upon the the quantum Bayes theorem. We have also proposed a principle of invariance for a classical trajectory of the detector in the course of the continuous inaccurate measurement in relation to the mode of qubit quantomechanic description [4].
Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of quantum machine learning models have been introduced and studied extensively. Yet, our understanding of how these models compare, both mutually and to classical models, remains limited. In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models. In particular, we show using tools from quantum information theory how data re-uploading circuits, an apparent outlier of this framework, can be efficiently mapped into the simpler picture of linear models in quantum Hilbert spaces. Furthermore, we analyze the experimentally-relevant resource requirements of these models in terms of qubit number and amount of data needed to learn. Based on recent results from classical machine learning, we prove that linear quantum models must utilize exponentially more qubits than data re-uploading models in order to solve certain learning tasks, while kernel methods additionally require exponentially more data points. Our results provide a more comprehensive view of quantum machine learning models as well as insights on the compatibility of different models with NISQ constraints.