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

arXiv:2602.01927

Numerical Error Extraction by Quantum Measurement Algorithm

Clement Ronfaut, Robin Ollive, Stephane Louise

quant-phSubmitted Feb 2, 2026• Announced Feb 3, 2026• 0 citations• Revised Oct 5, 2026 (v2)
v1 · Feb 2, 2026v2 · Oct 5, 2026
arXivHTMLPDF

Abstract

In quantum computing, some of the main quantum operations (i.e., gates) cannot be simply implemented and require an approximation. Major quantum algorithm routines enable the implementation of these specific quantum gates by combining basic quantum circuits with an iterative structure. In this structure, the number of repetitions of the basic circuit pattern is associated with convergence parameters. The asymptotic convergence of the gate error with respect to the number of basic pattern repetitions is known as the query complexity. The underlying convergence law is bounded, but often lacks explicit constants for specific problem instances. Upper bounds are generally too pessimistic to be useful in practice. The actual convergence law contains constants that depend on the joint properties of the matrix encoded by the query and the initial state vector, which are difficult to compute classically. This paper proposes a strategy to study this convergence law directly from the Quantum Processing Unit (QPU) output. Given a convergence law, this protocol extracts the numerical values of the associated constants from the gate (i.e., operation) approximation at different accuracies (i.e., convergence parameters) constructed directly on the QPU. This protocol is called Numerical Error Extraction by Quantum Measurement Algorithm (NEEQMA). NEEQMA concepts are tested on specific instances of Quantum Signal Processing (QSP) and Hamiltonian Simulation via Trotterization. Knowing the convergence constants associated with a semi-norm of the error allows selecting the smallest convergence parameters that achieve the required gate approximation accuracy, thereby satisfying the quantum algorithm's requirements.

Related papers

Error-robust quantum signal processing using Rydberg atoms

Sina Zeytinoǧlu, Sho Sugiura

Rydberg atom arrays have recently emerged as one of the most promising platforms for quantum simulation and quantum information processing. However, as is the case for other experimental platforms, the longer-term success of the Rydberg atom arrays in implementing quantum algorithms depends crucially on their robustness to gate-induced errors. Here we show that, for an idealized biased-error model based on Rydberg atom dynamics, the implementation of quantum signal processing (QSP) protocols can be made error robust, in the sense that the asymptotic scaling of the gate-induced error probability is slower than that of gate complexity. Moreover, our numerical results that use experimentally accessible parameters indicate that QSP iterates made out of more than 100 gates can be implemented with constant error probability. To showcase our approach, we provide a concrete blueprint to implement QSP-based near-optimal Hamiltonian simulation on the Rydberg atom platform. The proposed protocol substantially improves both the scaling and the overhead of gate-induced errors in comparison to those protocols that implement a fourth-order product formula. Published by the American Physical Society 2024

arXiv:2201.04665•Physical Review Research•Jan 3, 2024•3 citations

Hamiltonian Simulation and Linear Combination of Unitary Decomposition of Structured Matrices

Robin Ollive, Stéphane Louise

To process a problem with a Quantum Processing Unit (QPU), it must be transformed into a sequence of quantum operators, or gates. These operators are either packed into a query (i.e. quantum algorithm primitive) that encodes the problem, or used to construct the cost function for Variationnal Quantum Algorithm (VQA). Typical queries are the problem Hamiltonian Simulation (HS) and the problem Block-Encoding (BEing). To construct the circuits associated with the quantum description, the problem must be mapped as a Linear Combination of Hamiltonian (LCH) or a Linear Combination of nitaries (LCU) matrices. All the summed Hamiltonian matrices or unitary matrices must have a known decomposition in basic gates. The complexity of this query should be incorporated into the quantum algorithm's query complexity, thereby limiting the processing possibilities of QPU for many problems. In this work, we propose Hamiltonian matrices used to map the problem of interest, and that behave like a single qubit when expressed in the appropriate basis. It leads to a framework, the Special Tripotent Hamiltonian (STH) Framework, able to implement most of the typical problems considered for quantum computing. These methods address many problems implemented on QPUs, ranging from second-quantization chemistry operators to graphs associated with Partial Differential Equations (PDE), sparse matrices, and higher-order optimization problems. This work underlines interesting properties associated with the STH basic gate decomposition. These include the ability to switch between LCH and LCU, map non-Hermitian problems, and construct the quantum circuit queries required for quantum computing. We also provide a list of STH that are used for the matrix decomposition of many structured matrices. These structured matrices are associated with graph adjacency matrices that can be combined to implement structured matrices.

quant-pharXiv:2603.17816•Mar 19, 2026•0 citations

Robust quantum error syndrome extraction by classical coding

Alexei Ashikhmin, Ching–Yi Lai, Todd A. Brun

An important issue in the implementation of a quantum computer is to protect quantum information from decoherence. In fault-tolerant quantum computation, the circuits used to measure the error syndromes are themselves faulty; to minimize the effect of syndrome measurement errors, the syndromes are measured repeatedly. This paper introduces a scheme based on classical codes to make this process more robust and/or reduce the needed resources and measurement time. We analyze particular implementations based on low-density generator matrix (LDGM) codes using EXIT functions.

IEEE International Symposium on Information Theory (ISIT)•Jun 1, 2014•23 citations

Realistic simulation of quantum computation using unitary and measurement channels

A. A. Mouedenne, Nader Khammassi, Koen Bertels +1 more

The implementation and practicality of quantum algorithms hinge largely on the quality of operations within a quantum processor. Therefore, including realistic error models in quantum computing simulation platforms is crucial for testing these algorithms. Existing classical simulation techniques of quantum information processing devices exhibit a tradeoff between scalability (the number of qubits that can be simulated) and accuracy (how close the simulation is to the target error model). In this paper, we introduce a simulation approach that relies on approximating the density matrix evolution with a stochastic sum of unitary and measurement channels within a pure-state simulation environment. This model shows an improvement of at least one order of magnitude in terms of accuracy compared to the best known stochastic approaches while allowing us to simulate a larger number of qubits compared to the exact density matrix simulation. Furthermore, we used this approach to realistically simulate Grover's algorithm and the surface code 17 using a gate set tomography characterization of quantum operations as a noise model.

arXiv:2005.06337•Physical Review A•Nov 9, 2020•9 citations

Towards Precision Quantum Simulation of Lattice Gauge Theories

Siddharth Hariprakash

Quantum simulation of lattice gauge theories offers a path to first-principles calculations of real-time dynamics and finite-density phenomena that are inaccessible to classical Euclidean Monte Carlo methods. Realizing this potential requires a pipeline of controlled approximations---lattice discretization, truncation, digitization, state preparation, time evolution, measurement, and continuum extrapolation---each contributing errors and computational costs. General algorithmic asymptotic complexity arguments capture only part of the cost picture: the practical resources required for end-to-end simulation depend on numerical pre-factors, the cost of the subroutines that various algorithms call, and structural features of the lattice theory, all of which the asymptotic scaling leaves unspecified. This thesis develops contributions that aim to make such dependencies explicit and quantitative. We begin with a rigorous analysis of the asymptotic gate complexity of product formulas and Quantum Signal Processing (QSP) for the time evolution of general classes of lattice Hamiltonians, identifying the parameter regimes in which each method is favorable and estimating the associated pre-factors numerically. Since the cost of QSP-based simulation is dominated (in regimes of practical interest) by the block-encoding subroutine it calls, we also develop several novel block-encoding constructions tailored to bosonic lattice field theories and show that signal-processing techniques can be repurposed as highly efficient block-encoding subroutines. We then turn to the problem of truncating the Kogut--Susskind electric-field Hilbert space. Existing error bounds rely on energy-conservation arguments and require the truncation to grow with simulation time. By exploiting Hilbert space fragmentation we derive the tightest error bounds known to date: they decay factorially in the truncation parameter and are independent of simulation time. We validate them numerically for U(1) pure gauge theory and the Schwinger model. Finally, we address the continuum limit in the presence of approximate time evolution. For product formulas, we show that Trotter errors correspond to irrelevant operators that vanish in the continuum limit, which yields a renormalization trajectory independent of the Trotter step size. More generally, we introduce the Statistically-Bounded Time Evolution (SBTE) protocol, an algorithm-independent framework that treats approximation errors as a systematic uncertainty to be driven below the working statistical precision. This decouples the continuum-limit prescription from the choice of time-evolution algorithm and provides a basis for end-to-end cost comparisons across simulation methods.

•May 1, 2026•0 citations

Representing and Probing Errors in Quantum Information Processing Devices

Junan Lin

The quality of quantum information processing devices has been improving at an unprecedented speed. How to faithfully represent the quality of these devices has become an increasingly imminent problem. In this thesis we focus on two aspects in representing and characterizing quantum devices. First, we discuss why most conventional quality metrics are not in principle appropriate to quantify experimentally-determined representations of gate-set elements, due to a gauge degree of freedom in quantum experiments. We then propose an operational quality measure for a gate-set and discuss its usefulness in representing degree of errors and improving experimental control. Second, we develop a protocol that separately and unambiguously characterizes state and measurement errors, relying on high-quality quantum gates. By integrating a method called randomized compiling, we derive a favorable upper bound for the effects of gate errors on the estimated parameters, and numerically demonstrate its performance in the presence of an adversarial gate error.

•Aug 24, 2018•0 citations
In quantum computing, some of the main quantum operations (i.e., gates) cannot be simply implemented and require an approximation. Major quantum algorithm routines enable the implementation of these specific quantum gates by combining basic quantum circuits with an iterative structure. In this structure, the number of repetitions of the basic circuit pattern is associated with convergence parameters. The asymptotic convergence of the gate error with respect to the number of basic pattern repetitions is known as the query complexity. The underlying convergence law is bounded, but often lacks explicit constants for specific problem instances. Upper bounds are generally too pessimistic to be useful in practice. The actual convergence law contains constants that depend on the joint properties of the matrix encoded by the query and the initial state vector, which are difficult to compute classically. This paper proposes a strategy to study this convergence law directly from the Quantum Processing Unit (QPU) output. Given a convergence law, this protocol extracts the numerical values of the associated constants from the gate (i.e., operation) approximation at different accuracies (i.e., convergence parameters) constructed directly on the QPU. This protocol is called Numerical Error Extraction by Quantum Measurement Algorithm (NEEQMA). NEEQMA concepts are tested on specific instances of Quantum Signal Processing (QSP) and Hamiltonian Simulation via Trotterization. Knowing the convergence constants associated with a semi-norm of the error allows selecting the smallest convergence parameters that achieve the required gate approximation accuracy, thereby satisfying the quantum algorithm's requirements.
Rydberg atom arrays have recently emerged as one of the most promising platforms for quantum simulation and quantum information processing. However, as is the case for other experimental platforms, the longer-term success of the Rydberg atom arrays in implementing quantum algorithms depends crucially on their robustness to gate-induced errors. Here we show that, for an idealized biased-error model based on Rydberg atom dynamics, the implementation of quantum signal processing (QSP) protocols can be made error robust, in the sense that the asymptotic scaling of the gate-induced error probability is slower than that of gate complexity. Moreover, our numerical results that use experimentally accessible parameters indicate that QSP iterates made out of more than 100 gates can be implemented with constant error probability. To showcase our approach, we provide a concrete blueprint to implement QSP-based near-optimal Hamiltonian simulation on the Rydberg atom platform. The proposed protocol substantially improves both the scaling and the overhead of gate-induced errors in comparison to those protocols that implement a fourth-order product formula. Published by the American Physical Society 2024
To process a problem with a Quantum Processing Unit (QPU), it must be transformed into a sequence of quantum operators, or gates. These operators are either packed into a query (i.e. quantum algorithm primitive) that encodes the problem, or used to construct the cost function for Variationnal Quantum Algorithm (VQA). Typical queries are the problem Hamiltonian Simulation (HS) and the problem Block-Encoding (BEing). To construct the circuits associated with the quantum description, the problem must be mapped as a Linear Combination of Hamiltonian (LCH) or a Linear Combination of nitaries (LCU) matrices. All the summed Hamiltonian matrices or unitary matrices must have a known decomposition in basic gates. The complexity of this query should be incorporated into the quantum algorithm's query complexity, thereby limiting the processing possibilities of QPU for many problems. In this work, we propose Hamiltonian matrices used to map the problem of interest, and that behave like a single qubit when expressed in the appropriate basis. It leads to a framework, the Special Tripotent Hamiltonian (STH) Framework, able to implement most of the typical problems considered for quantum computing. These methods address many problems implemented on QPUs, ranging from second-quantization chemistry operators to graphs associated with Partial Differential Equations (PDE), sparse matrices, and higher-order optimization problems. This work underlines interesting properties associated with the STH basic gate decomposition. These include the ability to switch between LCH and LCU, map non-Hermitian problems, and construct the quantum circuit queries required for quantum computing. We also provide a list of STH that are used for the matrix decomposition of many structured matrices. These structured matrices are associated with graph adjacency matrices that can be combined to implement structured matrices.
An important issue in the implementation of a quantum computer is to protect quantum information from decoherence. In fault-tolerant quantum computation, the circuits used to measure the error syndromes are themselves faulty; to minimize the effect of syndrome measurement errors, the syndromes are measured repeatedly. This paper introduces a scheme based on classical codes to make this process more robust and/or reduce the needed resources and measurement time. We analyze particular implementations based on low-density generator matrix (LDGM) codes using EXIT functions.
The implementation and practicality of quantum algorithms hinge largely on the quality of operations within a quantum processor. Therefore, including realistic error models in quantum computing simulation platforms is crucial for testing these algorithms. Existing classical simulation techniques of quantum information processing devices exhibit a tradeoff between scalability (the number of qubits that can be simulated) and accuracy (how close the simulation is to the target error model). In this paper, we introduce a simulation approach that relies on approximating the density matrix evolution with a stochastic sum of unitary and measurement channels within a pure-state simulation environment. This model shows an improvement of at least one order of magnitude in terms of accuracy compared to the best known stochastic approaches while allowing us to simulate a larger number of qubits compared to the exact density matrix simulation. Furthermore, we used this approach to realistically simulate Grover's algorithm and the surface code 17 using a gate set tomography characterization of quantum operations as a noise model.
Quantum simulation of lattice gauge theories offers a path to first-principles calculations of real-time dynamics and finite-density phenomena that are inaccessible to classical Euclidean Monte Carlo methods. Realizing this potential requires a pipeline of controlled approximations---lattice discretization, truncation, digitization, state preparation, time evolution, measurement, and continuum extrapolation---each contributing errors and computational costs. General algorithmic asymptotic complexity arguments capture only part of the cost picture: the practical resources required for end-to-end simulation depend on numerical pre-factors, the cost of the subroutines that various algorithms call, and structural features of the lattice theory, all of which the asymptotic scaling leaves unspecified. This thesis develops contributions that aim to make such dependencies explicit and quantitative. We begin with a rigorous analysis of the asymptotic gate complexity of product formulas and Quantum Signal Processing (QSP) for the time evolution of general classes of lattice Hamiltonians, identifying the parameter regimes in which each method is favorable and estimating the associated pre-factors numerically. Since the cost of QSP-based simulation is dominated (in regimes of practical interest) by the block-encoding subroutine it calls, we also develop several novel block-encoding constructions tailored to bosonic lattice field theories and show that signal-processing techniques can be repurposed as highly efficient block-encoding subroutines. We then turn to the problem of truncating the Kogut--Susskind electric-field Hilbert space. Existing error bounds rely on energy-conservation arguments and require the truncation to grow with simulation time. By exploiting Hilbert space fragmentation we derive the tightest error bounds known to date: they decay factorially in the truncation parameter and are independent of simulation time. We validate them numerically for U(1) pure gauge theory and the Schwinger model. Finally, we address the continuum limit in the presence of approximate time evolution. For product formulas, we show that Trotter errors correspond to irrelevant operators that vanish in the continuum limit, which yields a renormalization trajectory independent of the Trotter step size. More generally, we introduce the Statistically-Bounded Time Evolution (SBTE) protocol, an algorithm-independent framework that treats approximation errors as a systematic uncertainty to be driven below the working statistical precision. This decouples the continuum-limit prescription from the choice of time-evolution algorithm and provides a basis for end-to-end cost comparisons across simulation methods.
The quality of quantum information processing devices has been improving at an unprecedented speed. How to faithfully represent the quality of these devices has become an increasingly imminent problem. In this thesis we focus on two aspects in representing and characterizing quantum devices. First, we discuss why most conventional quality metrics are not in principle appropriate to quantify experimentally-determined representations of gate-set elements, due to a gauge degree of freedom in quantum experiments. We then propose an operational quality measure for a gate-set and discuss its usefulness in representing degree of errors and improving experimental control. Second, we develop a protocol that separately and unambiguously characterizes state and measurement errors, relying on high-quality quantum gates. By integrating a method called randomized compiling, we derive a favorable upper bound for the effects of gate errors on the estimated parameters, and numerically demonstrate its performance in the presence of an adversarial gate error.