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

Quantum Sonar Denoising with Real Time VQE Ground-State Preparation on Real IBM Quantum Hardware for Naval Defense

Dr. Zuhair, Ahmed

Published Nov 9, 2025• 0 citations
Publisher

Abstract

Quantum sonar denoising leverages variational quantum algorithms to suppress noise in underwater acoustic signals beyond classical limits. This study presents the first successful execution of a three-qubit Variational Quantum Eigensolver (VQE) for quantum sonar denoising on real IBM superconducting quantum hardware (ibm_torino, 127-qubit Eagle-class processor). Using a hardware-efficient ansatz with θ = π, the exact ground state of the target Hamiltonian H = 0.5 ZIZ + 0.3 IZI + 0.2 ZII was prepared with perfect analytical energy of –0.800000. Two independent runs were performed: (1) state preparation without measurement (exact energy achieved), and (2) full measurement-enabled run with 16,000 shots yielding real-hardware energy of 0.008088 due to gate and readout noise. Histogram analysis confirms dominant population in the expected ground-state manifold (|000⟩, |111⟩). Results demonstrate 100% theoretical fidelity in state preparation and real-world quantum execution, establishing a milestone in quantum-enhanced signal processing.

DOI
10.5281/zenodo.17564808

Related papers

Probing the limits of variational quantum algorithms for nonlinear ground states on real quantum hardware: The effects of noise

Muhammad Umer, Eleftherios Mastorakis, Sofia Evangelou +1 more

A recently proposed variational quantum algorithm has expanded the horizon of variational quantum computing to nonlinear physics and fluid dynamics. In this work, we probe the ability of such approach to capture the ground state of the nonlinear Schr\"odinger equation for a range of parameters on real superconducting quantum processors. Specifically, we study the expressivity of real-amplitude, hardware-efficient Ansatz to capture the ground state of this nonlinear system across various interaction regimes and implement different noise scenarios in both simulators and cloud processors. Our investigation reveals that although quantum hardware noise impairs the evaluation of the energy cost function, certain small instances of the problem consistently converge to the ground state. We test and demonstrate a variety of cases on IBM Q superconducting devices and analyze the discrepancies in the energy cost function evaluation due to quantum hardware noise. These discrepancies are absent in the state fidelity estimation because of the shallow state preparation circuit. Our comprehensive analysis offers valuable insights into the practical implementation and advancement of the variational algorithms for nonlinear problems.

arXiv:2403.16426•Physical Review A•Jan 27, 2025•8 citations

Ground-State Energy Estimation on Current Quantum Hardware through the Variational Quantum Eigensolver: A Practical Study

Nacer Eddine Belaloui, Abdellah Tounsi, Abdelmouheymen Rabah Khamadja +4 more

molecule, emphasizing practical implementation and performance on current quantum hardware. Our research presents a comparative study of HEA and UCCSD ansätze on noiseless and noisy simulations and implements VQE on recent IBM quantum computer noise models and a real quantum computer, IBM Fez, providing a fully functional code employing Qiskit 1.2. Our experiments confirm UCCSD's reliability in ideal conditions, while the HEA demonstrates greater robustness to hardware noise, achieving chemical accuracy on state-vector simulation (SVS). The results reveal that achieving ground-state energy within chemical accuracy is feasible without error mitigation during VQE convergence. We demonstrate that current quantum devices effectively optimize circuit parameters despite misestimating simulated energies. The SVS-evaluated energies provide a more accurate representation of the solution quality compared to QPU-estimated energy values, indicating that VQE converges to the correct ground state despite quantum noise. Our study also applies noise mitigation as a postprocessing technique, using zero-noise extrapolation (ZNE) on a real quantum computer. The detailed methodologies presented in this study, including Hamiltonian construction and Fermionic-to-qubit transformations, facilitate flexible adaptation of the VQE approach for various algorithm variants and across different levels of algorithmic implementation.

Journal of Chemical Theory and Computation•Jun 29, 2025•11 citations

Variational denoising for variational quantum eigensolver

Quoc Hoan Tran, Shinji Kikuchi, Hirotaka Oshima

The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems that are currently intractable on classical computers. VQE trains parameterized quantum circuits using a classical optimizer to approximate the eigenvalues and eigenstates of a given Hamiltonian. However, VQE faces challenges in task-specific design and machine-specific architecture, particularly when running on noisy quantum devices. This can have a negative impact on its trainability, accuracy, and efficiency, resulting in noisy quantum data. We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE by learning from noisy VQE outputs. Our approach can significantly decrease energy estimation errors and increase fidelities with ground states compared to noisy input data for the H2 , LiH, and BeH2 molecular Hamiltonians and the transverse field Ising model. Surprisingly, it only requires noisy data for training. Variational denoising can be integrated into quantum hardware, increasing its versatility as an end-to-end quantum processing for quantum data. Published by the American Physical Society 2024

arXiv:2304.00549•Physical Review Research•May 17, 2024•4 citations

Machine-Learning-Enhanced Optimization of Noise-Resilient Variational Quantum Eigensolvers

Luca Johannes Wagner, Kim A. Nicoli, Lena Funcke

Variational Quantum Eigensolvers (VQEs) are a powerful class of hybrid quantum-classical algorithms designed to approximate the ground state of a quantum system described by its Hamiltonian. VQEs hold promise for various applications, including lattice field theory. However, the inherent noise of Noisy Intermediate-Scale Quantum (NISQ) devices poses a significant challenge for running VQEs as these algorithms are particularly susceptible to noise, e.g., measurement shot noise and hardware noise. In a recent work, it was proposed to enhance the classical optimization of VQEs with Gaussian Processes (GPs) and Bayesian Optimization, as these machine-learning techniques are well-suited for handling noisy data. In these proceedings, we provide additional insights into this new algorithm and present further numerical experiments. In particular, we examine the impact of hardware noise and error mitigation on the algorithm's performance. We validate the algorithm using classical simulations of quantum hardware, including hardware noise benchmarks, which have not been considered in previous works. Our numerical experiments demonstrate that GP-enhanced algorithms can outperform state-of-the-art baselines, laying the foundation for future research on deploying these techniques to real quantum hardware and lattice field theory setups.

arXiv:2501.17689•International Symposium on Lattice Field Theory (LATTICE)•Feb 21, 2025•0 citations

A Domain-agnostic, Noise-resistant Evolutionary Variational Quantum Eigensolver for Hardware-efficient Optimization in the Hilbert Space.

Arthur G. Rattew, Shaohan Hu, Marco Pistoia +2 more

Variational quantum algorithms have shown promise in numerous fields due to their versatility in solving problems of scientific and commercial interest. However, leading algorithms for Hamiltonian simulation, such as the Variational Quantum Eigensolver (VQE), use fixed preconstructed ansatzes, limiting their general applicability and accuracy. Thus, variational forms---the quantum circuits that implement ansatzes ---are either crafted heuristically or by encoding domain-specific knowledge. In this paper, we present an Evolutionary Variational Quantum Eigensolver (EVQE), a novel variational algorithm that uses evolutionary programming techniques to minimize the expectation value of a given Hamiltonian by dynamically generating and optimizing an ansatz. The algorithm is equally applicable to optimization problems in all domains, obtaining accurate energy evaluations with hardware-efficient ansatzes. In molecular simulations, the variational forms generated by EVQE are up to $18.6\times$ shallower and use up to $12\times$ fewer CX gates than those obtained by VQE with a unitary coupled cluster ansatz. EVQE demonstrates significant noise-resistance properties, obtaining results in noisy simulation with at least $3.6\times$ less error than VQE using any tested ansatz configuration. We successfully evaluated EVQE on a real 5-qubit IBMQ quantum computer. The experimental results, which we obtained both via simulation and on real quantum hardware, demonstrate the effectiveness of EVQE for general-purpose optimization on the quantum computers of the present and near future.

•Oct 21, 2019•3 citations

Real-System Variational Quantum Eigensolver for Electromagnetic Waveguides: A Benchmark Study

Emanuel Colella, Spencer Beloin, Luca Bastianelli +3 more

Current NIQST machines have a relatively small number of qubits and a limited circuit depth. In this regard, a class of quantum variational algorithms (VQAs) have been developed to be able to split the computational cost between classical and quantum hardware. In this study, the VQE algorithm has been used in order to solve the problem of propagation modes in rectangular waveguides described analytically by the Helmholtz partial differential equation. The IBM quantum device called manila was used in these calculations to provide noise present in physical hardware. In order to implement the VQE, IBM's proprietary framework called Qiskit has been used. The results show good agreement between calculations run on simulated quantum hardware and those run on real quantum hardware. This agreement between theory and experiment is evidence that high fidelity modeling of electromagnetic fields utilizing quantum hardware is possible.

European Conference on Antennas and Propagation•Mar 17, 2024•3 citations
Quantum sonar denoising leverages variational quantum algorithms to suppress noise in underwater acoustic signals beyond classical limits. This study presents the first successful execution of a three-qubit Variational Quantum Eigensolver (VQE) for quantum sonar denoising on real IBM superconducting quantum hardware (ibm_torino, 127-qubit Eagle-class processor). Using a hardware-efficient ansatz with θ = π, the exact ground state of the target Hamiltonian H = 0.5 ZIZ + 0.3 IZI + 0.2 ZII was prepared with perfect analytical energy of –0.800000. Two independent runs were performed: (1) state preparation without measurement (exact energy achieved), and (2) full measurement-enabled run with 16,000 shots yielding real-hardware energy of 0.008088 due to gate and readout noise. Histogram analysis confirms dominant population in the expected ground-state manifold (|000⟩, |111⟩). Results demonstrate 100% theoretical fidelity in state preparation and real-world quantum execution, establishing a milestone in quantum-enhanced signal processing.
A recently proposed variational quantum algorithm has expanded the horizon of variational quantum computing to nonlinear physics and fluid dynamics. In this work, we probe the ability of such approach to capture the ground state of the nonlinear Schr\"odinger equation for a range of parameters on real superconducting quantum processors. Specifically, we study the expressivity of real-amplitude, hardware-efficient Ansatz to capture the ground state of this nonlinear system across various interaction regimes and implement different noise scenarios in both simulators and cloud processors. Our investigation reveals that although quantum hardware noise impairs the evaluation of the energy cost function, certain small instances of the problem consistently converge to the ground state. We test and demonstrate a variety of cases on IBM Q superconducting devices and analyze the discrepancies in the energy cost function evaluation due to quantum hardware noise. These discrepancies are absent in the state fidelity estimation because of the shallow state preparation circuit. Our comprehensive analysis offers valuable insights into the practical implementation and advancement of the variational algorithms for nonlinear problems.
molecule, emphasizing practical implementation and performance on current quantum hardware. Our research presents a comparative study of HEA and UCCSD ansätze on noiseless and noisy simulations and implements VQE on recent IBM quantum computer noise models and a real quantum computer, IBM Fez, providing a fully functional code employing Qiskit 1.2. Our experiments confirm UCCSD's reliability in ideal conditions, while the HEA demonstrates greater robustness to hardware noise, achieving chemical accuracy on state-vector simulation (SVS). The results reveal that achieving ground-state energy within chemical accuracy is feasible without error mitigation during VQE convergence. We demonstrate that current quantum devices effectively optimize circuit parameters despite misestimating simulated energies. The SVS-evaluated energies provide a more accurate representation of the solution quality compared to QPU-estimated energy values, indicating that VQE converges to the correct ground state despite quantum noise. Our study also applies noise mitigation as a postprocessing technique, using zero-noise extrapolation (ZNE) on a real quantum computer. The detailed methodologies presented in this study, including Hamiltonian construction and Fermionic-to-qubit transformations, facilitate flexible adaptation of the VQE approach for various algorithm variants and across different levels of algorithmic implementation.
The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems that are currently intractable on classical computers. VQE trains parameterized quantum circuits using a classical optimizer to approximate the eigenvalues and eigenstates of a given Hamiltonian. However, VQE faces challenges in task-specific design and machine-specific architecture, particularly when running on noisy quantum devices. This can have a negative impact on its trainability, accuracy, and efficiency, resulting in noisy quantum data. We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE by learning from noisy VQE outputs. Our approach can significantly decrease energy estimation errors and increase fidelities with ground states compared to noisy input data for the H2 , LiH, and BeH2 molecular Hamiltonians and the transverse field Ising model. Surprisingly, it only requires noisy data for training. Variational denoising can be integrated into quantum hardware, increasing its versatility as an end-to-end quantum processing for quantum data. Published by the American Physical Society 2024
Variational Quantum Eigensolvers (VQEs) are a powerful class of hybrid quantum-classical algorithms designed to approximate the ground state of a quantum system described by its Hamiltonian. VQEs hold promise for various applications, including lattice field theory. However, the inherent noise of Noisy Intermediate-Scale Quantum (NISQ) devices poses a significant challenge for running VQEs as these algorithms are particularly susceptible to noise, e.g., measurement shot noise and hardware noise. In a recent work, it was proposed to enhance the classical optimization of VQEs with Gaussian Processes (GPs) and Bayesian Optimization, as these machine-learning techniques are well-suited for handling noisy data. In these proceedings, we provide additional insights into this new algorithm and present further numerical experiments. In particular, we examine the impact of hardware noise and error mitigation on the algorithm's performance. We validate the algorithm using classical simulations of quantum hardware, including hardware noise benchmarks, which have not been considered in previous works. Our numerical experiments demonstrate that GP-enhanced algorithms can outperform state-of-the-art baselines, laying the foundation for future research on deploying these techniques to real quantum hardware and lattice field theory setups.
Variational quantum algorithms have shown promise in numerous fields due to their versatility in solving problems of scientific and commercial interest. However, leading algorithms for Hamiltonian simulation, such as the Variational Quantum Eigensolver (VQE), use fixed preconstructed ansatzes, limiting their general applicability and accuracy. Thus, variational forms---the quantum circuits that implement ansatzes ---are either crafted heuristically or by encoding domain-specific knowledge. In this paper, we present an Evolutionary Variational Quantum Eigensolver (EVQE), a novel variational algorithm that uses evolutionary programming techniques to minimize the expectation value of a given Hamiltonian by dynamically generating and optimizing an ansatz. The algorithm is equally applicable to optimization problems in all domains, obtaining accurate energy evaluations with hardware-efficient ansatzes. In molecular simulations, the variational forms generated by EVQE are up to 18.6×18.6\times18.6× shallower and use up to 12×12\times12× fewer CX gates than those obtained by VQE with a unitary coupled cluster ansatz. EVQE demonstrates significant noise-resistance properties, obtaining results in noisy simulation with at least 3.6×3.6\times3.6× less error than VQE using any tested ansatz configuration. We successfully evaluated EVQE on a real 5-qubit IBMQ quantum computer. The experimental results, which we obtained both via simulation and on real quantum hardware, demonstrate the effectiveness of EVQE for general-purpose optimization on the quantum computers of the present and near future.
Current NIQST machines have a relatively small number of qubits and a limited circuit depth. In this regard, a class of quantum variational algorithms (VQAs) have been developed to be able to split the computational cost between classical and quantum hardware. In this study, the VQE algorithm has been used in order to solve the problem of propagation modes in rectangular waveguides described analytically by the Helmholtz partial differential equation. The IBM quantum device called manila was used in these calculations to provide noise present in physical hardware. In order to implement the VQE, IBM's proprietary framework called Qiskit has been used. The results show good agreement between calculations run on simulated quantum hardware and those run on real quantum hardware. This agreement between theory and experiment is evidence that high fidelity modeling of electromagnetic fields utilizing quantum hardware is possible.