MPStab: an hybrid stabilizers tensor-network quantum circuit simulator

• URL: http://arxiv.org/abs/2607.24258v1
• Date: Mon, 27 Jul 2026 10:47:55 GMT
• Title: MPStab: an hybrid stabilizers tensor-network quantum circuit simulator
• Abstract summary: We present MPStab, a quantum circuit simulator based on a hybrid formalism combining stabilizers and tensor networks.<n>We present the package, its core functionalities, and explore its performances in a few interesting simulation regimes.
• Score: 0.0538441598991272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of techniques for simulating quantum systems using classical computers is a paramount task for two primary reasons: i) there exist configurations for which classical computers are remarkably effective and will continue to be so, and ii) exploring the limits of classical computation facilitates the identification of the regimes of competence for quantum computers. In this work, we present MPStab, a quantum circuit simulator based on a hybrid formalism combining stabilizers and tensor networks, recently introduced in Ref. [1]. We present the package, its core functionalities, and explore its performances in a few interesting simulation regimes.

Related papers

• Learnable yet not simulable: a quantum resource theory of learning models [57.695822250391906]
  It remains unclear which quantum resource governs the classical learnability of quantum circuits.<n>We study the expectation-value functions of families of tunable quantum circuits.<n>These results establish a quantitative resource-theoretic framework for delineating the boundary between classical simulation and learning.
  arXiv  Detail & Related papers  (2026-08-03T14:48:03Z)
• Exploring polymer classification with a hybrid single-photon quantum approach [45.88028371034407]
  We present a hybrid classical-quantum formalism that couples a classical deep neural network for polymer featurization with a single-photon-based quantum classifier.<n>This pipeline successfully classifies polymer species by their optical gap, with performance in line with CPU-based noisy simulations and a proof-of-principle run on Quandela's Ascella quantum processor.
  arXiv  Detail & Related papers  (2025-12-19T23:06:38Z)
• Towards Quantum Software for Quantum Simulation [2.2677159713373247]
  We identify critical gaps in the quantum simulation software stack.<n>We advocate for a modular model-driven engineering (MDE) approach.<n>We outline a vision for a quantum simulation framework capable of supporting scalable, cross-platform simulation.
  arXiv  Detail & Related papers  (2025-11-17T15:53:37Z)
• GCAMPS: A Scalable Classical Simulator for Qudit Systems [1.126030751694434]
  We introduce a new hybrid simulation method for classical quantum systems.<n>We benchmark this method on quantum systems with three degrees of freedom, i.e. qutrits.<n>We see a greater improvement with qutrit simulation compared to qubit simulation on the same $T$-doped random benchmarking circuit.<n>This extension allows for the classical simulation of problems that were previously intractable without access to a quantum device.
  arXiv  Detail & Related papers  (2025-11-10T03:39:23Z)
• Fermionic dynamics on a trapped-ion quantum computer beyond exact classical simulation [0.024486319744627862]
  We implement an efficient quantum simulation algorithm on Quantinuum's System Model H2 trapped-ion quantum computer.<n>We focus on the periodic spinful 2D Fermi-Hubbard model and present evidence of spin-charge separation.<n>Our results herald the use of quantum computing for simulating strongly correlated electronic systems.
  arXiv  Detail & Related papers  (2025-10-30T09:39:02Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• A Hybrid Quantum-Classical Particle-in-Cell Method for Plasma Simulations [1.4612011935666247]
  The electrostatic field Poisson solver is implemented on a quantum computer simulator using a hybrid classical-quantum Neural Network (HNN)<n>The HNN is trained on classical PIC simulation results and executed via a PennyLane quantum simulator.<n>We test the hybrid quantum-classical electrostatic PIC against the two-stream instability, a standard benchmark in plasma physics.
  arXiv  Detail & Related papers  (2025-05-14T10:19:33Z)
• Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
  We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
  arXiv  Detail & Related papers  (2024-06-05T17:16:07Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Practicality of training a quantum-classical machine in the NISQ era [0.0]
  This study explores the limits of training a real experimental quantum classical hybrid system using supervised training protocols, on an ion trap platform.<n>Challenges associated with ion trap-coupled classical processors are addressed, highlighting the $robustness$ of the genetic algorithm as a classical in navigating the noisy channels of NISQ-devices.<n>These findings contribute insights into the performance of quantum-classical hybrid systems, emphasizing the significance of efficient training strategies and hardware considerations for practical quantum machine learning applications.
  arXiv  Detail & Related papers  (2024-01-22T16:27:14Z)
• Classical Chaos in Quantum Computers [39.58317527488534]
  Current-day quantum processors, comprising 50-100 qubits, operate outside the range of quantum simulation on classical computers. We demonstrate that the simulation of classical limits can be a potent diagnostic tool potentially mitigating this problem. We find that classical and quantum simulations lead to similar stability metrics in systems with $mathcalO$ transmons.
  arXiv  Detail & Related papers  (2023-04-27T18:00:04Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Recompilation-enhanced simulation of electron-phonon dynamics on IBM Quantum computers [62.997667081978825]
  We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems. We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling. Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
  arXiv  Detail & Related papers  (2022-02-16T19:00:00Z)
• Quantum-Classical Hybrid Algorithm for the Simulation of All-Electron Correlation [58.720142291102135]
  We present a novel hybrid-classical algorithm that computes a molecule's all-electron energy and properties on the classical computer. We demonstrate the ability of the quantum-classical hybrid algorithms to achieve chemically relevant results and accuracy on currently available quantum computers.
  arXiv  Detail & Related papers  (2021-06-22T18:00:00Z)
• Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale [57.84751206630535]
  We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework. The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators. By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
  arXiv  Detail & Related papers  (2021-04-21T13:26:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.