Quantum Circuit Simulation

Overview

Quantum circuit simulation asks how to reproduce or estimate a circuit’s outputs on classical computers, enabling algorithm development, verification, and planning before or alongside hardware runs. Methods include state-vector and stabilizer simulation, tensor-network contraction and circuit cutting, plus specialized GPU, database, and compiled execution for variational workloads. Benchmarks and learned cost models help predict runtime, memory, and accuracy or select representations and contraction plans based on circuit structure and hardware. No simulator is best across circuit families: entanglement, non-Clifford gates, precision, and depth shape classical difficulty, while approximations and hardware-aware optimization trade accuracy and generality for speed.

Latest papers

CardsList

14 papers4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

  1. Fully quantum training of parametric circuits with Quantum Hamiltonian Descent

    Oct 23, 2026Marco Burato, Leonardo BanchiQuantum Circuit OptimizationVariational Quantum Algorithms

  2. An Efficient Quantum Circuit for Flow Model Execution Using Quantum Neural Networks

    Oct 8, 2026Rui Che, Ludvig af KlintebergQuantum Generative ModelingQuantum Circuit Simulation

  3. Predicting Resource Efficient Hamiltonian Decomposition for Continuous-Time Quantum Walk Simulations

    Aug 21, 2026Mostafa Atallah, Rebekah Herrman, Zain H. SaleemQuantum Circuit SimulationQuantum Circuit Compilation

  4. Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation

    Aug 6, 2026Alfred M. Pastor, Maribel Castillo, Jose M. BadiaTensor NetworksLearning to Rank

  5. RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation

    Aug 6, 2026Chih-Chung HsuAlgorithm SelectionQuantum Circuit Simulation

  6. InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulation

    Jul 31, 2026Andrei Ilinescu, Aadi Patwardhan, Rihan HaiBenchmark DesignSynthetic Benchmark Generation

  7. Stacking the Deck: Tunable Trainability in Stacked LCUs

    Jul 27, 2026Nikhil Khatri, Stefan Zohren, Gabriel MatosBarren PlateausQuantum Circuit Simulation

  8. Entanglement geometry separates circuit cutting, classical hardness, and trainability

    Jul 20, 2026Maria Gragera Garces, Sabina Drăgoi, Lirandë PiraTensor NetworksQuantum Circuit Simulation

  9. VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?

    Jul 13, 2026Anton Firc, Martin Perešíni, Vojtěch Mrázek +7GPU AccelerationHybrid Quantum-Classical ML

  10. A Novel Parallel QCNN Architecture with Efficient Classical Simulability

    Jul 9, 2026Lawrence Nguyen, Hiu Yung WongQuantum Convolutional Neural NetworksQuantum Circuit Simulation

  11. Fine-Tuning Large Language Models for Quantum Reasoning

    Jun 20, 2026Katherine Ip, Casey R. Myers, Udaya Parampalli +2LLM Fine-TuningRL for Language Model Reasoning

  12. Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance

    Jun 10, 2026Honjar Xing, Yehong Jiang, Xianbang Wang +2Tensor NetworksQuantum Circuit Simulation