Breaking the Treewidth Barrier in Quantum Circuit Simulation with Decision Diagrams
- URL: http://arxiv.org/abs/2510.06775v1
- Date: Wed, 08 Oct 2025 08:58:58 GMT
- Title: Breaking the Treewidth Barrier in Quantum Circuit Simulation with Decision Diagrams
- Abstract summary: This work rigorously analyzes FeynmanDD, a decision diagram-based simulation method proposed in CAV 2025 by a subset of the authors.<n>We show that the method remains efficient if we use the Solovay-Kitaev algorithm to expand arbitrary single-qubit gates to sequences of Hadamard and T gates.
- Score: 8.586836962892443
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Classical simulation of quantum circuits is a critical tool for validating quantum hardware and probing the boundary between classical and quantum computational power. Existing state-of-the-art methods, notably tensor network approaches, have computational costs governed by the treewidth of the underlying circuit graph, making circuits with large treewidth intractable. This work rigorously analyzes FeynmanDD, a decision diagram-based simulation method proposed in CAV 2025 by a subset of the authors, and shows that the size of the multi-terminal decision diagram used in FeynmanDD is exponential in the linear rank-width of the circuit graph. As linear rank-width can be substantially smaller than treewidth and is at most larger than the treewidth by a logarithmic factor, our analysis demonstrates that FeynmanDD outperforms all tensor network-based methods for certain circuit families. We also show that the method remains efficient if we use the Solovay-Kitaev algorithm to expand arbitrary single-qubit gates to sequences of Hadamard and T gates, essentially removing the gate-set restriction posed by the method.
Related papers
- Scalable Graph Coreset Selection via Greedy Sampling [48.91894218306487]
We propose a simple and effective column-selective graph sampling algorithm based on a minimum inner product greedy selection rule.<n>We analyze the algorithm under the block model and show that, when the degree distribution is balanced across nodes, the algorithm achieves proportional sampling to cluster size.
arXiv Detail & Related papers (2026-07-30T02:45:31Z) - Quadratic Sums-of-Powers for Fixed-Parameter Tractable Quantum-Circuit Simulation [17.91644278251664]
We show that the structural quantity most accurately governing the difficulty is the rank-width of the path-variable graph.<n>We give an algorithm that evaluates the amplitude in time that is exponential only in this rank-width and in the circuit size.<n>New method applies to every circuit built from Hadamard and diagonal gates, in particular to circuits over Clifford+T.
arXiv Detail & Related papers (2026-05-28T13:54:42Z) - Explicit Block Encodings of Discrete Laplacians with Mixed Boundary Conditions [45.88028371034407]
Block encoding provides the standard method for accessing matrix data within quantum circuits.<n>We present a unified framework for efficiently block encoding finite-difference discretizations of the Laplacian.
arXiv Detail & Related papers (2026-03-12T19:35:16Z) - Block encoding of sparse matrices with a periodic diagonal structure [67.45502291821956]
We provide an explicit quantum circuit for block encoding a sparse matrix with a periodic diagonal structure.<n>Various applications for the presented methodology are discussed in the context of solving differential problems.
arXiv Detail & Related papers (2026-02-11T07:24:33Z) - Efficient graph-diagonal characterization of noisy states distributed over quantum networks via Bell sampling [0.10486135378491267]
Graph states are an important class of entangled states that serve as a key resource for distributed information processing and communication in quantum networks.<n>We propose a protocol that utilizes a Bell sampling subroutine to characterize the diagonal elements in the graph basis of noisy graph states distributed across a network.
arXiv Detail & Related papers (2025-12-07T04:19:09Z) - Practical Application of the Quantum Carleman Lattice Boltzmann Method in Industrial CFD Simulations [44.99833362998488]
This work presents a practical numerical assessment of a hybrid quantum-classical approach to CFD based on the Lattice Boltzmann Method (LBM)<n>We evaluate this method on three benchmark cases featuring different boundary conditions, periodic, bounceback, and moving wall.<n>Our results confirm the validity of the approach, achieving median error fidelities on the order of $10-3$ and success probabilities sufficient for practical quantum state sampling.
arXiv Detail & Related papers (2025-04-17T15:41:48Z) - A Quantum Genetic Algorithm Framework for the MaxCut Problem [49.59986385400411]
The proposed method introduces a Quantum Genetic Algorithm (QGA) using a Grover-based evolutionary framework and divide-and-conquer principles.<n>On complete graphs, the proposed method consistently achieves the true optimal MaxCut values, outperforming the Semidefinite Programming (SDP) approach.<n>On ErdHos-R'enyi random graphs, the QGA demonstrates competitive performance, achieving median solutions within 92-96% of the SDP results.
arXiv Detail & Related papers (2025-01-02T05:06:16Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Efficient Quantum Circuits for Non-Unitary and Unitary Diagonal Operators with Space-Time-Accuracy trade-offs [1.0749601922718608]
Unitary and non-unitary diagonal operators are fundamental building blocks in quantum algorithms.<n>We introduce a general approach to implement unitary and non-unitary diagonal operators with efficient-adjustable-depth quantum circuits.
arXiv Detail & Related papers (2024-04-03T15:42:25Z) - A multiple-circuit approach to quantum resource reduction with application to the quantum lattice Boltzmann method [39.671915199737846]
We introduce a multiple-circuit algorithm for a quantum lattice Boltzmann method (QLBM) solve of the incompressible Navier--Stokes equations.<n>The presented method is validated and demonstrated for 2D lid-driven cavity flow.
arXiv Detail & Related papers (2024-01-20T15:32:01Z) - Forward and Backward Constrained Bisimulations for Quantum Circuits using Decision Diagrams [3.788308836856851]
We develop efficient methods for the simulation of quantum circuits on classic computers.
In particular, we show that constrained bisimulation can boost decision-diagram-based quantum circuit simulation by several orders of magnitude.
arXiv Detail & Related papers (2023-08-18T12:40:47Z) - Graphix: optimizing and simulating measurement-based quantum computation
on local-Clifford decorated graph [0.0]
We introduce an open-source software library Graphix, which optimize and simulates measurement-based quantum computation (MBQC)
By combining the measurement calculus with an efficient graph state simulator, Graphix allows the classical preprocessing of Pauli measurements in the measurement patterns.
arXiv Detail & Related papers (2022-12-22T18:58:20Z) - Simulation Paths for Quantum Circuit Simulation with Decision Diagrams [72.03286471602073]
We study the importance of the path that is chosen when simulating quantum circuits using decision diagrams.
We propose an open-source framework that allows to investigate dedicated simulation paths.
arXiv Detail & Related papers (2022-03-01T19:00:11Z) - Simulation of Quantum Computing on Classical Supercomputers [23.350853237013578]
We propose a scheme based on cutting edges of undirected graphs.
This scheme cuts edges of undirected graphs with large tree width to obtain many undirected subgraphs.
It can simulate 120-qubit 3-regular QAOA algorithm on 4096-core supercomputer.
arXiv Detail & Related papers (2020-10-28T13:26:41Z) - Multipole Graph Neural Operator for Parametric Partial Differential
Equations [57.90284928158383]
One of the main challenges in using deep learning-based methods for simulating physical systems is formulating physics-based data.
We propose a novel multi-level graph neural network framework that captures interaction at all ranges with only linear complexity.
Experiments confirm our multi-graph network learns discretization-invariant solution operators to PDEs and can be evaluated in linear time.
arXiv Detail & Related papers (2020-06-16T21:56:22Z)
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.