Approximate simulation of complex quantum circuits using sparse tensors
- URL: http://arxiv.org/abs/2602.04011v1
- Date: Tue, 03 Feb 2026 20:58:32 GMT
- Title: Approximate simulation of complex quantum circuits using sparse tensors
- Authors: Benjamin N. Miller, Peter K. Elgee, Jason R. Pruitt, Kevin C. Cox,
- Abstract summary: We introduce a method to approximately simulate quantum circuits using sparse tensors.<n>We show that the data structure and contraction algorithm are efficient, leading to expected runtime scalings versus qubit number and circuit depth.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The study of quantum circuit simulation using classical computers is a key research topic that helps define the boundary of verifiable quantum advantage, solve quantum many-body problems, and inform development of quantum hardware and software. Tensor networks have become forefront mathematical tools for these tasks. Here we introduce a method to approximately simulate quantum circuits using sparsely-populated tensors. We describe a sparse tensor data structure that can represent quantum states with no underlying symmetry, and outline algorithms to efficiently contract and truncate these tensors. We show that the data structure and contraction algorithm are efficient, leading to expected runtime scalings versus qubit number and circuit depth. Our results motivate future research in optimization of sparse tensor networks for quantum simulation.
Related papers
- 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) - Tensor Quantum Programming [0.0]
We develop an algorithm that encodes Matrix Product Operators into quantum circuits with a depth that depends linearly on the number of qubits.
It demonstrates effectiveness on up to 50 qubits for several frequently encountered in differential equations, optimization problems, and quantum chemistry.
arXiv Detail & Related papers (2024-03-20T10:44:00Z) - Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval [1.6421520075844793]
A prime example is the weight optimization of laminated composite materials, which to this day remains a formidable problem.
The rapidly developing field of quantum computation may offer novel approaches for addressing these intricate problems.
arXiv Detail & Related papers (2024-02-09T15:01:56Z) - Tensor Networks or Decision Diagrams? Guidelines for Classical Quantum
Circuit Simulation [65.93830818469833]
tensor networks and decision diagrams have independently been developed with differing perspectives, terminologies, and backgrounds in mind.
We consider how these techniques approach classical quantum circuit simulation, and examine their (dis)similarities with regard to their most applicable abstraction level.
We provide guidelines for when to better use tensor networks and when to better use decision diagrams in classical quantum circuit simulation.
arXiv Detail & Related papers (2023-02-13T19:00:00Z) - On the quantum simulation of complex networks [0.0]
Continuous-time quantum walk algorithms assume that we can simulate the dynamics of quantum systems where the Hamiltonian is given by the adjacency matrix of the graph.
We extend the state-of-the-art results on quantum simulation to graphs that contain a small number of hubs, but that are otherwise sparse.
arXiv Detail & Related papers (2022-12-12T18:55:31Z) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem.
The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment.
We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
arXiv Detail & Related papers (2022-04-18T21:45:13Z) - TensorLy-Quantum: Quantum Machine Learning with Tensor Methods [67.29221827422164]
We create a Python library for quantum circuit simulation that adopts the PyTorch API.
Ly-Quantum can scale to hundreds of qubits on a single GPU and thousands of qubits on multiple GPU.
arXiv Detail & Related papers (2021-12-19T19:26:17Z) - An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian
Simulation [55.41644538483948]
Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in chip size and error rates.
We derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions.
The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond $mathcalO(103)$ spins.
arXiv Detail & Related papers (2021-08-06T19:38:03Z) - Verifying Random Quantum Circuits with Arbitrary Geometry Using Tensor
Network States Algorithm [0.0]
Algorithm is up to $2$ orders of magnitudes faster than Sch$ddottexto$dinger-Feynman algorithm.
We simulate larger random quantum circuits up to $104$ qubits, showing that this algorithm is an ideal tool to verify relatively shallow quantum circuits on near-term quantum computers.
arXiv Detail & Related papers (2020-11-05T02:20:56Z) - Simple heuristics for efficient parallel tensor contraction and quantum
circuit simulation [1.4416132811087747]
We propose a parallel algorithm for the contraction of tensor networks using probabilistic models.
We apply the resulting algorithm to the simulation of random quantum circuits.
arXiv Detail & Related papers (2020-04-22T23:00:42Z)
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.