CT-bMPS: Approximate Tensor Network Contraction with Boundary MPS on Contraction Trees

Hidetaka Manabe; Feng Pan

Home/Archive/arXiv

arXiv · 2610.06574

CT-bMPS: Approximate Tensor Network Contraction with Boundary MPS on Contraction Trees

Hidetaka Manabe·Feng Pan

View original source ↗Read source document ↗Export metadata ↓

Abstract

We introduce contraction tree boundary matrix product states (CT-bMPS), a scalable approximate contraction method for general closed tensor networks. CT-bMPS represents the boundary tensor and its complementary environment at each edge of a contraction tree as matrix product states. This representation allows local compression based on a reduced transition matrix to incorporate environment information along arbitrary contraction trees, while keeping the time and memory costs polynomial in the network size and bond dimension. We benchmark the method on a variety of models and applications, including Ising partition functions, random tensor networks, kicked Ising dynamics, and quantum error correction decoding. These benchmarks demonstrate improved accuracy and computational efficiency over existing general-purpose approximate contraction methods, together with the ability to handle larger and more complex networks. Optimized contraction trees and environment updates substantially improve accuracy without increasing the bond dimension. These results establish CT-bMPS as a general and scalable approximation framework with flexible contraction orders and environment-based compression for a wide range of applications.

Explore related subjects

quant-ph

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Hidetaka Manabe, Feng Pan. 2026-10-05. CT-bMPS: Approximate Tensor Network Contraction with Boundary MPS on Contraction Trees. https://arxiv.org/abs/2610.06574

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The Concept of Entropic Time: A Preliminary Discussion

The deep connection between entropy and information is discussed in terms of both classical and quantum physics. The mechanism of information transfer between systems via entanglement is explored in the context of decoherence theory. The concept of entropic time is then introduced on the basis of information acquisition, which is argued to be effectively irreversible and consistent with both the Second Law of Thermodynamics and our psychological perception of time. This is distinguished from the notion of parametric time, which serves as the temporal parameter for the unitary evolution of a physical state in non-relativistic quantum mechanics. The interpretation of these ideas in terms of both subjective and objective collapse models is also discussed. It is shown that energy is conserved under subjective collapse schemes whereas, in general, under objective collapse it is not. This is consistent with the fact that the latter is inherently non-unitary and that energy conservation arises out of time symmetry in the first place.

quant-ph↗

arXiv2026

Tunable spectral correlations of highly multimode visible light via broadband quantum frequency conversion

Multimode squeezed states of light are a resource for achieving quantum advantage in computing and sensing, where spatial or temporal modes have been the experimental norm. In our experiments, we generated highly frequency-multimode infrared quantum light, and show how adiabatic frequency conversion can be used to convert the quantum state to visible wavelengths, while concurrently manipulating the joint spectrum by realizing a configurable many-port frequency-domain-beamsplitter unitary transformation. We report near-unity-efficiency quantum frequency conversion over a bandwidth >45 THz, which allowed us to measure the state with an electron-multiplying CCD (EMCCD) camera-based spectrometer, at non-cryogenic temperatures. The parametric amplification and conversion of >400 frequency modes yielded an overall mean of approximately 700 visible photons per shot, and photon statistics consistent with squeezing. Our work shows how many-mode quantum states of light can be generated, manipulated, and measured with efficient use of hardware resources, motivating the use of frequency encoding in quantum optics.

quant-ph↗

arXiv2026

Quantum convolutional neural networks for jet images classification

Recently, interest in quantum computing has significantly increased, driven by its potential advantages over classical techniques. Quantum machine learning (QML) exemplifies one of the important quantum computing applications that are expected to surpass classical machine learning in a wide range of instances. This paper addresses the performance of QML in the context of high-energy physics (HEP). As an example, we focus on the top-quark tagging, for which classical convolutional neural networks (CNNs) have been effective but fall short in accuracy when dealing with highly energetic jet images. In this paper, we use a quantum convolutional neural network (QCNN) for this task and compare its performance with CNN using a classical noiseless simulator. We compare various setups for the QCNN, varying the convolutional circuit, type of encoding, loss function, and batch sizes. For every quantum setup, we design a similar setup to the corresponding classical model for a fair comparison. Our results indicate that, using a classical simulator, QCNN with proper setups tend to perform better than their CNN counterparts, especially when the convolution block has a lower number of parameters. For the higher parameter regime, the QCNN circuit was adjusted according to the dimensional expressivity analysis (DEA) to lower the parameter count while preserving its optimal structure. The DEA circuit demonstrated improved results over the comparable classical CNN model.

quant-ph↗