一句话：提出 QCOP 抽象、约束语言 Masq 与编译器 CoTenN，将带硬约束的量子优化问题自动编译为张量网络并求基态。
物理系统模拟常需寻找哈密顿量的基态，且须满足对称性等硬约束。基于张量网络的本征求解器（如 DMRG）是经典计算机上的常用方法，但将约束优化问题编码为张量网络目前依赖人工，难以为新问题高效寻找编码。本文提出量子约束优化问题（QCOP）抽象，并给出首个可用张量网络实现的 QCOP 约束编程语言 Masq，以及自动将 Masq 程序映射为张量网络的编译器 CoTenN。作者用两个物理问题在 Masq 中建模，并用 CoTenN 求基态；结果显示生成的张量网络通常优于现有问题表述，跨运行取得 2.05×–53.32× 总加速，对无约束问题截断误差最多降低 2.49×10^7 倍。
Simulation of physics problems is one of the most important use cases of quantum computing. For this class of problems, the goal is typically to find the minimum energy state, or the ground state, of a physical system’s Hamiltonian. These problems frequently have constraints, such as symmetry conditions, which must also be satisfied. To solve such problems, researchers in computational physics use quantum-inspired algorithms that execute on classical computers. In particular, tensor network-based eigensolvers such as DMRG have become popular. However, to use these eigensolvers, the constrained optimization problem must first be encoded as a tensor network that implements a low-rank decomposition of the system’s Hamiltonian and state vector. These tensor network encodings are highly flexible, allowing for variables with ≥ 2 quantum states and supporting efficient constraint encodings that directly constrain the state vector. A critical challenge to developing tensor network-based encodings is that, currently, the encoding process is manual; significant effort is required to identify an efficient encoding for a new physics problem. In this work, we introduce a quantum constrained optimization problem (QCOP), a general abstraction for describing minimization problems over quantum variables that are subject to hard constraints. We present Masq, the first constraint programming language for QCOPs implementable with tensor networks, and CoTenN, a compiler that automatically maps QCOPs specified with Masq programs to tensor networks. To demonstrate the utility of Masq and CoTenN, we formulate two physics problems in Masq and then use CoTenN to find their ground states. We find the CoTenN-generated tensor networks generally outperform SOTA problem formulations, providing between 2.05×–53.32× total speedups across runs for QCOPs and yielding up to 2.49 · 10 7 × lower truncation errors for otherwise unconstrained problems.
@article{SharmaPDA26,
title = {CoTenN: Constrained Optimization with Tensor Networks},
author = {Ritvik Sharma and Cheng Peng and Siddharth Dangwal and Sara Achour},
journal = {Proceedings of the ACM on Programming Languages},
volume = {10},
number = {PLDI},
year = {2026},
doi = {10.1145/3808272}
}