aiqbench analysis
This paper presents an LLM-based agent that autonomously maps phase diagrams of quantum spin Hamiltonians by planning, running DMRG calculations, and cross-checking them against exact diagonalization. The headline result is a recovery of known phases in a recently published spin-1 chain without prior hints, but the benchmark is a single system, so generalization to genuinely unknown physics remains untested. Readers should watch whether the agent's workflow holds up on harder cases and whether its finite-size analyses are reproducible across independent runs.
QuantumMatterExplorer: An Autonomous Agent for the Discovery of Quantum Many-Body Phases of Matter
arXiv:2610.32001v1 Announce Type: new Abstract: Mapping the phase diagram of a new quantum Hamiltonian demands expert judgement at every step: which observables to measure, where in parameter space to compute, or how to separate genuine order from finite-size artefacts. We introduce QuantumMatterExplorer, an autonomous agent that takes only a Hamiltonian and its parameter ranges as input and returns a phase diagram obtained from its own numerical simulations. The agent is built on a large language model and is designed to reason over a long horizon, maintain a persistent written plan, and drive a set of computational tools, with density-matrix renormalization group (DMRG) calculations cross-checked against independent exact diagonalization. The agent combines a large language model with persistent planning, a library of expert computational-physics skills, and density-matrix renormalization group calculations validated against independent exact diagonalization. It adaptively selects quantities to evaluate, refines parameter scans, and performs finite-size analyses as evidence accumulates. After validating the approach on Hamiltonians with known transitions, we challenge it with a frustrated bilinear-biquadratic spin-1 chain with single-ion anisotropy, whose two-dimensional phase diagram was published only recently and is unlikely to appear in any training corpus. Given no hint about the expected physics, the agent proposes which observables to probe, and identifies candidate N\'eel, Haldane and dimerized phases, in broad agreement with the reference phase diagram. It also detects incommensurate correlations and infers the frustration-dependent geometry of the phase boundaries, all within an eight-hour compute budget. These results open a practical route to the autonomous computational discovery of new phases of quantum matter.
Abstract:Mapping the phase diagram of a new quantum Hamiltonian demands expert judgement at every step: which observables to measure, where in parameter space to compute, or how to separate genuine order from finite-size artefacts. We introduce QuantumMatterExplorer, an autonomous agent that takes only a Hamiltonian and its parameter ranges as input and returns a phase diagram obtained from its own numerical simulations. The agent is built on a large language model and is designed to reason over a long horizon, maintain a persistent written plan, and drive a set of computational tools, with density-matrix renormalization group (DMRG) calculations cross-checked against independent exact diagonalization. The agent combines a large language model with persistent planning, a library of expert computational-physics skills, and density-matrix renormalization group calculations validated against independent exact diagonalization. It adaptively selects quantities to evaluate, refines parameter scans, and performs finite-size analyses as evidence accumulates. After validating the approach on Hamiltonians with known transitions, we challenge it with a frustrated bilinear-biquadratic spin-1 chain with single-ion anisotropy, whose two-dimensional phase diagram was published only recently and is unlikely to appear in any training corpus. Given no hint about the expected physics, the agent proposes which observables to probe, and identifies candidate Néel, Haldane and dimerized phases, in broad agreement with the reference phase diagram. It also detects incommensurate correlations and infers the frustration-dependent geometry of the phase boundaries, all within an eight-hour compute budget. These results open a practical route to the autonomous computational discovery of new phases of quantum matter.
42 pages, 10 figures and 3 tables in the main manuscript. Includes a 13-page agent-generated research note
Quantum Physics (quant-ph); Strongly Correlated Electrons (cond-mat.str-el)
arXiv:2610.32001 [quant-ph]
(or arXiv:2610.32001v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.32001 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 365 words
