Quantum Chemistry

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A four-player potential game for barren-plateau-aware quantum ansatz design
Apr 23, 2026Rubén Darío GuerreroQuantum Chemistry Barren Plateaus
Parametrized-QC-Graphs Project
Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory
Apr 22, 2026Roman Remme, Fred A. HamprechtSurrogate Modeling Quantum Chemistry
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University
A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States
Apr 17, 2026Daran Sun, Bowen Kan, Haoquan Long +13GPU Kernel Optimization GPU Acceleration
Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences · University of Science and Technology of China
Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory
Apr 10, 2026Siqi Chen, Zhiqiang Wang, Yili Shen +8Graph Neural Networks Machine Learning Interatomic Potentials
Department of Chemical and Biomolecular Engineering, University of Massachusetts Amherst, Amherst, MA 01003 · Department of Chemistry, University of Massachusetts Amherst, Amherst, MA 01003 · Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431 +5
An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
Date pendingAdam Foster, Zeno Schätzle, P. Bernát Szabó +7Quantum Chemistry Quantum Machine Learning
Microsoft Research AI for Science · Freie Universität Berlin
El Agente Quntur: A research collaborator agent for quantum chemistry
Date pendingJuan B. Pérez-Sánchez, Yunheng Zou, Jorge A. Campos-Gonzalez-Angulo +12AI Agents for Scientific Discovery Quantum Chemistry
Department of Chemistry, University of Toronto, 80 St. George St., Toronto, ON M5S 3H6, Canada · Vector Institute for Artificial Intelligence, W1140-108 College St., Schwartz Reisman Innovation Campus, Toronto, ON M5G 0C6, Canada · Acceleration Consortium, 700 University Ave., Toronto, ON M7A 2S4, Canada +6
A Density-Matrix Framework for Electronic-Structure Analysis of Electrolytes for Lithium Batteries
Date pendingMingkang Liu, Huize Yu, Lei ShenQuantum Chemistry Materials Science
Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore · National University of Singapore (Chongqing) Research Institute, Chongqing 401120, China · National University of Singapore Guangzhou Research Translation and Innovation Institute, Guangzhou, Guangdong 510700, China

Apr 23, 2026, quant-phJ/K move · Enter open · S save

A four-player potential game for barren-plateau-aware quantum ansatz design

Rubén Darío Guerrero

Parametrized-QC-Graphs Project

Quantum Chemistry Barren Plateaus Potential Games Variational Quantum Circuits Quantum Circuit Trainability

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We cast the design of parameterized quantum circuits as a four-player potential game whose state is a circuit directed acyclic graph (DAG) and whose players encode trainability, non-stabilizerness, task performance, and hardware cost. Per-player restricted action sets factorize the move space into append, remove, retype, and rewire operations; a block-coordinate

\varepsilon

-Nash residual

δ_\text{Nash}

certifies that no single player can improve unilaterally. A single weight sweep on MaxCut

K_4

traces a Pareto frontier from a Clifford endpoint

(M_2/n,\langle H\rangle)=(0,4.00)

to a non-Clifford endpoint

(0.48,3.30)

. On three four-qubit hardware topologies (heavy-hex,

2\times 2

grid, Rydberg all-to-all), Nash search achieves the highest mean potential; on the

2\times 2

grid Nash reaches the theoretical ceiling

Φ_\text{max}=4.10

on two of five seeds while the simulated-annealing baseline does so on one; paired Wilcoxon tests over five seeds cannot reject the null on any single topology (

p\ge 0.22

). On LiH/STO-3G, seeding Nash from a 58-gate Givens-doubles ansatz produces a 48-operation, depth-25 circuit retaining

97.7\%

of the correlation energy while simultaneously reducing gate count, increasing non-stabilizerness, and controlling trainability. The framework is complementary to energy-only searches such as ADAPT-VQE and k-UpCCGSD, which reach chemical accuracy with fewer operations but do not optimize the other three axes.

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Latest papers 37