arXiv subjects

Max Haas

Publications and source records attributed to Max Haas.

3 recordsLinked to original sources

arXiv2026

Efficient Operator Selection and Warm-Start Strategy for Excitations in Variational Quantum Eigensolvers

We present a novel approach for efficient preparation of electronic ground states, leveraging the optimizer ExcitationSolve [J\"ager et al., Comm. Phys. (2025)] and established variational quantum eigensolver-based operator selection methods, such as Energy Sorting (ES). By combining these tools, we demonstrate a computationally efficient protocol that enables the construction of an approximate ground state from a unitary coupled cluster ansatz via a single sweep over the operator pool. Utilizing efficient classical pre-processing to select the majority of relevant operators, this approach reduces the computational complexity associated with traditional variational quantum eigensolver (VQE) optimization methods. We further show that second-order Epstein-Nesbet (EN2) perturbation theory emerges as the first-order Taylor expansion of our protocol in terms of a correlation measure, clarifying why our approach provides a more robust initial guess for the ground state in strongly correlated regimes. We also find that second-order M{\o}ller-Plesset perturbation (MP2) theory, which is widely used for unitary coupled cluster (UCC) initialization, performs worse than both EN2 and our protocol. Furthermore, we show that our method can be seamlessly integrated with one-variational-parameter couple exchange operators, thereby further reducing the number of required CNOT operations. Overall, we empirically observe a quadratic convergence speedup beyond state-of-the-art methods, advancing the preparation of high-fidelity electronic ground states - one of the cornerstones of meaningful electronic structure calculations in the noisy intermediate-scale quantum computing (NISQ) era, and a prerequisite for fault-tolerant quantum computing (FTQC) algorithms such as quantum phase estimation.

quant-ph↗

arXiv2025

Domain-wall driven suppression of thermal conductivity in a ferroelectric polycrystal

A common strategy for reducing thermal conductivity of polycrystalline systems is to increase the number of grain boundaries. Indeed, grain boundaries enhance the probability of phonon scattering events, which has been applied to control the thermal transport in a wide range of materials, including hard metals, diamond, oxides and 2D systems such as graphene. Here, we report the opposite behavior in improper ferroelectric ErMnO3 polycrystals, where the thermal conductivity decreases with increasing grain size. We attribute this unusual relationship between heat transport and microstructure to phonon scattering at ferroelectric domain walls. The domain walls are more densely packed in larger grains, leading to an inversion of the classical grain-boundary-dominated transport behavior. Our findings open additional avenues for microstructural engineering of materials for thermoelectric and thermal management applications, enabling simultaneous control over mechanical, electronic, and thermal properties.

cond-mat.mtrl-sci↗

arXiv2024

Fast gradient-free optimization of excitations in variational quantum eigensolvers

Finding molecular ground states and energies with variational quantum eigensolvers is central to chemistry applications on quantum computers. Physically motivated ans\"atze based on excitation operators respect physical symmetries, but existing quantum-aware optimizers, such as Rotosolve, have been limited to simpler operator types. To fill this gap, we introduce ExcitationSolve, a fast quantum-aware optimizer that is globally-informed, gradient-free, and hyperparameter-free. ExcitationSolve extends these optimizers to parameterized unitaries with generators $G$ of the form $G^3=G$ exhibited by excitation operators in approaches such as unitary coupled cluster. ExcitationSolve determines the global optimum along each variational parameter using the same quantum resources that gradient-based optimizers require for one update step. We provide optimization strategies for both fixed and adaptive variational ans\"atze, along with generalizations for simultaneously selecting and optimizing multiple excitations. On molecular ground state energy benchmarks, ExcitationSolve outperforms state-of-the-art optimizers by converging faster, achieving chemical accuracy for equilibrium geometries in a single parameter sweep, yielding shallower adaptive ans\"atze and remaining robust to real hardware noise. By uniting physical insight with efficient optimization, ExcitationSolve paves the way for scalable quantum chemistry calculations.

quant-ph↗