Unbiasing fermionic auxiliary-field quantum Monte Carlo with matrix product state trial wavefunctions

Physical Review Research, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 10, 2025

In this work, we report, for the first time, an implementation of fermionic auxiliary-field quantum Monte Carlo (AFQMC) using matrix product state (MPS) trial wavefunctions, dubbed MPS-AFQMC. Calculating overlaps between MPS and arbitrary Slater determinants up to a multiplicative error, crucial subroutine in MPS-AFQMC, is proven be #P-hard. Nonetheless, tested several promising heuristics successfully improving phaseless AFQMC energies. We also proposed way evaluate local energy force bias evaluations free operators. This allows larger basis set calculations without significant overhead. showcase utility our approach on one- two-dimensional hydrogen lattices, even when itself struggles obtain high accuracy. Our work offers new tools that can solve currently challenging electronic structure problems with future improvements. Published by American Physical Society 2025

Language: Английский

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Evaluating a quantum-classical quantum Monte Carlo algorithm with Matchgate shadows

Physical Review Research, Journal Year: 2024, Volume and Issue: 6(4)

Published: Oct. 24, 2024

Solving the electronic structure problem of molecules and solids to high accuracy is a major challenge in quantum chemistry condensed matter physics. The rapid emergence development computers offer promising route systematically tackle this problem. Recent work by [Huggins , ] proposed hybrid quantum-classical Monte Carlo (QC-QMC) algorithm using Clifford shadows determine ground state Fermionic Hamiltonian. This approach displayed inherent noise resilience potential for improved compared its purely classical counterpart. Nevertheless, use introduces an exponentially scaling postprocessing cost. In work, we investigate QC-QMC scheme utilizing recently developed Matchgate technique [], which removes aforementioned exponential bottleneck. We observe from experiments on hardware that inherently robust. show has more subtle origin than case shadows. find postprocessing, while asymptotically efficient, requires hours runtime thousands CPUs even smallest chemical systems, presenting scalability algorithm. Published American Physical Society 2024

Language: Английский

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Quantum Computing Approach to Fixed-Node Monte Carlo Using Classical Shadows

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 5, 2025

Quantum Monte Carlo (QMC) methods are powerful approaches for solving electronic structure problems. Although they often provide high-accuracy solutions, the precision of most QMC is ultimately limited by trial wave function that must be used. Recently, an approach has been demonstrated to allow use functions prepared on a quantum computer [Huggins et al., Unbiasing fermionic with computer. Nature 2022, 603, 416] in auxiliary-field (AFQMC) method using classical shadows estimate required overlaps. However, this exponential post-processing step construct these overlaps when performing obtained random Clifford circuits. Here, we study avoid scaling fixed-node based full configuration interaction Carlo. This applied local unitary cluster Jastrow ansatz. We consider H4, ferrocene, and benzene molecules up 12 qubits as examples. Circuits compiled native gates typical near-term architectures, assess impact circuit-level depolarizing noise method. also comparison AFQMC approaches, demonstrating more robust errors, although extrapolations energy reduce discrepancy. can used reach chemical accuracy, sampling cost achieve high even small active spaces, suggesting caution about prospect outperforming conventional approaches.

Language: Английский

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Contextual Subspace Auxiliary-Field Quantum Monte Carlo: Improved Bias with Reduced Quantum Resources

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

Using trial wave functions prepared on quantum devices to reduce the bias of auxiliary-field Monte Carlo (QC-AFQMC) has established itself as a promising hybrid approach simulation strongly correlated many body systems. Here, we further required resources by decomposing function into classical and parts, respectively treated devices, within contextual subspace projection formalism. Importantly, show that our algorithm is compatible with recently developed matchgate shadow protocol for efficient overlap calculation in QC-AFQMC. Investigating nitrogen dimer reductive decomposition ethylene carbonate lithium-based batteries, observe method outperforms number ground state energy computations, while reaching chemical precision less than half original qubits.

Language: Английский

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Unified framework for matchgate classical shadows

npj Quantum Information, Journal Year: 2025, Volume and Issue: 11(1)

Published: April 16, 2025

Abstract Estimating quantum fermionic properties is a computationally difficult yet crucial task for the study of electronic systems. Recent developments have begun to address this challenge by introducing classical shadows protocols relying on sampling Fermionic Gaussian Unitaries (FGUs): class transformations in space which can be conveniently mapped matchgates circuits. The different proposed literature use sub-ensembles orthogonal group O(2 n ) FGUs associated. We propose an approach that unifies these protocols, proving their equivalence, and deriving from it optimal scheme. begin demonstrating first three moments FGU ensemble associated with SO(2 its intersection Clifford are equal, generalizing result known addressing question raised previous works. Building proof, we establish equivalence between resulting ensembles analyzed literature. Finally, our results, scheme small sub-ensemble circuits terms number gates inherits performances guarantees ensembles.

Language: Английский

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Self-Refinement of Auxiliary-Field Quantum Monte Carlo via Non-Orthogonal Configuration Interaction

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

For optimal accuracy, auxiliary-field quantum Monte Carlo (AFQMC) requires trial states consisting of multiple Slater determinants. We develop an efficient algorithm to select the determinants from AFQMC random walk eliminating need for other methods. When contribute significantly nonorthogonal configuration interaction energy, we include them in state. These refined wave functions reduce phaseless bias and sampling variance local energy estimator. With 100 200 determinants, lower error by up a factor 10 second-row elements that are not accurately described with Hartree-Fock function. HEAT set, improve average within chemical accuracy. benzene, largest studied system, 80% 214 find 10-fold increase time solution. show errors prevail systems static correlation or strong spin contamination. such systems, improved enable stable free-projection calculations, achieving accuracy even strongly correlated regime.

Language: Английский

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Walking through Hilbert Space with Quantum Computers

Chemical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: May 2, 2025

Computations of chemical systems' equilibrium properties and nonequilibrium dynamics have been suspected being a "killer app" for quantum computers. This review highlights the recent advancements algorithms tackling complex sampling tasks in key areas computational chemistry: ground state, thermal state properties, calculations. We broad range algorithms, from hybrid quantum-classical to fully quantum, focusing on traditional Monte Carlo family, including Markov chain Carlo, variational projector path integral etc. also cover other relevant techniques involving tasks, such as quantum-selected configuration interaction, minimally entangled typical states, entanglement forging, Carlo-flavored Lindbladian dynamics. provide comprehensive overview these algorithms' classical counterparts, detailing their theoretical frameworks discussing potentials challenges achieving advantages.

Language: Английский

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Improved modularity and new features in ipie: Toward even larger AFQMC calculations on CPUs and GPUs at zero and finite temperatures

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(16)

Published: Oct. 25, 2024

ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package that has undergone substantial improvements since its initial release [Malone et al., J. Chem. Theory Comput. 19(1), 109–121 (2023)]. This paper outlines the improved modularity and new capabilities implemented in ipie. We highlight ease of incorporating different trial walker types seamless integration with external libraries. enable distributed Hamiltonian simulations large systems otherwise would not fit on single central processing unit node or graphics (GPU) card. development enabled us to compute interaction energy benzene dimer 84 electrons 1512 orbitals multi-GPUs. Using CUDA cupy for NVIDIA GPUs, supports GPU-accelerated multi-slater determinant wavefunctions [Huang al. arXiv:2406.08314 (2024)] efficient highly accurate large-scale systems. allows near-exact ground state energies multi-reference clusters, [Cu2O2]2+ [Fe2S2(SCH3)4]2−. also describe implementations free projection AFQMC, finite temperature AFQMC electron–phonon systems, automatic differentiation calculating physical properties. These advancements position as leading platform research chemistry, facilitating more complex ambitious computational method their applications.

Language: Английский

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Modeling Heterogeneous Catalysis Using Quantum Computers: An Academic and Industry Perspective

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 29, 2024

Heterogeneous catalysis plays a critical role in many industrial processes, including the production of fuels, chemicals, and pharmaceuticals, research to improve current catalytic processes is important make chemical industry more sustainable. Despite its importance, challenge identifying optimal catalysts with required activity selectivity persists, demanding detailed understanding complex interactions between reactants at various length time scales. Density functional theory (DFT) has been workhorse modeling heterogeneous for than three decades. While DFT instrumental, this review explores application quantum computing algorithms catalysis, which could bring paradigm shift our approach interfaces. Bridging academic perspectives by focusing on emerging materials, such as multicomponent alloys, single-atom catalysts, magnetic we delve into limitations capturing strong correlation effects spin-related phenomena. The also presents their applications relevant showcase advancements field. Additionally, embedding strategies where handle strongly correlated regions, while traditional chemistry address remainder, thereby offering promising large-scale modeling. Looking forward, ongoing investments academia reflect growing enthusiasm computing's potential research. concludes envisioning future seamlessly integrate workflows, propelling us new era computational reshaping landscape catalysis.

Language: Английский

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