[Paper Review] Accurate quantum-centric simulations of supramolecular interactions

Danil Kaliakin, Akhil Shajan+10 more|arXiv (Cornell University)|Oct 11, 2024

Methane Hydrates and Related Phenomena5 citations

TL;DR

This study demonstrates quantum-centric simulations of noncovalent hydrophilic and hydrophobic interactions in water and methane dimers using sample-based quantum diagonalization (SQD) within a quantum–classical hybrid workflow, achieving agreement with CASCI and close to CCSD(T) in equilibrium regions.

ABSTRACT

We present the first quantum-centric simulations of noncovalent interactions using a supramolecular approach. We simulate the potential energy surfaces (PES) of the water and methane dimers, featuring hydrophilic and hydrophobic interactions, respectively, with a sample-based quantum diagonalization (SQD) approach. Our simulations on quantum processors, using 27- and 36-qubit circuits, are in remarkable agreement with classical methods, deviating from complete active space configuration interaction (CASCI) and coupled-cluster singles, doubles, and perturbative triples (CCSD(T)) within 1 kcal/mol in the equilibrium regions of the PES. Finally, we test the capacity limits of the quantum methods for capturing hydrophobic interactions with an experiment on 54 qubits. These results mark significant progress in the application of quantum computing to chemical problems, paving the way for more accurate modeling of noncovalent interactions in complex systems critical to the biological, chemical and pharmaceutical sciences.

Motivation & Objective

• Motivate accurate modeling of noncovalent interactions pivotal to biology, chemistry, and drug discovery.
• Introduce quantum-centric supramolecular simulations (QCSC/SQD) to study dimer PES.
• Benchmark SQD against classical methods (CASCI, CCSD(T), HCI) to assess accuracy within active spaces.
• Demonstrate scalability of quantum-classical workflows on current quantum hardware for supramolecular problems.

Proposed method

• Adopt a supramolecular approach computing binding energies from E_AB-bound − E_AB-unbound using large active spaces.
• Map the active-space Hamiltonian to qubits via Jordan-Wigner transformation and prepare a LUCJ-based quantum state ansatz.
• Use sample-based quantum diagonalization (SQD) with self-consistent configuration recovery on classical HPC to post-process quantum samples and solve the subspace Hamiltonian.
• Compare SQD results to CASCI, CCSD, CCSD(T), and HCI to benchmark accuracy in water and methane dimers.
• Employ noise mitigation (gate twirling, dynamical decoupling) on IBM Eagle devices and perform energy extrapolation where beneficial.

Experimental results

Research questions

• RQ1 Can quantum-centric SQD accurately reproduce noncovalent interaction energies for hydrophilic (water dimer) and hydrophobic (methane dimer) dimers?
• RQ2 How does SQD perform relative to established classical methods (CASCI, CCSD(T), HCI) across different active spaces and geometries?
• RQ3 To what extent can increasing active space size or sampling improve SQD accuracy, and can extrapolation reduce sampling requirements?
• RQ4 What are the scalability limits of SQD on current quantum hardware for supramolecular problems?
• RQ5 Does the quantum-classical SQD workflow capture essential dispersion and dynamical correlation within feasible qubit counts?

Key findings

• SQD energies for the water dimer (27-qubit) agree with CASCI within near exactness and deviate from CCSD(T) by less than 1 kcal/mol in equilibrium regions.
• For the methane dimer (36-qubit) SQD agrees with CASCI (16e,16o) within 0.005 kcal/mol in the attractive region; full-basis CCSD/CCSD(T) show stronger binding due to dynamical correlation missing in the active space.
• MQD with 54-qubit simulations demonstrates systematic improvement in accuracy with larger sampling and active space expansion; extrapolation techniques can reduce required samples while maintaining accuracy.
• SQD captures noncovalent interactions at a level comparable to classical methods within the chosen active spaces, and results improve as virtual orbitals (3s/3p, potentially 3d shells) are added.
• The study establishes a quantum-centric framework (QCSC/SQD) that leverages classical HPC for subspace diagonalization and quantum hardware for sampling, enabling larger active spaces than previously feasible on hardware.