The carbon-carbon bonds in the ground states of C 2 and C 2 H 2 , at their equilibrium geometries, are compared by analysing the changes in the off-nucleus magnetic shielding tensor within the space surrounding each of these molecules. A wide range of quantum-chemical approaches, including full-valence CASSCF-GIAO, CCSD(T)-GIAO and CCSDT-GIAO, all with the cc-pVQZ basis set, as well as HF-GIAO and MP2-GIAO, with the cc-pVQZ, cc-pV5Z and cc-pV6Z basis sets, show that the surroundings of the carbon-carbon bond in C 2 are more shielded than those of the carbon-carbon bond in C 2 H 2 . The additional shielding of the carbon-carbon bond in C 2 is found to be due to a larger paramagnetic contribution to component of the shielding tensor which is perpendicular to the molecular axis. The analysis of the off-nucleus shielding data indicates that the carbon-carbon bond in C 2 is "bulkier" and, therefore, of a higher multiplicity, but weaker than the corresponding bond in C 2 H 2 . According to the results of the shielding calculations, the carbon nuclei in C 2 should be much more shielded than those in C 2 H 2 , with 13 C isotropic magnetic shieldings in the ca. 224-227 ppm and ca. 123-125 ppm ranges for C 2 and C 2 H 2 , respectively.

We have demonstrated a prototypical hybrid classical and quantum computational workflow for the quantification of protein-ligand interactions. The workflow combines the density matrix embedding theory (DMET) embedding procedure with the variational quantum eigensolver (VQE) approach for finding molecular electronic ground states. A series of β -secretase (BACE1) inhibitors is rank-ordered using binding energy differences calculated on the latest superconducting transmon (IBM) and trapped-ion (Quantinuum) noisy intermediate scale quantum (NISQ) devices. This is the first application of real quantum computers to the calculation of protein-ligand binding energies. The results shed light on hardware and software requirements which would enable the application of NISQ algorithms in drug design. | INTRODUCTIONThe advent of quantum mechanics at the turn of the 20th century changed the way we look at the physical sciences. For chemistry, the implications were profound, as Dirac famously noted: "the fundamental laws … for the whole of chemistry are thus completely known, and the difficulty lies only in the fact that application of these laws leads to equations that are too complex to be solved" [1]. Indeed, calculations of accurate solutions of the electronic Schrödinger equation, such as the full configuration interaction method (FCI), of molecules scale exponentially with the number of atoms [2], rendering them applicable only to the smallest systems. Practical approximations to FCI, such as, for example, CCSD(T) [3], touted as computational chemistry's gold standard, do exist, but their applicability is limited: single-reference methods like CCSD(T) fail for strongly correlated ("multireference") systems and the formal scaling of CCSD(T) with system size is O(N 7 ), rendering it useful only for relatively small molecules. While the scaling limitation of CCSD(T) can be dramatically reduced via localized methods such as DLPNO [4] at the expense of accuracy, FCI-based methods for strongly correlated systems scale exponentially with the number of correlated electrons and, even for systems small enough to be tractable, require expert knowledge to be applied. Emerging approaches for strongly correlated systems, such as DMRG [5] and selected CI [6] can achieve results close to FCI at lower cost, thereby extending the limit to about 100 orbitals. In most cases, the correlated method is applied to an active space of selected orbitals, the choice of which has traditionally been arbitrary but can also be performed by automated procedures [7,8]. On the other hand, mean-field methods, such as Hartree-Fock (HF) and density functional theory (DFT), which either dispense with electron correlation completely or treat it in an approximate, implicit manner, are routinely applied to organic and inorganic systems of sizes up to few hundred atoms. This is possible due to their relatively low, O N 3 -O N 4 À Á , formal scaling, which could be reduced even to O N ð Þ in approximate implementations for systems comprised of thousands of atoms...

Circuit depth reduction is of critical importance for quantum chemistry simulations on current and near term quantum computers. This issue is tackled by introducing a chemically aware strategy for the unitary coupled cluster ansatz. The objective is to use the chemical description of a system to aid in the synthesis of a quantum circuit. We combine this approach with two flavors of symmetry verification for the reduction of experimental noise. These methods enable the use of Quantinuum’s System Model H1 ion trap quantum computer for a 6-qubit quantum subspace expansion calculation. We present (i) calculations to obtain methane’s optical spectra; (ii) an atmospheric gas reaction simulation involving [CH3⋅—H—OH]‡. Using our chemically aware unitary coupled cluster state-preparation strategy in tandem with state of the art symmetry verification methods, we improve device yield for CH4 at 6 qubits. This is demonstrated by a 90% improvement in two-qubit gate count and a reduction in relative error to 0.2% for electronic energy calculated on System Model H1.