Publications by authors named "Scott N Genin"

A method for performing variable-width (thawed) Gaussian wavepacket (GWP) variational dynamics on machine-learned potentials is presented. Instead of fitting the potential energy surface (PES), the anharmonic correction to the global harmonic approximation (GHA) is fitted using kernel ridge regression─this is a Δ-machine learning approach. The training set consists of energy differences between ab initio electronic energies and values given by the GHA.

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We present an efficient method for construction of a fully anticommutative set of Pauli generators (elements of the Pauli group) from a commutative set of operators that are composed exclusively from Pauli operators (purely X generators) and sorted by an associated numerical measure, such as absolute energy gradients. Our approach uses the Gauss-Jordan elimination applied to a binary matrix that encodes the set of X generators to bring it to the reduced row-echelon form, followed by the construction of an anticommutative system in a standard basis by means of a modified Jordan-Wigner transformation and returning to the original basis. The algorithm complexity is linear in the size of the X set and quadratic in the number of qubits.

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Two difficulties associated with the computations of thermal vibrational correlation functions are discussed. The first one is the lack of a well-behaved expression that is valid at both high-temperature and → 0 K limits. Specifically, if the partition function and the propagator are considered separately, then thermal vibrational correlation functions may have an indeterminate form 0/0 in the limit → 0 K.

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Here we calculate T →S transition energies in nine phosphorescent iridium complexes using the iterative qubit coupled cluster (iQCC) method to determine if quantum simulations have any advantages over classical methods. These simulations would require a gate-based quantum computer with at least 72 fully-connected logical qubits. Since such devices do not yet exist, we demonstrate the iQCC method using a purpose-built quantum simulator on classical hardware.

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An iterative version of the qubit coupled cluster (QCC) method [I. G. Ryabinkin et al.

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For quantum computing applications, the electronic Hamiltonian for the electronic structure problem needs to be unitarily transformed into a qubit form. We found that mean-field procedures on the original electronic Hamiltonian and on its transformed qubit counterpart can give different results. We establish conditions of when fermionic and qubit mean fields provide the same or different energies.

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Variational quantum eigensolver (VQE) is an efficient computational method promising chemical accuracy in electronic structure calculations on a universal-gate quantum computer. However, such a simple task as computing the electronic energy of a hydrogen molecular cation, H, is not possible for a general VQE protocol because the calculation will invariably collapse to a lower energy of the corresponding neutral form, H. The origin of the problem is that VQE effectively performs an unconstrained energy optimization in the Fock space of the original electronic problem.

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A unitary coupled cluster (UCC) form for the wave function in the variational quantum eigensolver has been suggested as a systematic way to go beyond the mean-field approximation and include electron correlation in solving quantum chemistry problems on a quantum computer. Although being exact in the limit of including all possible coupled cluster excitations, practically, the accuracy of this approach depends on the number and type of terms are included in the wave function parametrization. Another difficulty of UCC is a growth of the number of simultaneously entangled qubits even at the fixed Fermionic excitation rank.

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A parallel plate air lift reactor was used to examine the growth kinetics of mixed culture algal biofilms grown on various materials (acrylic, glass, polycarbonate, polystyrene and cellulose acetate). The growth kinetics of the algal biofilms were non-linear overall and their overall productivities ranged from 1.10-2.

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