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Financial services is a prospect industry where unlocked near-term quantum utility could yield profitable potential, and, in particular, quantum machine learning algorithms could potentially benefit businesses by improving the quality of predictive models. Quantum kernel methods have demonstrated success in financial, binary classification tasks, like fraud detection, and avoid issues found in variational quantum machine learning approaches. However, choosing a suitable quantum kernel for a classical dataset remains a challenge. We propose a hybrid, quantum multiple kernel learning (QMKL) methodology that can improve classification quality over a single kernel approach. We test the robustness of QMKL on several financially relevant datasets using both fidelity and projected quantum kernel approaches. We further demonstrate QMKL on quantum hardware using an error mitigation pipeline and show the benefits of QMKL in the large qubit regime.

In this work, we present a Gauss-Newton based quantum algorithm (GNQA) for combinatorial optimization problems that, under optimal conditions, rapidly converges towards one of the optimal solutions without being trapped in local minima or plateaus. Quantum optimization algorithms have been explored for decades, but more recent investigations have been on variational quantum algorithms, which often suffer from the aforementioned problems. Our approach mitigates those by employing a tensor product state that accurately represents the optimal solution, and an appropriate function for the Hamiltonian, containing all the combinations of binary variables. Numerical experiments presented here demonstrate the effectiveness of our approach, and they show that GNQA outperforms other optimization methods in both convergence properties and accuracy for all problems considered here. Finally, we briefly discuss the potential impact of the approach to other problems, including those in quantum chemistry and higher order binary optimization., Comment: 21 pages, 8 figures

High-order harmonic generation (HHG) in aligned linear molecules can offer valuable information about strong-field interactions in lower-lying molecular orbitals, but extracting this information is difficult for three-dimensional molecular geometries. Our measurements of the asymmetric top SO2 show large axis dependencies, which change with harmonic order. The analysis shows that these spectral features must be due to field ionization and recombination from multiple orbitals during HHG. We expect that HHG can probe orbital dependencies using this approach for a broad class of asymmetric-top molecules., Comment: Main text and supplemental material

Nonradiative energy dissipation in electronically excited polyatomic molecules proceeds through conical intersections, loci of degeneracy between electronic states. We observe a marked enhancement of laser-induced double ionization in the vicinity of a conical intersection during a non-radiative transition. We measured double ionization by detecting the kinetic energy of ions released by laser-induced strong-field fragmentation during the ring-opening transition between 1,3-cyclohexadiene and 1,3,5-hexatriene. The enhancement of the double ionization correlates with the conical intersection between the HOMO and LUMO orbitals., Comment: The manuscript has been accepted for publication at the Journal of Chemical Physics (October 28 2013)

An important goal in molecular physics and chemistry today is to obtain structure-dependent information about molecular function to obtain a deeper understanding into chemical reactions. However, until now, asymmetric tops, which comprise the widest and most general class of molecules, remain principally unexplored. This gap is particularly evident in high harmonic generation (HHG). HHG has successfully obtained structural information about electron hole pairs or orbitals for simple linear molecules. Unfortunately, for more complicated molecules, the emission from different molecular directions interfere, concealing individual angular signatures. Here we introduce a method to extract orientation-dependent information from asymmetric tops and apply it to the sulfur dioxide (SO2) molecule. We use the rotational revival structure to decompose the angular contributions of HHG emission. This method also extends HHG-based tomographic imaging into three dimensions and makes it applicable to a much wider class of systems than previously envisioned. Our results suggest that HHG is a powerful tool to probe electron orbital structure and dynamics of complex molecules., Comment: Main text and supplementary information