Peruzzo, AlbertoMcClean, Jarrod RyanShadbolt, PeterYung, Man-HongZhou, Xiao-QiLove, PeterAspuru-Guzik, AlanO’Brien, Jeremy L.2014-08-072014Peruzzo, Alberto, Jarrod Ryan McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter Love, Alán Aspuru-Guzik, and Jeremy L. O’Brien. 2014. “A Variational Eigenvalue Solver on a Photonic Quantum Processor.” Nature Communications 5 (July 23): 4213. doi:10.1038/ncomms5213. http://dx.doi.org/10.1038/ncomms5213.2041-1723http://nrs.harvard.edu/urn-3:HUL.InstRepos:12697345Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer. For quantum systems, where the physical dimension grows exponentially, finding the eigenvalues of certain operators is one such intractable problem and remains a fundamental challenge. The quantum phase estimation algorithm efficiently finds the eigenvalue of a given eigenvector but requires fully coherent evolution. Here we present an alternative approach that greatly reduces the requirements for coherent evolution and combine this method with a new approach to state preparation based on ansätze and classical optimization. We implement the algorithm by combining a highly reconfigurable photonic quantum processor with a conventional computer. We experimentally demonstrate the feasibility of this approach with an example from quantum chemistry—calculating the ground-state molecular energy for \(He–H^+\). The proposed approach drastically reduces the coherence time requirements, enhancing the potential of quantum resources available today and in the near future.en-USphysical sciencesapplied sciencesoptical sciencesJournal Article2014-08-0710.1038/ncomms5213