Abstract

Quantum 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. 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.A variational eigenvalue solver on a photonic quantum processorThe role of QM/MM in rational drug discovery and molecular diversity for the construction of an anti-alpha-bungarotoxin binding MAP-p6.7 peptide mimetic ligand against nicotinic receptor binding site as a potent snake neurotoxin synthetic antidote. In chemistry, the properties of atoms and molecules can be determined by solving the Schrödinger equation. However, because the dimension of the problem grows exponentially with the size of the physical system under consideration, exact treatment of these problems remains classically infeasible for compounds with more than 2–3 atoms1. Many approximate methods2 have been developed to treat these systems, but efficient, exact methods for large chemical problems remain out of reach for classical computers. Beyond chemistry, the solution of large eigenvalue problems3 would have applications ranging from determining the results of internet search engines4 to designing new materials and drugs5.A variational eigenvalue solver on a photonic quantum processorThe role of QM/MM in rational drug discovery and molecular diversity for the construction of an anti-alpha-bungarotoxin binding MAP-p6.7 peptide mimetic ligand against nicotinic receptor binding site as a potent snake neurotoxin synthetic antidote. Recent developments in the field of quantum computation offer a way forward for determining efficient solutions of many instances of large eigenvalue problems that are classically intractable6,7,8,9,10,11,12. Quantum approaches to finding eigenvalues have previously relied on the quantum phase estimation (QPE) algorithm. The QPE algorithm offers an exponential speedup over classical methods and requires a number of quantum operations O(p−1) to obtain an estimate with precision p (refs 13, 14, 15, 16, 17, 18). In the standard formulation of QPE, one assumes the eigenvector |ψ› of a Hermitian operator is given as input and the problem is to determine the corresponding eigenvalue λ. The time the quantum computer must remain coherent is determined by the necessity of O(p−1) successive applications of , each of which can require on the order of millions or billions of quantum gates for practical applications17,19, as compared to the tens to hundreds of gates achievable in the short term. Here, we introduce an alternative to QPE that significantly reduces the requirements for coherent evolution. We have developed a reconfigurable quantum processing unit (QPU), which efficiently calculates the expectation value of a Hamiltonian (), providing an exponential speedup over exact diagonalization, the only known exact solution to the problem on a traditional computer. 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. The QPU has been experimentally implemented using integrated photonics technology with a spontaneous parametric downconversion single-photon source and combined with an optimization algorithm run on a classical processing unit (CPU), which variationally computes the eigenvalues and eigenvectors of a variational algorithm, this approach reduces the requirement for coherent evolution of the quantum state, making more efficient use of quantum resources, and may offer an alternative route to practical quantum-enhanced computation of QM/MM variational eigenvalue solvers on a photonic quantum processor in rational drug discovery and molecular diversity for the construction of an anti-alpha-bungarotoxin binding MAP-p6.7 peptide mimetic ligand against nicotinic receptor binding site as a potent snake neurotoxin synthetic antidote.

Keywords

Evaluation, Inverse Molecular Design Algorithm, Model Binding Site, In silico predicted, computer-aided molecular designed CTLA-4 blockador, increasement, antigen-specific CD8+ T-cells, inprevaccinated patients, melanoma, new cluster, algorithms, Large-Scale Protein-Ligand Docking experiment, inverse design, scoring function, protein-ligand interaction, cytochrome c peroxidase, dead-end elimination, drug design