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Quantum-computing related developments

On this page we post about interesting quantum-computing related research and news which we are following.

Survey of Quantum Computational Chemistry

Survey of Quantum Computational Chemistry

Quantum chemistry

This report by Olson et al. summarizes the resuts of an NSF Workshop on Quantum Computational Chemistry held in November 2016. The workshop was attended by a wide range of experts from directly quantum-oriented fields such as algorithms, chemistry, machine learning, optics, simulation, and metrology, as well as experts in related fields such as condensed matter physics, biochemistry, physical chemistry, inorganic and organic chemistry, and spectroscopy. The goal of the workshop was to summarize recent progress in research at the interface of quantum information science and chemistry as well as to discuss the promising research challenges and opportunities in the field.

Quantum computing augmented classical simulation of a chemical reaction

Quantum computing augmented classical simulation of a chemical reaction

Quantum chemistry

At its core, the detailed understanding and prediction of complex chemical reaction mechanisms, requires highly accurate electronic structure methods. For molecules with many energetically close-lying orbitals, much less than a hundred strongly correlated electrons are already out of reach for classical calculation methods that could achieve the required accuracy. In this paper, Reiher et al. using as an example the open problem of biological nitrogen fixation in nitrogenase, to show how quantum computers can augment classical computer simulations used to probe these reaction mechanisms, to significantly increase their accuracy and enable hitherto intractable simulations. They demonstrate that quantum computers will be able to tackle important problems in chemistry without requiring exorbitant resources (in this case as little as 111 qubits and 1.0x10^14 T gates)

Determining the ground state energy of small molecules (up to BeH2) using the VQE algorithm

Determining the ground state energy of small molecules (up to BeH2) using the VQE algorithm

Quantum chemistry

Quantum computers can be used to address molecular structure, materials science and condensed matter physics problems, which currently stretch the limits of existing high-performance computing resources. Finding exact numerical solutions to these interacting fermion problems has exponential cost, while Monte Carlo methods are plagued by the fermionic sign problem. In Quantum Computational Chemistry solutions, the Variational Quantum Eigensolver (VQE) algorithm offers a hybrid classical-quantum, and thus low quantum circuit depth, alternative to the Phase Estimation algorithm used to measure the ground-state energy of a molecular Hamiltonian. In VQE the quantum computer is used to prepare variational trial states that depend on a set of parameters. Then, the expectation value of the energy is estimated and used by a classical optimizer to generate a new set of improved parameters. The advantage of VQE over classical simulation methods is that in VQE one can prepare trial states that are not amenable to efficient classical numerics. In this paper, Kandala et al. demonstrate the experimental results for determining the ground state energy for molecules of increasing size, up to BeH2 using the VQE algorithm.

Experimental results of an adaptive Bayesian approach to Quantum Phase Estimation for Quantum Chemistry

Experimental results of an adaptive Bayesian approach to Quantum Phase Estimation for Quantum Chemistry

Quantum chemistry

Most near-term quantum-computational chemistry experiments have so-far been implemented by applying the Variational Quantum Eigensolver (VQE) classical-quantum hybrid algorithm as an alternative to Quantum Phase Estimation (QPE). This is due to the fact that QPE requires many orders of magnitude more quantum gates than is feasible with typical coherence times of current and near-term quantum-processors. As an alternative, in this paper, Paesani et al. report experimental results of a recently proposed adaptive Bayesian approach to quantum phase estimation and use it to simulate molecular energies on a Silicon quantum photonic device. The approach is verified to be well suited for NISQ quantum-processors by investigating its superior robustness to noise and decoherence compared to the iterative phase estimation algorithm. There results shows a promising route to unlock the power of QPE much sooner than previously believed possible.