### Quantum-computing related developments

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

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

February 25, 2020

- Source: arXiv

Quantum computers were initially proposed to efficiently simulate quantum mechanical systems with an exponential speedup compared to classical computers. We are currently in the noisy intermediate-scale quantum (NISQ) era, which means quantum chips still have a small number of qubits. This prohibits straightforward quantum simulations of realistic molecules and materials, whose description requires hundreds of atoms and thousands to millions of degrees of freedom to represent the electronic wavefunctions. One research direction which attempts to bypass this restriction is the development of hybrid quantum-classical methods where the quantum computation is restricted to a small portion of the system.

In this paper, a quantum embedding theory is proposed for the calculation of strongly-correlated electronic states of active regions, while the rest of the system is described with density functional theory (DFT). DFT (and its various approximations) has been extremely successful in predicting numerous properties of solids, liquids and molecules, and in providing key interpretations to a variety of experimental results, but is often inadequate to describe strongly-correlated electronic state. The novel theory proposed in this paper is built on DFT and does not require the explicit evaluation of virtual electronic states, thus making the method scalable to materials with thousands of electrons. Also, it includes the effect of exchange-correlation interactions of the environment on active regions, thus going beyond commonly adopted approximations in conventional DFT.

The proposed quantum embedding theory utilizes a classical and a quantum algorithm to solve the Hamiltonian that describes the problem and yields results in good agreement with existing experimental measurements and still-tractable computations on classical computing architectures. The theory is tested in various solid-state quantum information technologies, which exhibit strongly-correlated electronic states. In this way, the authors show how the quantum-classical hybrid approach incorporating DFT enables the study of large-scale material systems while adding the strongly-correlated dynamics analysis which the quantum simulation algorithm can provide.

January 31, 2020

- Source: arXiv

In this arXiv submission by Qu & Co and Covestro, a well-known approximation in classical computational methods for quantum chemistry is applied to a quantum computing scheme for simulating molecular chemistry efficiently on near-term quantum devices. The restricted mapping allows for a polynomial reduction in both the quantum circuit depth and the total number of measurements required, as compared to the conventional variational approaches based on near-term quantum simulation of molecular chemistry, such as UCCSD. This enables faster runtime convergence of the variational algorithm to a potentially higher accuracy by using a larger basis set allowed by the restricted mapping. The latter is shown via an example simulation of the disassociation curve of lithium hydride. These results open up a new direction for efficient near-term quantum chemistry simulation, as well as decreasing the effective quantum resource requirements for future fault-tolerant quantum computing schemes.

July 12, 2019

- Source: McKinsey & Co

In this article by McKinsey & Co, a strategy consulting firm, Florian Budde and Daniel Volz state that the chemical companies must act now to capture the benefits of quantum computing. Of course we at Qu & Co are a bit biased on this topic, but we do agree with the authors that the chemical sector is likely to be an early beneficiary of the vastly expanded modeling and computational capabilities, which is promised to be unlocked by quantum computing.

January 15, 2019

- Source: ArXiv

Thus far, quantum chemistry quantum algorithms have been experimentally demonstrated only on gate-based quantum computers. Efforts have been made to also map the chemistry problem Fermionic Hamiltonian to an Ising Hamiltonian in order to solve it on a quantum annealer. However, the number of qubits required still scales exponentially with the problem size (the number of orbitals considered in the electronic structure problem). As an alternative, this paper presents a different approach exploiting the efficiency at which quantum annealers can solve discrete optimization problems, and mapping a qubit coupled cluster method to this form. They simulate their method on an ideal Ising machine and on a D-Wave 2000Q system, and find promising success rates for smaller molecules. However, further investigation would be necessary to investigate the usability for larger or more complex systems, as the scaling of their folding technique with the number of local minima is unknown. In addition, it is unclear from the experimental data whether the limitations of the D-Wave system as compared to a perfect Ising machine could hinder expected performance gains for more complex systems.

April 19, 2018

- Source: Arxiv

Recently, promising experimental results have been shown for quantum-chemistry calculations using small, noisy quantum processors. As full scale fault-tolerant error correction is still many years away, near-term quantum computers will have a limited number of qubits, and each qubit will be noisy. Methods that reduce noise and correct errors without doing full error correction on every qubit will help extend the range of interesting problems that can be solved in the near-term. In this paper Otten et al. present a scheme for accounting (and removal) of errors in observables determined from quantum algorithms and apply this scheme to the variational quantum eigensolver algorithm, simulating the calculation of the ground state energy of equilibrium H2 and LiH in the presence of several noise sources, including amplitude damping, dephasing, thermal noise, and correlated noise. They show that their scheme provides a decrease in the needed quality of the qubits by up to two orders of magnitude.

April 15, 2018

- Source: Arxiv

In this paper Bian et al. compare four different quantum simulation methods to simulate the ground state energy of the Hamiltonian for the water molecule on a quantum computer, being 1) the phase estimation algorithm based on Trotter decomposition, 2) phase estimation based on the direct implementation of the Hamiltonian, 3) direct measurement based on the implementation of the Hamiltonian and 4) the variational quantum eigensolver (classical-quantum hybrid) algorithm. They compare a.o. the required number of qubits, gate-complexity, accuracy/error.

March 27, 2018

- Source: Arxiv

Efficient quantum simulations of classically intractable instances of the associated electronic structure problem promise breakthroughs in our understanding of basic chemistry and could revolutionize research into new materials, pharmaceuticals, and industrial catalysts. 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 this paper, Hempel et al. use a digital quantum simulator based on trapped ions to experimentally investigate the VQE algorithm for the calculation of molecular ground state energies of two simple molecules (H2 and LiH) and experimentally demonstrate and compare different encoding methods using up to four qubits.

January 23, 2018

- Source: Company website

XtalPi, a computation-driven pharmaceutical technology company, closed a series B funding of $ 15 mln led by Sequoia China, with participation from Google and existing investor Tencent. To date, XtalPi raised over $ 20 mln. Although the company does not (yet) uses quantum computing, it combines AI, quantum physics, and cloud-HPC, to compute and predict characteristics of small-molecule drugs and solid forms. Founded was founded in 2014 by a group of quantum physicists at MIT.

January 11, 2018

- Source: Arxiv

Quantum computers promise to reduce the computational complexity of simulating quantum many-body systems from exponential to polynomial. Much effort is being put in reducing the complexity of the necessary algorithms, to allow them to be run on noisy intermediate scale quantum computers. In this paper, Dumitrescu et al. report a quantum simulation of the deuteron binding energy on 2 such small-scale noisy cloud accessible quantum processors (the IBM QX5 and Rigetti 19Q).

January 3, 2018

- Source: Arxiv

Understanding and modeling the behavior of large numbers of interacting fermions is key to understanding the macroscopic properties of matter. However, the memory required to represent such a many-body state scales exponentially with the number of fermions, which makes simulation of many interesting cases intractable on classical computers. Algorithms leveraging the advantages of quantum computers for quantum simulations have steadily been developed in the past two decades. Variational quantum eigensolvers (VQE) have recently appeared as a promising class of quantum algorithms designed to prepare states for such quantum simulations. Low-depth circuits for such state preparation and quantum simulation are needed for practical quantum chemistry applications on near-term quantum devices with limited coherence. In this paper, Dallaire-Demers et al. present a new type of low-depth VQE ansatz, which should be in reach of near-term quantum devices and which can accurately prepare the ground state of correlated fermionic systems.