Prospects of quantum computing for molecular sciences

Abstract

Molecular science is governed by the dynamics of electrons and atomic nuclei, and by their interactions with electromagnetic fields. A faithful physicochemical understanding of these processes is crucial for the design and synthesis of chemicals and materials of value for our society and economy. Although some problems in this field can be adequately addressed by classical mechanics, many demand an explicit quantum mechanical description. Such quantum problems require a representation of wave functions that grows exponentially with system size and therefore should naturally benefit from quantum computation on a number of logical qubits that scales only linearly with system size. In this perspective, we elaborate on the potential benefits of quantum computing in the molecular sciences, i.e., in molecular physics, chemistry, biochemistry, and materials science.

Dynamical Self-energy Mapping (DSEM) for Creation of Sparse Hamiltonians Suitable for Quantum Computing

“…The estimation of the cost of performing molecular calculations on quantum devices is critical to gain insight into the efficiency of such calculations. Such estimations were done most notably for phase estimation in ref and subsequently for the VQE formalism . These estimations point to the crucial dependence of the circuit depth on the sparsity of the Hamiltonian.…”

Error propagation in NISQ devices for solving classical optimization problems

“…In addition to developing technologies towards the final goal of a fault tolerant quantum computer, there is widespread interest in exploring the capabilities of the currently available noisy intermediate-scale quantum (NISQ) devices [4]. This has inspired a number of heuristic quantum algorithms for NISQ devices [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], but it remains unclear if they can provide a quantum advantage for practically interesting problems.…”

Interaction Energies on Noisy Intermediate-Scale Quantum Computers

“…The most prominent NISQ methods are hybrid quantum-classical algorithms like the variational quantum eigensolver (VQE) [20,21], quantum Krylov methods [18,[22][23][24][25] or the fermionic quantum Monte Carlo method [26]. The second challenge is to find specific applications that could harness quantum computing [27,28]. Many application studies in chemistry use either reduced model systems or molecules with a simple electronic structure [29][30][31][32].…”