Quantum algorithms for electronic structures: basis sets and boundary conditions

Liu, Jie; Fan, Yi; Li, Zhenyu; Yang, Jinlong

doi:10.1039/d1cs01184g

Cited by 14 publications

(11 citation statements)

References 183 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we developed a new protocol for evaluating molecular response properties on near-term quantum computers based on the linear-response framework, named as the quantum linear-response (qLR) theory. Inspired by the recent work [36,69,77], we make use of Mukherjee's self-consistent [64] (sc) and Surján's projected [65] (proj) excitation operator manifolds in conjunction with the qLR formalism to make sure that the "killer condition" is always satisfied. The two proposed formalisms, namely, qLR(sc) and qLR(proj), have been used for the evaluation of dipole polarizabilities and specific rotations of small molecular systems using the ground-state wavefunction obtained through the fermionic ADAPT-VQE algorithm.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Quantum simulation of molecular response properties

Kumar¹,

Asthana²,

Abraham³

et al. 2023

Preprint

View full text Add to dashboard Cite

Accurate modeling of the response of molecular systems to an external electromagnetic field is challenging on classical computers, especially in the regime of strong electronic correlation. In this paper, we develop a quantum linear response (qLR) theory to calculate molecular response properties on near-term quantum computers. Inspired by the recently developed variants of the quantum counterpart of equation of motion (qEOM) theory, the qLR formalism employs "killer condition" satisfying excitation operator manifolds that offers a number of theoretical advantages along with reduced quantum resource requirements. We also used the qEOM framework in this work to calculate state-specific response properties. Further, through noise-less quantum simulations, we show that response properties calculated using the qLR approach are more accurate than the ones obtained from the classical coupled-cluster based linear response models due to the improved quality of the ground-state wavefunction obtained using the ADAPT-VQE algorithm.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Fan and co-workers [69,77] recently made use of these operator manifolds within the framework of equation of motion theory to calculate band structures on a quantum computer. To ensure that Eq.…”

Section: Projection Operatorsmentioning

confidence: 99%

Quantum simulation of molecular response properties

Kumar¹,

Asthana²,

Abraham³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Excitations (lines [14][15][16][17][18]. This block gives further details on the ansatz, which takes the form…”

Section: The Electronic Structure Module I Unitary-coupled Clus-ter A...mentioning

confidence: 99%

“…By tuning the parameters to optimize a given objective function, the output of the circuit is driven toward a solution of a particular problem. This framework is extremely general and can be applied to a wide variety of different problems, including machine learning [11], finance [12], optimization [13], and quantum chemistry [14][15][16][17][18].…”

mentioning

confidence: 99%

TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era

Li¹,

Allcock²,

Cheng³

et al. 2023

Preprint

View full text Add to dashboard Cite

TenCirChem is an open-source Python library for simulating variational quantum algorithms for chemistry, biology, and material science. Its easy-to-use high-level interface enables users to optimize molecular energies or study quantum dynamics in only a few lines of code, while still allowing for a high degree of flexibility and customizability. By making use of compact representations of quantum states and excitation operators, efficient quantum circuits for fermionic excitations, and the powerful TensorCircuit software framework, TenCirChem displays high performance in simulating both noiseless and noisy quantum circuits, even when large numbers of qubits and tunable parameters are involved. As an example, we use it to compute the potential energy surface of H 2 O with 6-31G(d) basis set and (8e, 17o) active space using a quantum circuit ansatz of 34 qubits with 565 independent parameters, and achieve an equilibrium bond length error to 0.01 Å with respect to experiments.

show abstract

“…Two typical problems of this kind are computational-aided material design and drug discovery, in which quantum chemistry plays a critical role in answering questions such as ∼Which one is the best?∼. Many recent efforts have been devoted to the development of advanced quantum algorithms for solving quantum chemistry problems on noisy intermediate-scale quantum (NISQ) devices, 2,4–14 while implementing these algorithms for complex problems is limited by available qubit counts, coherence time and gate fidelity. Specifically, without error correction, quantum simulations of quantum chemistry are viable only if low-depth quantum algorithms are implemented to suppress the total error rate.…”

Section: Introductionmentioning

confidence: 99%