Quantum implementation of the unitary coupled cluster for simulating molecular electronic structure

By Yangchao Shen, Xiang Zhang, Shuaining Zhang, Jing-Ning Zhang, Man-Hong Yung, Kihwan Kim. 2015

261 citations recorded by the source corpus.

In classical computational chemistry, the coupled-cluster ansatz is one of the most commonly used $ab~initio$ methods, which is critically limited by its non-unitary nature. The unitary modification as an ideal solution to the problem is, however, extremely inefficient in classical conventional computation. Here, we provide the first experimental evidence that indeed the unitary version of the coupled cluster ansatz can be reliably performed in physical quantum system, a trapped ion system. We perform a simulation on the electronic structure of a molecular ion (HeH$^+$), where the ground-state energy surface curve is probed, energies of excited-states are studied and the bond-dissociation is simulated non-perturbatively. Our simulation takes advantages from quantum computation to overcome the intrinsic limitations in classical computation and our experimental results indicate that the method is promising for preparing molecular ground-states for quantum simulation.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/2222096d75ea2af7cc327f7ec5f42121e2b19aa2

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In Atomic and Molecular Physics, and Optics

Citations in the corpus

Cloud Quantum Computing of an Atomic Nucleus. (2018, methodology): “In quantum computing, a popular approach to determine the ground-state energy Y ( ✓ 0 ) Y ( ✓ 0 ) ✓ ✓ 1 2 ◆ ✓ ✓ 1 2 ◆ | "i|"i ⌘ 2 Y ✓ � ⌘ 2 ◆ of a Hamiltonian is to use UCC ansatz in tandem with the VQE algorithm [12, 15, 21].”
Quantum Chemistry Calculations on a Trapped-Ion Quantum Simulator (2018, methodology): “The work in reported in [62] used a single 171Yb ion and mapped the Hamiltonian of the molecule to four of its internal energy levels, which is not a scalable procedure and provides no advantage over classical computation [90].”
An adaptive variational algorithm for exact molecular simulations on a quantum computer (2018, methodology): “[19] Shen, Y.”
Quantum Chemistry in the Age of Quantum Computing. (2018, background): “ors in an easy to prepare basis (e.g. the computational basis). The transformation Uis a 2N 2N matrix, however it is equivalent to rotating the basis, ˚p= X q qupq; a y p= X q cqupq; ap= X q cy qupq; (53) where ˚p, ay p, ap are spin-orbitals and fermionic operators in the rotated basis and uis an N ”
qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor (2019, background): “Compared to PEA, VQE has much more modest requirements on the coherence times of the quantum processor, so it has already been realized on NISQ devices, such as superconducting qubits [11, 12], photons [8] and trapped ions [13, 14].”
Barren plateaus in quantum neural network training landscapes (2018, methodology): “This algorithm has been successfully demonstrated on a number of experimental setups with extensions to excited states and other forms of incoherent error mitigation [2, 9, 12–16].”