Development of a compactAnsatzvia operator commutativity screening: Digital quantum simulation of molecular systems

Abstract

Recent advancements in quantum information and quantum technology have stimulated a good deal of interest in the development of quantum algorithms toward the determination of the energetics and properties of many-fermionic systems. While the variational quantum eigensolver is the most optimal algorithm in the noisy intermediate scale quantum era, it is imperative to develop compact Ansätze with low-depth quantum circuits that are physically realizable in quantum devices. Within the unitary coupled cluster framework, we develop a disentangled Ansatz construction protocol that can dynamically tailor an optimal Ansatz using the one- and two-body cluster operators and a selection of rank-two scatterers. The construction of the Ansatz may potentially be performed in parallel over multiple quantum processors through energy sorting and operator commutativity prescreening. With a significant reduction in the circuit depth toward the simulation of molecular strong correlation, our dynamic Ansatz construction protocol is shown to be highly accurate and resilient to the noisy circumstances of the near-term quantum hardware.

Machine learning assisted construction of a shallow depth dynamic ansatz for noisy quantum hardware

“…1–3 In recent years, a plethora of state-of-the-art methods have been developed that aim to produce accurate energies and wavefunctions for molecular systems utilizing quantum hardware. Leading the pack are the variational algorithms, 4–17 which rely on the dynamic construction and deployment of shallow depth parameterized ansatzes to generate the molecular wavefunctions. They are highly suitable for Noisy Intermediate-Scale Quantum (NISQ) 18 devices that suffer from limited coherence time, state preparation and measurement (SPAM) errors, and poor gate fidelity.…”

Deep Neural Network Assisted Quantum Chemistry Calculations on Quantum Computers

“…Recent works have proposed novel ways to improve the efficiency of the VQE algorithm, such as (i) proposing newer chemically inspired ansatzes like k-UpCCGSD with lower circuit depths than that of the UCCSD ansatz, (ii) modifying the existing UCCSD ansatz through operator screening or by using the newer variants of UCCSD like dual exponential variant for reducing the circuit depth, − and (iii) by employing embedding schemes and entanglement forging techniques for reducing the qubit requirement while working with larger molecules. , Specific to the issue with the parameter optimization in VQE, Tao et al proposed the deep neural network (DNN)-VQE method, where a DNN model is used to predict the final optimized variational parameters for the quantum circuit . Using such parameters as the initial point for a VQE calculation, the authors showed that the DNN-VQE method is able to accurately construct the PESs of several small molecules such as H 2 , LiH, BeH 2 , and H 4 .…”