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As the year-to-year gains in speeds of classical computers continue to taper off, computational chemists are increasingly examining quantum computing as a possible route to achieve greater computational performance. Quantum computers, built upon the properties of superposition, interference, and entanglement of quantum bits, offer, in principle, the possibility to outperform classical computers for solving many important classes of problems. In the field of chemistry, quantum algorithm development offers promising propositions for solving classically intractable problems in areas such as electronic structure, chemical quantum dynamics, spectroscopy, and cheminformatics. However, physical implementations of quantum computers are still in their infancy and have yet to outperform classical computers for useful computations. Still, quantum software development for chemistry is a highly active area of research. In this perspective, we summarize recent progress in the areas of quantum computing algorithms, hardware, and software, and we describe the challenges that remain for useful implementations of quantum computing for chemical applications. 

Efficient methods for encoding and compression are likely to pave the way toward the problem of efficient trainability on higher-dimensional Hilbert spaces, overcoming issues of barren plateaus. Here, we propose an alternative approach to variational autoencoders to reduce the dimensionality of states represented in higher dimensional Hilbert spaces. To this end, we build a variational algorithm-based autoencoder circuit that takes as input a dataset and optimizes the parameters of a Parameterized Quantum Circuit (PQC) ansatz to produce an output state that can be represented as a tensor product of two subsystems by minimizing Tr(ρ2). The output of this circuit is passed through a series of controlled swap gates and measurements to output a state with half the number of qubits while retaining the features of the starting state in the same spirit as any dimension-reduction technique used in classical algorithms. The output obtained is used for supervised learning to guarantee the working of the encoding procedure thus developed. We make use of the Bars and Stripes (BAS) dataset for an 8 × 8 grid to create efficient encoding states and report a classification accuracy of 95% on the same. Thus, the demonstrated example provides proof for the working of the method in reducing states represented in large Hilbert spaces while maintaining the features required for any further machine learning algorithm that follows. 

We explore how to build quantum circuits that compute the lowest energy state corresponding to a given Hamiltonian within a symmetry subspace by explicitly encoding it into the circuit. We create an explicit unitary and a variationally trained unitary that maps any vector output by ansatz A(α→) from a defined subspace to a vector in the symmetry space. The parameters are trained varitionally to minimize the energy, thus keeping the output within the labelled symmetry value. The method was tested for a spin XXZ Hamiltonian using rotation and reflection symmetry and H2 Hamiltonian within Sz=0 subspace using S2 symmetry. We have found the variationally trained unitary gives good results with very low depth circuits and can thus be used to prepare symmetry states within near term quantum computers. 

Abstract Adaptive Variational Quantum Dynamics (AVQD) algorithms offer a promising approach to providing quantum‐enabled solutions for systems treated within the purview of open quantum dynamical evolution. In this study, the unrestricted‐vectorization variant of AVQD is employed to simulate and benchmark various non‐unitarily evolving systems. Exemplification of how construction of an expressible ansatz unitary and the associated operator pool can be implemented to analyze examples such as the Fenna–Matthews–Olson complex (FMO) and even the permutational invariant Dicke model of quantum optics. Furthermore, an efficient decomposition scheme is shown for the ansatz used, which can extend its applications to a wide range of other open quantum system scenarios in near future. In all cases the results obtained are in excellent agreement with exact numerical computations that bolsters the effectiveness of this technique. The successful demonstrations pave the way for utilizing this adaptive variational technique to study complex systems in chemistry and physics, like light‐harvesting devices, thermal, and opto‐mechanical switches, to name a few. 

Abstract Non‐classical features like interference are already being harnessed to control the output of chemical reactions. However, quantum entanglement which is an equally enigmatic many‐body quantum correlation can also be used as a powerful resource yet has eluded explicit attention. In this report, an experimental scheme under the crossed beam molecular dynamical setup, with the F + HD reaction, is proposed aiming to study the possible influence of entanglement within reactant pairs on the angular features of the product distribution. The aforesaid reaction has garnered interest recently, as an unusual horseshoe shape pattern in the product (HF) distribution was observed, which has been attributed to the coupling of spin and orbital degrees of freedom. An experimental scheme is proposed aiming to study the possible influence of entanglement on the necessity for the inclusion of such spin–orbit characteristics, under circumstances wherein the existence of entanglement and spin–orbit interaction is simultaneously detectable. The attainable results are further numerically simulated highlighting specific patterns corresponding to various possibilities. Such studies if extended can provide unforeseen mechanistic insight into analogous reactions, too, from the lens of quantum information.