@article {3078, title = {Accurate and Efficient Quantum Computations of Molecular Properties Using Daubechies Wavelet Molecular Orbitals: A Benchmark Study against Experimental Data}, journal = {PRX Quantum}, volume = {3}, year = {2022}, month = {5/28/2022}, pages = {020360}, abstract = {

Although quantum computation (QC) is regarded as a promising numerical method for computational quantum chemistry, current applications of quantum-chemistry calculations on quantum computers are limited to small molecules. This limitation can be ascribed to technical problems in building and manipulating more qubits and the associated complicated operations of quantum gates in a quantum circuit when the size of the molecular system becomes large. As a result, reducing the number of required qubits is necessary to make QC practical. Currently, the minimal STO-3G basis set is commonly used in benchmark studies because it requires the minimum number of spin orbitals. Nonetheless, the accuracy of using STO-3G is generally low and thus cannot provide useful predictions. We propose to adopt Daubechies wavelet functions as an accurate and efficient method for QCs of molecular electronic properties. We demonstrate that a minimal basis set constructed from Daubechies wavelet basis can yield accurate results through a better description of the molecular Hamiltonian, while keeping the number of spin orbitals minimal. With the improved Hamiltonian through Daubechies wavelets, we calculate vibrational frequencies for H2 and LiH using quantum-computing algorithm to show that the results are in excellent agreement with experimental data. As a result, we achieve quantum calculations in which accuracy is comparable with that of the full configuration interaction calculation using the cc-pVDZ basis set, whereas the computational cost is the same as that of a STO-3G calculation. Thus, our work provides a more efficient and accurate representation of the molecular Hamiltonian for efficient QCs of molecular systems, and for the first time demonstrates that predictions in agreement with experimental measurements are possible to be achieved with quantum resources available in near-term quantum computers.

}, doi = {https://doi.org/10.1103/PRXQuantum.3.020360}, url = {https://arxiv.org/abs/2205.14476}, author = {Cheng-Lin Hong and Ting Tsai and Jyh-Pin Chou and Peng-Jen Chen and Pei-Kai Tsai and Yu-Cheng Chen and En-Jui Kuo and David Srolovitz and Alice Hu and Yuan-Chung Cheng and Hsi-Sheng Goan} } @article {3067, title = {Boson Sampling for Generalized Bosons}, year = {2022}, month = {5/2/2022}, abstract = {

We introduce the notion of \"generalized bosons\" whose exchange statistics resemble those of bosons, but the local bosonic commutator [ai,a\†i]=1 is replaced by an arbitrary single-mode operator that is diagonal in the generalized Fock basis. Examples of generalized bosons include boson pairs and spins. We consider the analogue of the boson sampling task for these particles and observe that its output probabilities are still given by permanents, so that the results regarding hardness of sampling directly carry over. Finally, we propose implementations of generalized boson sampling in circuit-QED and ion-trap platforms.

}, url = {https://arxiv.org/abs/2204.08389}, author = {En-Jui Kuo and Yijia Xu and Dominik Hangleiter and Andrey Grankin and Mohammad Hafezi} } @article {2817, title = {Decoding conformal field theories: from supervised to unsupervised learning}, year = {2021}, month = {7/10/2021}, abstract = {

We use machine learning to classify rational two-dimensional conformal field theories. We first use the energy spectra of these minimal models to train a supervised learning algorithm. We find that the machine is able to correctly predict the nature and the value of critical points of several strongly correlated spin models using only their energy spectra. This is in contrast to previous works that use machine learning to classify different phases of matter, but do not reveal the nature of the critical point between phases. Given that the ground-state entanglement Hamiltonian of certain topological phases of matter is also described by conformal field theories, we use supervised learning on R{\'e}yni entropies and find that the machine is able to identify which conformal field theory describes the entanglement Hamiltonian with only the lowest few R{\'e}yni entropies to a high degree of accuracy. Finally, using autoencoders, an unsupervised learning algorithm, we find a hidden variable that has a direct correlation with the central charge and discuss prospects for using machine learning to investigate other conformal field theories, including higher-dimensional ones. Our results highlight that machine learning can be used to find and characterize critical points and also hint at the intriguing possibility to use machine learning to learn about more complex conformal field theories.

}, url = {https://arxiv.org/abs/2106.13485}, author = {En-Jui Kuo and Alireza Seif and Rex Lundgren and Seth Whitsitt and Mohammad Hafezi} }