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Magneto-conductance in thin wires often exhibits complicated patterns due to the quantum interference of conduction electrons. These patterns reflect microscopic structures in the wires, such as defects or potential distributions. In this study, we propose an inverse design method to automatically generate a microscopic structure that exhibits desired magneto-conductance patterns. We numerically demonstrate that our method accurately generates defect positions in wires and can be effectively applied to various complicated patterns. We also discuss techniques for designing structures that facilitate experimental investigation., Comment: 7 pages, 5 figures

Solving inverse problems to identify Hamiltonians with desired properties holds promise for the discovery of fundamental principles. In quantum systems, quantum entanglement plays a pivotal role in not only characterizing the quantum nature but also developing quantum technology like quantum computing. Nonetheless, the design principles of the quantum entanglement are yet to be clarified. Here we apply an inverse design framework using automatic differentiation to quantum spin systems, aiming to construct Hamiltonians with large quantum entanglement. We show that the method automatically finds the Kitaev model with bond-dependent anisotropic interactions, whose ground state is a quantum spin liquid, on both honeycomb and square-octagon lattices. On triangular and maple-leaf lattices with geometrical frustration, it generates numerous solutions with spatially inhomogeneous interactions rather than converging to a specific model, but it still helps to construct unprecedented models. The comparative study reveals that bond-dependent anisotropic interactions, rather than isotropic Heisenberg interactions, amplify quantum entanglement, even in systems with geometrical frustration. The present study paves the way for the automatic design of new quantum systems with desired quantum nature and functionality.

Quantum computers (QCs), which work based on the law of quantum mechanics, are expected to be faster than classical computers in several computational tasks such as prime factoring and simulation of quantum many-body systems. In the last decade, research and development of QCs have rapidly advanced. Now hundreds of physical qubits are at our disposal, and one can find several remarkable experiments actually outperforming the classical computer in a specific computational task. On the other hand, it is unclear what the typical usages of the QCs are. Here we conduct an extensive survey on the papers that are posted in the quant-ph section in arXiv and claim to have used QCs in their abstracts. To understand the current situation of the research and development of the QCs, we evaluated the descriptive statistics about the papers, including the number of qubits employed, QPU vendors, application domains and so on. Our survey shows that the annual number of publications is increasing, and the typical number of qubits employed is about six to ten, growing along with the increase in the quantum volume (QV). Most of the preprints are devoted to applications such as quantum machine learning, condensed matter physics, and quantum chemistry, while quantum error correction and quantum noise mitigation use more qubits than the other topics. These imply that the increase in QV is fundamentally relevant, and more experiments for quantum error correction, and noise mitigation using shallow circuits with more qubits will take place., Comment: 14 pages, 5 figures, figures regenerated

An ultimate goal of materials science is to deliver materials with desired properties at will. In the theoretical study, a standard approach consists of constructing a Hamiltonian based on phenomenology or first principles, calculating physical observables, and improving the Hamiltonian through feedback. However, there is also an approach that bypasses such a cumbersome procedure, namely, to obtain an appropriate Hamiltonian directly from the desired properties. Solving the inverse problem has the potential to reach qualitatively different principles, but most research to date has been limited to quantitative determination of parameters within known models. Here, we present a general framework that enables the inverse design of Hamiltonians by optimizing numerous parameters using automatic differentiation. By applying it to the quantum anomalous Hall effect (AHE), we show that our framework can not only rediscover the Haldane model but also automatically generate a new Hamiltonian that exhibits a six-times larger AHE. In addition, the application to the photovoltaic effect (PVE) gives an optimal Hamiltonian for electrons moving on a noncoplanar spin texture, which can generate $\sim 900$~A/m$^2$ under solar radiation. This framework would accelerate materials exploration by establishing unprecedented models and principles beyond human intuition and empirical rules.

We propose a general framework for finding the ground state of many-body fermionic systems by using feed-forward neural networks. The anticommutation relation for fermions is usually implemented to a variational wave function by the Slater determinant (or Pfaffian), which is a computational bottleneck because of the numerical cost of $O(N^3)$ for $N$ particles. We bypass this bottleneck by explicitly calculating the sign changes associated with particle exchanges in real space and using fully connected neural networks for optimizing the rest parts of the wave function. This reduces the computational cost to $O(N^2)$ or less. We show that the accuracy of the approximation can be improved by optimizing the "variance" of the energy simultaneously with the energy itself. We also find that a reweighting method in Monte Carlo sampling can stabilize the calculation. These improvements can be applied to other approaches based on variational Monte Carlo methods. Moreover, we show that the accuracy can be further improved by using the symmetry of the system, the representative states, and an additional neural network implementing a generalized Gutzwiller-Jastrow factor. We demonstrate the efficiency of the method by applying it to a two-dimensional Hubbard model.

Stimulated by anomalous behaviors found in non-Kramers $f$-electron systems in an applied magnetic field, we study a two-channel Kondo lattice model by using a cluster extension of the dynamical mean-field theory combined with the continuous-time quantum Monte Carlo method. We include the effect of the external magnetic field in two ways: the Zeeman coupling to conduction electron spins and an effective coupling to the quadrupole degree of freedom through the crystalline electric field splitting. We show that the magnetic field suppresses the antiferroic-spin order (physically, corresponding to the antiferroic-quadrupole order), and yields a channel-selective non-Fermi liquid state where one of the two channels (physically, spin-up or -down) exhibits non-Fermi liquid behavior while the other shows Fermi liquid behavior, before entering the Fermi liquid regime in higher fields. This anomalous state appears in a dome-shaped region which extends from inside the antiferroic-spin ordered phase to the paramagnetic phase. We find that the composite correlation, which is a measure of differentiation in the Kondo coupling between the two channels, is enhanced in this dome-shaped region. We also find that the specific heat coefficient is enhanced in this region in the paramagnetic side, indicating heavy fermion behavior not only in the vicinity of the critical field where the antiferroic-spin order vanishes but also in a certain region of the field and temperature. We discuss the results in comparison with the ordinary Kondo lattice model. We also discuss the implication of our findings to the peculiar behavior observed in the 1-2-20 compounds such as $\rm{PrIr_2Zn_{20}}$, $\rm{PrRh_2Zn_{20}}$, and $\rm{PrV_2Al_{20}}$ under a magnetic field.