Takashi Kakue

Electrons confined in quantum-disk structures exhibit an intrinsic spin vortex state with spin angular momentum (SAM) and orbital angular momentum (OAM) [ Phys. Rev. B 79(15), 155450 (2009)10.1103/PhysRevB.79.155450 ]. A higher-order electron state, which is a SAM – OAM entangled state expressed by superposition of spin–vortex states, has a spatial spin structure and can be represented as a higher-order Bloch vector on a higher-order Bloch sphere [ Optica Quantum 2(4), 245 (2024)10.1364/OPTICAQ.527615 ]. In this study, we analyzed the Rabi oscillation of higher-order electron states in a quantum disk structure driven by higher-order photons possessing both SAM and OAM, with the aim of manipulating SAM – OAM entangled electron states. The higher-order Bloch vector precesses about the Stokes vector of the higher-order photon-driven field, and its precession behavior can be controlled by the state of the driving photon field, pulse width, and intensity. These results will lead to the realization of high-dimensional quantum computations and high-dimensional quantum interfaces using SAM and OAM of electrons and photons.

Projection-type holographic displays are three-dimensional (3D) displays that utilize computer holography. These displays consist of a projection lens and a transparent holographic optical element (HOE) with off-axis concave mirror phase distribution and allow the user to observe a large 3D image by designing the magnification of the projection lens and the light-collecting function of the HOE screen independently. However, the observation area of the 3D image is limited. In this paper, we propose a method of expanding the observation area using time-division projection of multiple reproduced lights. With the proposed system developed to synchronize illumination lights and holograms, the proposed method can expand the observation area by a factor of 1.7 while maintaining the image size twofold that obtained by the conventional system. In addition, we succeeded in generating reproducing 3D video at 20 frames per second.

Digital light-in-flight (LIF) holography is an ultrafast imaging technique capable of single-shot simultaneous 3D and femtosecond time resolution acquisitions of light pulse propagation. However, the numerical diffraction algorithms used to model light on femtosecond timescales are currently limited in scope, accuracy, and efficiency. We derive an analytical model capable of modeling LIF hologram formation for various optical setup configurations, able to model 3D objects and precisely account for the limited temporal coherence of the signal. We design an efficient algorithmic implementation and validate the system in numerical simulations and with an experimental LIF holographic recording setup. We report ultrafast numerical diffraction over 10,000 times faster than the reference technique, with higher accuracy and capable of modeling 3D samples, thereby broadening its application domain.

Incoherent digital holography is a three-dimensional (3D) imaging technique that uses a natural light source. Through computer-based diffraction calculations, 3D information can be reconstructed from self-interferometer-captured holograms. As practical applications require a computer capable of high-speed diffraction calculations, an investigation was conducted into a special-purpose computer that uses a field-programmable gate array (FPGA). This approach had not been used for incoherent digital holography. In this study, the computational performance of a proposed monochromatic diffraction circuit was improved. By using direct memory access transfer, the data transfer time bottleneck, which is a problem in conventional circuits, was eliminated. The calculation was also parallelized, and the processing speed was 150 times faster than that of conventional circuits. In addition, a special-purpose computer for 3D color imaging was developed by implementing the proposed circuits for the three wavelengths on a single FPGA board. As a result of the verification, a speedup of approximately nine times over a 10-core CPU was achieved. The results of this research strongly promote the realization of a holographic portable camera with real-time 3D color imaging capability.

Accelerating computer-generated holograms (CGHs) is crucial for practical three-dimensional (3D) displays with electro-holography. The authors developed a fast CGH calculation algorithm that focuses on projecting only 3D wireframes because the formulas or algorithms for generating CGH become simpler and, in many cases, the expressivity of the wireframe is sufficient to provide users with all necessary information, such as which direction to move in a car navigation system. In this presentation, we provide an overview of how to accelerate the CGH calculation of wireframe art and discuss our future research directions with respect to the remaining issues.

In this paper, we have proposed a hologram calculation method using polynomial approximations for reducing the computational complexity of point-cloud-based hologram calculations. The computational complexity of existing point-cloud-based hologram calculations is proportional to the product of the number of point light sources and hologram resolution, whereas that of the proposed method can be reduced to approximately proportional to the sum of the number of point light sources and hologram resolution by approximating the object wave with polynomials. The computation time and reconstructed image quality were compared with those of the existing methods. The proposed method was approximately 10 times faster than the conventional acceleration method, and did not produce significant errors when the object was far from the hologram.

We propose a deep hologram converter based on deep learning to convert low-precision holograms into middle-precision holograms. The low-precision holograms were calculated using a shorter bit width. It can increase the amount of data packing for single instruction/multiple data in the software approach and the number of calculation circuits in the hardware approach. One small and one large deep neural network (DNN) are investigated. The large DNN exhibited better image quality, whereas the smaller DNN exhibited a faster inference time. Although the study demonstrated the effectiveness of point-cloud hologram calculations, this scheme could be extended to various other hologram calculation algorithms.