Hiroki Takesue

High-quality quantum state generation is essential for advanced quantum information processing, including quantum communication, quantum sensing, and quantum computing. In realistic experiments, however, the generated quantum state is degraded by multiple experimental error sources whose individual contributions are often difficult to quantify and calibrate. Quantum state tomography (QST) provides experimental access to the resulting density matrix, but it does not by itself attribute observed deviations to specific error sources. To address this problem, we propose an automated method for quantifying error sources by combining simulation and parameter optimization, in which a parametrized simulator of state preparation and measurement is optimized so that the simulated density matrix best fits the experimentally reconstructed density matrix obtained via QST. We focus on the experimental generation of time-bin entangled photon pairs, for which we model the relevant error sources and simulate the density matrix with adjustable model parameters, thereby optimizing the parameters and minimizing the trace distance to the experimental data. Optimization of the parameters reduced the trace distance from 0.177 to 0.024, indicating that our modeled error sources explain 86% of the errors. Reducing the predicted error sources improves the state quality, consistent with our predictions and thus validating the proposed method. The proposed framework provides a practical route to automated, quantitative error attribution and calibration directly from experimental QST data.

The high-rate detection of entangled photons is essential for advancing photonic quantum information processing. Although several experimental demonstrations have been reported, the achievable coincidence rates have so far remained limited. One of the main bottlenecks arises from the dead time of single-photon detectors, which constrains coincidence detection at high photon-pair generation rates. In this work, we employ 16-pixel superconducting nanowire single-photon detectors (SNSPDs) to mitigate the impact of detector dead time. Consequently, we achieve coincidence rates exceeding 3 million counts per second (Mcps) in two-photon interference and CHSH inequality experiments using a 5-GHz clocked sequential time-bin entangled photon pair source. To the best of our knowledge, this is the first demonstration of multi-Mcps coincidence detection of entangled photons, paving the way for high-speed entangled-photon-based quantum information processing.

Combinatorial optimizers using physical systems, or “Ising machines,” have been intensively studied as a way of computation that is more efficient than current digital computers. Here, we demonstrate that a coherent Ising machine (CIM) based on degenerate optical parametric oscillator (DOPO) pulses can find solutions to large-scale maximum independent set problems, which is an essential optimization problem that is closely related to real-world optimization tasks. We implemented a stable external field for each Ising spin as an interaction between a DOPO pulse and a group of ferromagnetically coupled auxiliary pulses. Consequently, the CIM delivered independent sets for dense graphs with up to 40,000 nodes. We compared the performance of the CIM with that of optimized simulated annealing algorithm implemented on a digital computer and found that the CIM outperformed the central processing unit (CPU) in terms of the time needed to obtain good approximate solutions when the graph size exceeded several thousand nodes.

Unmanned aerial vehicles (UAVs) are used widely in wireless communications to enhance the service experience of mobile users on the ground. This study investigates a dynamic resource management problem in a large-scale UAV-aided wireless network, where multiple UAVs operate as aerial base stations to serve ground users. To enhance communication efficiency for all users in the presence of co-channel interference, we proposed the use of an Ising model-based optimization method for fast and accurate UAV-user association and resource allocation. First, we formulated the dynamic user association and resource allocation problem as a combinatorial optimization problem. Subsequently, We transformed the objective function in the formulated problem into the energy function of a quadratic unconstrained binary optimization (QUBO) model and applied Coherent Ising Machine (CIM), which has been proven to be robust for solving dense Ising problems. We elevated our proposed method through simulations using CIM for optimization. Additionally, we compared our method with simulated annealing, steepest descent, and exhaustive search. Simulation results indicate that our proposed method is superior in terms of computational time and data rate.

In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

We report the recent progress of a coherent Ising machine, which simulates the Ising model using a network of degenerate optical parametric oscillators (DOPO). We also describe a spiking neural network realized with DOPOs.

A coherent XY machine (CXYM) is a physical spin simulator that can simulate the XY model by mapping XY spins onto the continuous phases of non-degenerate optical parametric oscillators (NOPOs). Here, we demonstrated a large-scale CXYM with >47,000 spins by generating 10-GHz-clock time-multiplexed NOPO pulses via four-wave mixing in a highly nonlinear fiber inside a fiber ring cavity. By implementing a unidirectional coupling from the ith pulse to the (i + 1)th pulse with a variable 1-pulse delay planar lightwave circuit interferometer, we successfully controlled the effective temperature of a one-dimensional XY spin network within two orders of magnitude.

Noisy mean-field annealing (NMFA) is an algorithm that mimics a coherent Ising machine (CIM), which is an optical system for solving Ising problems. The NMFA has reproduced the solver performance of the CIM for systems of limited size even though it simplifies the interaction between spins with a mean-field approximation. However, recent experiments observing various thermodynamic quantities have revealed that the CIM can capture the phase transitions of the two-dimensional Ising models that the mean field cannot capture. This situation leads to a fundamental question as to how well the NMFA can capture the features of the thermodynamic quantities around the phase transition. This paper answers that the NMFA reproduces the thermodynamic features of the mean field, but cannot reproduce the CIM results. This suggests that, in terms of sampling, the level of performance of the CIM is beyond that of the NMFA.

Non-orthogonal multiple access (NOMA) technique is important for achieving a high data rate in next-generation wireless communications. A key challenge to fully utilizing the effectiveness of the NOMA technique is the optimization of the resource allocation (RA), e.g., channel and power. However, this RA optimization problem is NP-hard, and obtaining a good approximation of a solution with a low computational complexity algorithm is not easy. To overcome this problem, we propose the coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems. The CIM is an Ising system that can deliver fair approximate solutions to combinatorial optimization problems at high speed (millisecond order) by operating optimization algorithms based on mutually connected photonic neural networks. The performance of our proposed method was evaluated using a simulation model of the CIM. We compared the performance of our proposed method to simulated annealing, a conventional-NOMA pairing scheme, deep Q learning based scheme, and an exhaustive search scheme. Simulation results indicate that our proposed method is superior in terms of speed and the attained optimal solutions.

A coherent Ising machine (CIM) is known to deliver the low-energy states of the Ising model. Here, we investigate how well the CIM simulates the thermodynamic properties of a two-dimensional square-lattice Ising model. Assuming that the spin sets sampled by the CIM can be regarded as a canonical ensemble, we estimate the effective temperature of the spins represented by degenerate optical parametric oscillator pulses by using maximum likelihood estimation. With the obtained temperature, we confirmed that the thermodynamic quantities obtained with the CIM exhibited a phase-transition-like behavior that better matches the analytical and numerical results than the mean field one.