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Moe Shimada

Publications and source records attributed to Moe Shimada.

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Complementary quantum and classical records of qubit decoherence

Decoherence is usually viewed as the loss of local coherence, but it also writes information into the environment. Here we show that the environment stores this information in two distinct forms. One is a recoverable quantum record: after a transverse qubit measurement, the bath is projected onto a Schr\"odinger-cat-like state in a mode-matched physical collective coordinate. The other is a redundant classical which-path record distributed across physical frequency-band fragments. Using tensor-network simulations of a spin--boson reservoir, we reconstruct the conditional bath Wigner function and find visible negativity. In the chain representation used for the simulations, one natural orbital carries more than $95\%$ of the bath one-body occupation associated with the record. A parity symmetry gives an exact nonperturbative identity between the remaining qubit coherence and the overlap of the two environmental branches, while the classical pointer information forms a Darwinian record across fragments. At finite temperature the quantum record is thermally smoothed. In the pure-dephasing limit it becomes an exact mixture of displaced cat states, and negativity remains visible over the simulated range. These results connect decoherence to phase-space tomography and outline how both records can be observed by qubit readout and collective-mode Wigner tomography.

quant-ph

Quantum-inspired Ising machine using sparsified spin connectivity

Combinatorial optimization problems become computationally intractable as these NP-hard problems scale. We previously proposed extraction-type majority voting logic (E-MVL), a quantum-inspired algorithm using digital logic circuits. E-MVL mimics the thermal spin dynamics of simulated annealing (SA) through controlled sparsification of spin interactions for efficient ground-state search. This study investigates the performance potential of E-MVL through systematic optimization and comprehensive benchmarking against SA. The target problem is the Sherrington-Kirkpatrick (SK) model with bimodal and Gaussian coupling distributions. Through equilibrium state analysis, we demonstrate that the sparsity control mechanism provides a consistent search of the solution space regardless of the problem's coupling distribution (bimodal, Gaussian) or size. E-MVL not only achieves the best performance among all tested algorithms--solving exact solutions up to 1600 spins where the best SA baseline is limited to 400 spins--but also provides insights that significantly improve SA's own temperature scheduling. These results establish E-MVL's dual contribution as both an efficient optimizer and a practical methodology for enhancing SA performance. Moreover, FPGA implementation achieved an approximately 6-fold faster solution speed than SA.

cs.ET

Multi-tasking through quantum annealing

Quantum annealing approximately solves combinatorial optimization problems by leveraging the principles of adiabatic quantum systems. In this approach, the system's Hamiltonian evolves from an initial general state to a problem-specific state. This study introduces multi-tasking quantum annealing (MTQA), a method that enables the parallel processing of multiple optimization problems by embedding them into spatially distinct regions on quantum hardware. MTQA is evaluated using two NP-hard problems: the minimum vertex cover problem (MVCP) and the graph partitioning problem (GPP). This parallel approach optimizes quantum resource utilization by concurrently utilizing idle qubits. The findings demonstrate that MTQA achieves a solution quality comparable to single-problem quantum annealing and classical simulated annealing (SA), while notably reducing the time-to-solution (TTS) metrics. Eigenspectrum analysis further theoretically supports the hypothesis that parallel embedding preserves quantum coherence and does not increase computational complexity by efficiently utilizing available quantum hardware (e.g., qubits and couplers). MTQA enables efficient multitasking in quantum annealing, optimizing hardware utilization and improving throughput for concurrent tasks and demonstrating performance for problems up to 100 nodes in real-world applications.

quant-ph