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Takashi Takemoto

Publications and source records attributed to Takashi Takemoto.

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Smooth velocity shuttling for suppressing valley excitations in disordered Si/SiGe quantum dots

Coherent electron shuttling is a key requirement for realizing scalable silicon quantum computing architectures. However, in silicon qubits, the existence of nearly degenerate conduction-band valleys poses a significant challenge because non-adiabatic transitions to excited valley states cause spin dephasing via spin-valley mixing. In this paper, we propose a smooth velocity shuttling protocol to suppress these valley excitations. By mapping the time-domain design of the shuttling velocity profile onto the design problem of window functions in signal processing, we establish an analytical and intuitive design guideline that does not require computationally expensive numerical optimization. We demonstrate that the high-frequency sidelobes of the shuttling velocity spectrum can be effectively suppressed by applying a frequency-modulated gate voltage based on the Tukey window. Through statistical numerical simulations incorporating realistic spatial randomness of the valley landscape, we show that the proposed smooth velocity control significantly reduces the average spin infidelity in the moderate-to-low disorder regime ($|Δ_0|/σ_Δ\simeq \mathcal{O}(1)$). Our results underscore that this simple, control-level velocity shaping provides a robust pathway toward high-fidelity spin transport in large-scale silicon quantum processors.

quant-ph

Digital-Controlled Method of Conveyor-Belt Spin Shuttling in Silicon for Large-Scale Quantum Computation

We propose a digital-controlled conveyor-belt shuttling method for silicon-based quantum processors, addressing the scalability challenges of conventional analog sinusoidal implementations. By placing a switch matrix and low-pass filters in a cryogenic environment, our approach synthesizes near-sinusoidal waveforms from a limited number of DC voltage levels. Simulation results demonstrate that the proposed method achieves fidelity comparable to analog methods while significantly reducing wiring overhead and power dissipation. Moreover, the design offers robustness against device-level variations, enabling large-scale integration of high-fidelity spin shuttling for quantum error correction.

quant-ph

Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102,400 nodes

Minor embedding heuristics have become an indispensable tool for compiling problems in quadratically unconstrained binary optimization (QUBO) into the hardware graphs of quantum and CMOS annealing processors. While recent embedding heuristics have been developed for annealers of moderate size (about 2000 nodes) the size of the latest CMOS annealing processor (with 102,400 nodes) poses entirely new demands on the embedding heuristic. This raises the question, if recent embedding heuristics can maintain meaningful embedding performance on hardware graphs of increasing size. Here, we develop an improved version of the probabilistic-swap-shift-annealing (PSSA) embedding heuristic [which has recently been demonstrated to outperform the standard embedding heuristic by D-Wave Systems (Cai et al., 2014)] and evaluate its embedding performance on hardware graphs of increasing size. For random-cubic and Barabasi-Albert graphs we find the embedding performance of improved PSSA to consistently exceed the threshold of the best known complete graph embedding by a factor of 3.2 and 2.8, respectively, up to hardware graphs with 102,400 nodes. On the other hand, for random graphs with constant edge density not even improved PSSA can overcome the deterministic threshold guaranteed by the existence of the best known complete graph embedding. Finally, we prove a new upper bound on the maximal embeddable size of complete graphs into hardware graphs of CMOS annealers and show that the embedding performance of its currently best known complete graph embedding has optimal order for hardware graphs with fixed coordination number.

quant-ph