Quantum Optimal Control (QOC) frameworks are powerful tools for compiling quantum circuits into low-latency hardware control pulses, but recent studies suffer from two critical limitations: lengthy compilation times and potential logical inconsistencies from flawed gate grouping strategies. In this work, we introduce d'ArQ, a novel QOC framework that solves these challenges. (i) We identify and resolve the causality problem, a flaw in greedy partitioning that can produce invalid schedules, by introducing a DAG-based grouping algorithm with assigning mergeability to each group so that it guarantees logical correctness. (ii) To mitigate compilation times, we use a pre-computed library of pulses derived from random unitary matrices to provide a high-quality random initialization for pulse optimization. (iii) Diverging from prior work based on GRAPE, d'ArQ is built on the GOAT algorithm. We demonstrate that the choice of analytic basis is a critical hyperparameter and introduce a heuristic cost model to dynamically select the optimal basis for each synthesis task, improving pulse performance. When evaluated against the state-of-the-art baseline PAQOC on a realistic, inhomogeneous hardware model, d'ArQ demonstrates superior performance. Notably, d'ArQ reduces circuit latency up to 22.8% and compilation time up to 56.8%, establishing a more robust and physically realistic path for circuit compilation.

In this work, we propose a novel method for modeling numerous speakers, which enables expressing the overall characteristics of speakers in detail like a trained multi-speaker model without additional training on the target speaker’s dataset. Although various works with similar purposes have been actively studied, their performance has not yet reached that of trained multi-speaker models due to their fundamental limitations. To overcome previous limitations, we propose effective methods for feature learning and representing target speakers’ speech characteristics by discretizing the features and conditioning them to a speech synthesis model. Our method obtained a significantly higher similarity mean opinion score (SMOS) in subjective similarity evaluation than seen speakers of a high-performance multi-speaker model, even with unseen speakers. The proposed method also outperforms a zero-shot method by significant margins. Furthermore, our method shows remarkable performance in generating new artificial speakers. In addition, we demonstrate that the encoded latent features are sufficiently informative to reconstruct an original speaker’s speech completely. It implies that our method can be used as a general methodology to encode and reconstruct speakers’ characteristics in various tasks.

This paper introduces a two-pass on-device automatic speech recognition (ASR) system, which is developed for commercialized devices. The first pass of the system is based on a causal Conformer-transducer model to generate partial results from the input audio stream. After processing an entire input utterance in the first pass, the candidates for the final result are rescored with a full-context attention model in the second pass. To minimize the computational overhead from rescoring, we compress the full-context model by applying knowledge distillation (KD). The total model size is reduced by 35% after KD with a 0.02% absolute loss in word error rate (WER). We also introduce decoding techniques to boost the accuracy on the test cases mismatched with the distribution of the training set. The techniques include on-device personal adaptation, spell correction and handling incorrectly segmented speech, which solve the critical issues for production-grade systems. The whole system including the two-pass end-to-end (E2E) model and a language model (LM) occupies 72MB in storage after 8-bit quantization. We demonstrate the entire system on mobile devices and report results on test sets collected from the production environment. The developed system achieves 5.65% WER which surpasses the baseline system with 39% relative WER improvement.