arXiv subjects

Masatoshi Ishii

Publications and source records attributed to Masatoshi Ishii.

3 recordsLinked to original sources

arXiv2026

Implementation and verification of coherent error suppression using randomized compiling for Grover's algorithm on a trapped-ion device

In near-term quantum computations that do not employ fault tolerant error correction, noise can proliferate rapidly, corrupting the quantum state and making results unreliable. These errors originate from both decoherence and control imprecision and the latter can manifest as coherent error that is especially detrimental. In the pre-fault tolerant setting, previous work has shown that different error suppression methods have shown promising complementary advantages but highly variable performance under different algorithmic and error model conditions. Here, we evaluate the effectiveness of several error suppression methods under varying algorithmic settings, both theoretically with numerical simulations and experimentally on a trapped-ion quantum computer. For our case study, we explore a range of output states under Grover's algorithm quantum circuits containing up to 10 qubits and 28 two-qubit gates with varying output state features. We explore the complementary effectiveness of randomized compiling and algorithm error detection, where the latter is realized via post-selection on ancillary qubits that ideally return to the ground state at the end of each circuit. In all settings, combining randomized compiling and error detection yields the largest suppression of error, indicating that these methods are most effective when combined to extend the capabilities of near-term devices for moderately deep circuits. We demonstrate for the first time significant suppression of coherent error on a trapped-ion platform, and moreover achieve this outcome via cloud access. However our results highlight that the degree of error suppression depends sensitively on the nature of the error model and the algorithm instance.

quant-ph↗

arXiv2023

Synergetic quantum error mitigation by randomized compiling and zero-noise extrapolation for the variational quantum eigensolver

We propose a quantum error mitigation strategy for the variational quantum eigensolver (VQE) algorithm. We find, via numerical simulation, that very small amounts of coherent noise in VQE can cause substantially large errors that are difficult to suppress by conventional mitigation methods, and yet our proposed mitigation strategy is able to significantly reduce these errors. The proposed strategy is a combination of previously reported techniques, namely randomized compiling (RC) and zero-noise extrapolation (ZNE). Intuitively, randomized compiling turns coherent errors in the circuit into stochastic Pauli errors, which facilitates extrapolation to the zero-noise limit when evaluating the cost function. Our numerical simulation of VQE for small molecules shows that the proposed strategy can mitigate energy errors induced by various types of coherent noise by up to two orders of magnitude.

quant-ph↗

arXiv2020

Ultra-low power on-chip learning of speech commands with phase-change memories

Embedding artificial intelligence at the edge (edge-AI) is an elegant solution to tackle the power and latency issues in the rapidly expanding Internet of Things. As edge devices typically spend most of their time in sleep mode and only wake-up infrequently to collect and process sensor data, non-volatile in-memory computing (NVIMC) is a promising approach to design the next generation of edge-AI devices. Recently, we proposed an NVIMC-based neuromorphic accelerator using the phase change memories (PCMs), which we call as Raven. In this work, we demonstrate the ultra-low-power on-chip training and inference of speech commands using Raven. We showed that Raven can be trained on-chip with power consumption as low as 30~uW, which is suitable for edge applications. Furthermore, we showed that at iso-accuracies, Raven needs 70.36x and 269.23x less number of computations to be performed than a deep neural network (DNN) during inference and training, respectively. Owing to such low power and computational requirements, Raven provides a promising pathway towards ultra-low-power training and inference at the edge.

eess.AS↗