KY
K.Y. Yu
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4 records found
1
Conference paper
(2026)
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E. Hua, Steven van Ommen, K.Y. Yu, J.S. van Leeuwen, R.K. Bishnoi, Heba Abunahla, S. Nur, S. Feld, R. Ishihara
Accurate quantum state tomography (QST) is vital for calibrating quantum processors but faces exponential scaling challenges. We benchmark seven neural architectures—FCN, CNN, CGAN, Transformer, RNN, RBM, and SVAE—for QST reconstruction using expectation- and probability-based measurements. CNN and CGAN achieve high fidelity (F > 0.99), while SVAE enables efficient event-driven learning. To enhance scalability, memristor-based computation-in-memory (CiM) acceleration is proposed for CNN and SVAE, leveraging analog matrix–vector multiplication in HfO2 crossbars. The fabricated arrays show stable bipolar switching and STDP behavior, advancing energy-efficient, real-time quantum diagnostics through algorithm–hardware co-design.
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Accurate quantum state tomography (QST) is vital for calibrating quantum processors but faces exponential scaling challenges. We benchmark seven neural architectures—FCN, CNN, CGAN, Transformer, RNN, RBM, and SVAE—for QST reconstruction using expectation- and probability-based measurements. CNN and CGAN achieve high fidelity (F > 0.99), while SVAE enables efficient event-driven learning. To enhance scalability, memristor-based computation-in-memory (CiM) acceleration is proposed for CNN and SVAE, leveraging analog matrix–vector multiplication in HfO2 crossbars. The fabricated arrays show stable bipolar switching and STDP behavior, advancing energy-efficient, real-time quantum diagnostics through algorithm–hardware co-design.
Quantum computation represents a promising frontier in the domain of high-performance computing, blending quantum information theory with practical applications to overcome the limitations of classical computation. This study investigates the challenges of manufacturing high-fidelity and scalable quantum processors. Quantum gate set tomography (QGST) is a critical method for characterizing quantum processors and understanding their operational capabilities and limitations. This paper introduces Ml4Qgst as a novel approach to QGST by integrating machine learning techniques, specifically utilizing a transformer neural network model. Adapting the transformer model for QGST addresses the computational complexity of modeling quantum systems. Advanced training strategies, including data grouping and curriculum learning, are employed to enhance model performance, demonstrating significant congruence with ground-truth values. We benchmark this training pipeline on the constructed learning model, to successfully perform QGST for 2 and 3 gates on single-qubit and two-qubit systems, with over-rotation error and depolarizing noise estimation with comparable accuracy to pyGSTi. This research marks a pioneering step in applying deep neural networks to the complex problem of quantum gate set tomography, showcasing the potential of machine learning to tackle nonlinear tomography challenges in quantum computing.
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Quantum computation represents a promising frontier in the domain of high-performance computing, blending quantum information theory with practical applications to overcome the limitations of classical computation. This study investigates the challenges of manufacturing high-fidelity and scalable quantum processors. Quantum gate set tomography (QGST) is a critical method for characterizing quantum processors and understanding their operational capabilities and limitations. This paper introduces Ml4Qgst as a novel approach to QGST by integrating machine learning techniques, specifically utilizing a transformer neural network model. Adapting the transformer model for QGST addresses the computational complexity of modeling quantum systems. Advanced training strategies, including data grouping and curriculum learning, are employed to enhance model performance, demonstrating significant congruence with ground-truth values. We benchmark this training pipeline on the constructed learning model, to successfully perform QGST for 2 and 3 gates on single-qubit and two-qubit systems, with over-rotation error and depolarizing noise estimation with comparable accuracy to pyGSTi. This research marks a pioneering step in applying deep neural networks to the complex problem of quantum gate set tomography, showcasing the potential of machine learning to tackle nonlinear tomography challenges in quantum computing.
Conference paper
(2023)
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R. Ishihara, J. Hermias, S. Neji, K. Y. Yu, M. Van Der Maas, S. Nur, T. Iwai, T. Miyatake, S. Miyahara, More authors...
Quantum computer chip based on spin qubits in diamond uses modules that are entangled with on-chip optical links. This enables an increased connectivity and a negligible crosstalk and error-rate when the number of qubits increases onchip. Here, 3D integration is the key enabling technology for a large-scale integration of the diamond spin qubits with photonic and electronic circuits for routing, control and readout of qubits. There are several engineering challenges to integrate the large number of spins in diamond with the on-chip circuits operating at a cryogenic temperature. In this paper we will address challenges, present recent results and discuss future outlook of the integration technology for realization of a scalable quantum computer based on diamond spin qubits.
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Quantum computer chip based on spin qubits in diamond uses modules that are entangled with on-chip optical links. This enables an increased connectivity and a negligible crosstalk and error-rate when the number of qubits increases onchip. Here, 3D integration is the key enabling technology for a large-scale integration of the diamond spin qubits with photonic and electronic circuits for routing, control and readout of qubits. There are several engineering challenges to integrate the large number of spins in diamond with the on-chip circuits operating at a cryogenic temperature. In this paper we will address challenges, present recent results and discuss future outlook of the integration technology for realization of a scalable quantum computer based on diamond spin qubits.
Conference paper
(2021)
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R. Ishihara, J. Hermias, Y. LI, S. Yu, K. Y. Yu, S. Nur, T. Iwai, T. Miyatake, K. Kawaguchi, Y. Doi, S. Sato
Quantum computer chip based on spin qubits in diamond uses modules that are entangled with on-chip optical links. This enables an increased connectivity and a negligible crosstalk and error-rate when the number of qubits increases on-chip. Here, 3D integration is the key enabling technology for a large-scale integration of the diamond spin qubits with photonic circuits and CMOS electronics for routing, control and readout of qubits. Several engineering challenges exist in order to integrate the large number of spins in diamond with the on-chip circuits operating at a cryogenic temperature. We will review trends, address challenges and discuss future outlook of the integration technology for realization of a scalable quantum computer based on diamond spin qubits.
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Quantum computer chip based on spin qubits in diamond uses modules that are entangled with on-chip optical links. This enables an increased connectivity and a negligible crosstalk and error-rate when the number of qubits increases on-chip. Here, 3D integration is the key enabling technology for a large-scale integration of the diamond spin qubits with photonic circuits and CMOS electronics for routing, control and readout of qubits. Several engineering challenges exist in order to integrate the large number of spins in diamond with the on-chip circuits operating at a cryogenic temperature. We will review trends, address challenges and discuss future outlook of the integration technology for realization of a scalable quantum computer based on diamond spin qubits.