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Yuanzheng Paul Tan

Publications and source records attributed to Yuanzheng Paul Tan.

8 recordsLinked to original sources

Qibocal: an open-source framework for calibration of self-hosted quantum devices

Calibration of quantum devices is fundamental to successfully deploy quantum algorithms on current available quantum hardware. We present Qibocal, an open-source software library to perform calibration and characterization of superconducting quantum devices within the Qibo framework. Qibocal completes the Qibo middleware framework by providing all necessary tools to easily (re)calibrate self-hosted quantum platforms. After presenting the layout and the features of the library, we give an overview on some of the protocols implemented to perform single and two-qubit gates calibration. Finally, we present applications involving recalibration and monitoring of superconducting platforms.

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Digital Predistortion for Flux Control of Tunable Superconducting Qubits

Flux-tunable superconducting qubits rely on fast flux control pulses to implement two-qubit entangling quantum gates, a key building block for quantum algorithms. However, distortion effects introduced by non-ideal control electronics, parasitic components, and the cryogenic quantum chip response can all degrade the gate fidelity. We present a digital predistortion (DPD) framework for characterizing and then compensating for these distortions using a combination of infinite impulse response (IIR) and finite impulse response (FIR) filters. Experiments on a flux-tunable quantum processing unit (QPU) demonstrate a successful correction of step-response distortions on the flux-control line, with a compensated control signal showing only sub-percent deviations from the ideal target linear behavior. The demonstrated method enables automated rapid calibration of flux control channels for superconducting QPUs.

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Qiboml: towards the orchestration of quantum-classical machine learning

We present Qiboml, an open-source software library for orchestrating quantum and classical components in hybrid machine learning workflows. Building on Qibo's quantum computing capabilities and integrating with popular machine learning frameworks such as TensorFlow and PyTorch, Qiboml enables the construction of quantum and hybrid models that can run on a broad range of backends: (i) multi-threaded CPUs, GPUs, and multi-GPU systems for simulation with statevector or tensor network methods; (ii) quantum processing units, both on-premise and through cloud providers. In this paper, we showcase its functionalities, including diverse simulation options, noise-aware simulations, and real-time error mitigation and calibration.

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Characterizing Noise in Controlling Superconducting Qubits

Meaningful quantum computing is currently bottlenecked by the error rates of current generation Noisy Intermediate Scale Quantum (NISQ) devices. To improve the fidelity of the quantum logic gates, it is essential to recognize the contributions of various sources of errors, including background noise. In this work, we investigate the effects of noise when applied to superconducting qubit control pulses to observe the dependency of the gate fidelity with the signal-to-noise ratio (SNR). We propose a model on how the noise of the control electronics interacts with the qubit system and demonstrate a method for characterizing the noise environment of the qubit control.

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An open-source framework for quantum hardware control

The development of quantum computers needs reliable quantum hardware and tailored software for controlling electronics specific to various quantum platforms. Middleware is a type of computer software program that aims to provide standardized software tools across the entire pipeline, from high level execution of quantum computing algorithms to low level driver instructions tailored to specific experimental setups, including instruments. This paper presents updates to Qibolab, a software library that leverages Qibo capabilities to execute quantum algorithms on self hosted quantum hardware platforms. Qibolab offers an application programming interface (API) for instrument control through arbitrary pulses and driver operations including sweepers. This paper offers an overview of the new features implemented in Qibolab, including the redefined boundaries between platform and channel classes, the integration of an emulator for simulating quantum hardware behaviour, and it shows updated execution times benchmarks for superconducting single qubit calibration routines.

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Qibolab: an open-source hybrid quantum operating system

We present Qibolab, an open-source software library for quantum hardware control integrated with the Qibo quantum computing middleware framework. Qibolab provides the software layer required to automatically execute circuit-based algorithms on custom self-hosted quantum hardware platforms. We introduce a set of objects designed to provide programmatic access to quantum control through pulses-oriented drivers for instruments, transpilers and optimization algorithms. Qibolab enables experimentalists and developers to delegate all complex aspects of hardware implementation to the library so they can standardize the deployment of quantum computing algorithms in a extensible hardware-agnostic way, using superconducting qubits as the first officially supported quantum technology. We first describe the status of all components of the library, then we show examples of control setup for superconducting qubits platforms. Finally, we present successful application results related to circuit-based algorithms.

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ICARUS-Q: Integrated Control and Readout Unit for Scalable Quantum Processors

We present a control and measurement setup for superconducting qubits based on Xilinx 16-channel radio-frequency system-on-chip (RFSoC) device. The proposed setup consists of four parts: multiple RFSoC boards, a setup to synchronise every digital to analog converter (DAC), and analog to digital converter (ADC) channel across multiple boards, a low-noise direct current (DC) supply for tuning the qubit frequency and cloud access for remotely performing experiments. We also design the setup to be free of physical mixers. The RFSoC boards directly generate microwave pulses using sixteen DAC channels up to the third Nyquist zone which are directly sampled by its eight ADC channels between the fifth and the ninth zones.

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Ghost factors in Gauss-sum factorization with transmon qubits

A challenge in the Gauss sums factorization scheme is the presence of ghost factors - non-factors that behave similarly to actual factors of an integer - which might lead to the misidentification of non-factors as factors or vice versa, especially in the presence of noise. We investigate Type II ghost factors, which are the class of ghost factors that cannot be suppressed with techniques previously laid out in the literature. The presence of Type II ghost factors and the coherence time of the qubit set an upper limit for the total experiment time, and hence the largest factorizable number with this scheme. Discernability is a figure of merit introduced to characterize this behavior. We introduce preprocessing as a strategy to increase the discernability of a system, and demonstrate the technique with a transmon qubit. This can bring the total experiment time of the system closer to its decoherence limit, and increase the largest factorizable number.

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