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Register a new userQuantum-tailored machine-learning characterization of a superconducting qubit
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Machine learning (ML) is a promising approach for performing challenging quantum-information tasks such as device characterization, calibration and control. ML models can train directly on the data produced by a quantum device while remaining agnostic to the quantum nature of the learning task. However, these generic models lack physical interpretability and usually require large datasets in order to learn accurately. Here we incorporate features of quantum mechanics in the design of our ML approach to characterize the dynamics of a quantum device and learn device parameters. This physics-inspired approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data obtained from continuous weak measurement of a driven superconducting transmon qubit. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task, thus laying the groundwork for more scalable characterization techniques.
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Precise nanofabrication represents a critical challenge to developing semiconductor quantum-dot qubits for practical quantum computation. Here, we design and train a convolutional neural network to interpret in-line scanning electron micrographs and quantify qualitative features affecting device functionality. The high-throughput strategy is exemplified by optimizing a model lithographic process within a five-dimensional design space and by demonstrating a new approach to address lithographic proximity effects. The present results emphasize the benefits of machine learning for developing robust processes, shortening development cycles, and enforcing quality control during qubit fabrication.
The length of time that a quantum system can exist in a superposition state is determined by how strongly it interacts with its environment. This interaction entangles the quantum state with the inherent fluctuations of the environment. If these fluctuations are not measured, the environment can be viewed as a source of noise, causing random evolution of the quantum system from an initially pure state into a statistical mixture-a process known as decoherence. However, by accurately measuring the environment in real time, the quantum system can be maintained in a pure state and its time evolution described by a quantum trajectory conditioned on the measurement outcome. We employ weak measurements to monitor a microwave cavity embedding a superconducting qubit and track the individual quantum trajectories of the system. In this architecture, the environment is dominated by the fluctuations of a single electromagnetic mode of the cavity. Using a near-quantum-limited parametric amplifier, we selectively measure either the phase or amplitude of the cavity field, and thereby confine trajectories to either the equator or a meridian of the Bloch sphere. We perform quantum state tomography at discrete times along the trajectory to verify that we have faithfully tracked the state of the quantum system as it diffuses on the surface of the Bloch sphere. Our results demonstrate that decoherence can be mitigated by environmental monitoring and validate the foundations of quantum feedback approaches based on Bayesian statistics. Moreover, our experiments suggest a new route for implementing what Schrodinger termed quantum steering-harnessing action at a distance to manipulate quantum states via measurement.
The interaction of photons and coherent quantum systems can be employed to detect electromagnetic radiation with remarkable sensitivity. We introduce a quantum radiometer based on the photon-induced-dephasing process of a superconducting qubit for sensing microwave radiation at the sub-unit-photon level. Using this radiometer, we demonstrated the radiative cooling of a 1-K microwave resonator and measured its mode temperature with an uncertainty ~0.01 K. We have thus developed a precise tool for studying the thermodynamics of quantum microwave circuits, which provides new solutions for calibrating hybrid quantum systems and detecting candidate particles for dark matter.
Bidirectional conversion of electrical and optical signals lies at the foundation of the global internet. Such converters are employed at repeater stations to extend the reach of long-haul fiber optic communication systems and within data centers to exchange high-speed optical signals between computers. Likewise, coherent microwave-to-optical conversion of single photons would enable the exchange of quantum states between remotely connected superconducting quantum processors, a promising quantum computing hardware platform. Despite the prospects of quantum networking, maintaining the fragile quantum state in such a conversion process with superconducting qubits has remained elusive. Here we demonstrate the conversion of a microwave-frequency excitation of a superconducting transmon qubit into an optical photon. We achieve this using an intermediary nanomechanical resonator which converts the electrical excitation of the qubit into a single phonon by means of a piezoelectric interaction, and subsequently converts the phonon to an optical photon via radiation pressure. We demonstrate optical photon generation from the qubit with a signal-to-noise greater than unity by recording quantum Rabi oscillations of the qubit through single-photon detection of the emitted light over an optical fiber. With proposed improvements in the device and external measurement set-up, such quantum transducers may lead to practical devices capable of realizing new hybrid quantum networks, and ultimately, distributed quantum computers.
Quantum feedback is a technique for measuring a qubit and applying appropriate feedback depending on the measurement results. Here, we propose a new on-chip quantum feedback method where the measurement-result information is not taken from the chip to the outside of a dilution refrigerator. This can be done by using a selective qubit-energy shift induced by measurement apparatus. We demonstrate on-chip quantum feedback and succeed in the rapid initialization of a qubit by flipping the qubit state only when we detect the ground state of the qubit. The feedback loop of our quantum feedback method closed on a chip, and so the operating time needed to control a qubit is of the order of 10 ns. This operating time is shorter than with the convectional off-chip feedback method. Our on-chip quantum feedback technique opens many possibilities such as an application to quantum information processing and providing an understanding of the foundation of thermodynamics for quantum systems.
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