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Bulletin of the American Physical Society

A Quantum Kernel and Configurable Electronic System for Scalable Quantum Control and Measurement

Venu Reddy, Mohsen Najafi Yazdi, Guillaume Duclos-Cianci, Fernando Petruzziello, Alireza Najafi­-Yazdi

Published Mar 7, 2018• 0 citations
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Qubits are the basic building blocks of a quantum processor which require electromagnetic pulses in giga hertz frequency range and latency in nanoseconds for control and readout. In this paper, we address three main challenges associated with room temperature electronics used for controlling and measuring superconducting qubits: scalability, direct microwave synthesis, and a unified user interface. To tackle these challenges, we have developed SQ-CARS, a system based on the ZCU111 evaluation kit. SQ-CARS is designed to be scalable, configurable, and phase synchronized, providing multi-qubit control and readout capabilities. The system offers an interactive Python framework, making it user-friendly. Scalability to a larger number of qubits is achieved by deterministic synchronization of multiple channels. The system supports direct synthesis of arbitrary vector microwave pulses using the second-Nyquist zone technique, from 4 to 9 GHz. It also features on-board data processing like tunable low pass filters and configurable rotation blocks, enabling lock-in detection and low-latency active feedback for quantum experiments. All control and readout features are accessible through an on-board Python framework. To validate the performance of SQ-CARS, we conducted various time-domain measurements to characterize a superconducting transmon qubit. Our results were compared against traditional setups commonly used in similar experiments. With deterministic synchronisation of control and readout channels, and an open-source approach for programming, SQ-CARS paves the way for advanced experiments with superconducting qubits.
Quantum support vector machines are classification algorithms that rely on quantum-generated kernels. The fidelity quantum kernel commonly used in quantum support vector machines suffers from exponential concentration as system size increases, preventing an efficient scaling beyond fewqubit systems. We introduce the Hamming quantum kernel, a classical post-processing method that is based on the same measurement outcomes as the fidelity quantum kernel. However, it avoids the exponential concentration problem by using the full measurement statistics rather than a single fidelity value. We evaluate the approach on both classical data (MNIST) and synthetic data generated from quantum circuits, using systems ranging from 2 to 27 qubits. Throughout the simulations, the Hamming quantum kernel outperforms the fidelity quantum kernel whenever 15 or more qubits are used. Furthermore, for synthetic quantum data, our method consistently outperforms the classical Gaussian kernel. This demonstrates that the Hamming quantum kernel improves the expressivity and robustness at larger qubit scales without requiring any additional quantum ressources.
Research on near-term quantum machine learning has explored how classical machine learning algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely classical counterparts. Although theoretical work has shown provable advantage on synthetic data sets, no work done to date has studied empirically whether quantum advantage is attainable and with what data. In this paper, we report the first systematic investigation of empirical quantum advantage (EQA) in healthcare and life sciences and propose an end-to-end framework to study EQA. We selected electronic health records (EHRs) data subsets and created a configuration space of 5-20 features and 200-300 training samples. For each configuration coordinate, we trained classical support vector machine (SVM) models based on radial basis function (RBF) kernels and quantum models with custom kernels using an IBM quantum computer, making this one of the largest quantum machine learning experiments to date. We empirically identified regimes where quantum kernels could provide advantage and introduced a terrain ruggedness index, a metric to help quantitatively estimate how the accuracy of a given model will perform. The generalizable framework introduced here represents a key step towards a priori identification of data sets where quantum advantage could exist.