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Bulletin of the American Physical Society

A method of extracting operating parameters of a quantum circuit

Eyob Alebachew Sete, Maxwell Block, Michael G. Scheer, Cris Zanoci, Mehrnoosh Vahidpour, D Thompson, Chad T. Rigetti

Published Mar 16, 2016• 0 citations
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Abstract Efficiently compiling quantum operations remains a major bottleneck in scaling quantum computing. Today’s state-of-the-art methods achieve low compilation error by combining search algorithms with gradient-based parameter optimization, but they incur long runtimes and require multiple calls to quantum hardware or expensive classical simulations, making their scaling prohibitive. Recently, machine-learning models have emerged as an alternative, though they are currently restricted to discrete gate sets. Here, we introduce a multimodal denoising diffusion model that simultaneously generates a circuit’s structure and its continuous parameters for compiling a target unitary. It leverages two independent diffusion processes, one for discrete gate selection and one for parameter prediction. We benchmark the model over different experiments, analyzing the method’s accuracy across varying qubit counts and circuit depths, showcasing the ability of the method to outperform existing approaches in gate counts and under noisy conditions. Additionally, we show that a simple post-optimization scheme allows us to significantly improve the generated ansätze. Finally, by exploiting its rapid circuit generation, we create large datasets of circuits for particular operations and use these to extract valuable heuristics that can help us discover new insights into quantum circuit synthesis. Code and models are publicly available at https://github.com/FlorianFuerrutter/genQC .
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An improved variational quantum shadow learning (VQSL) method is presented for the fault diagnosis of transformers. Localized variational quantum circuits for shadow feature extraction and a fully connected neural network are designed. Firstly, the dissolved gas analysis data is mapped into a high-dimensional Hilbert space, then, shadow features between adjacent and non-adjacent quantum bits are extracted simultaneously by multiple localized quantum circuits through parameter sharing, which reduces the number of training parameters and improves the model accuracy. The key factors that influence the performance of the VQSL method are discussed, which include quantum coding methods, the width and the depth of localized quantum circuits, the size of the global quantum bits, and the noise levels. Experimental results indicate that the improved VQSL method achieves a diagnostic accuracy of 95.4%, surpassing the commonly used methods such as QNN, GNN, CNN, SAE, SVM, and ratio methods. Even in a noise interference situation, the proposed method consistently maintains a high fault diagnosis accuracy.
We introduce two classes of lightweight, adaptive calibration protocols for quantum computers that leverage fast feedback. The first enables shot-by-shot updates to device parameters using measurement outcomes from simple, indefinite-outcome quantum circuits. This low-latency approach supports rapid tuning of one or more parameters in real time to mitigate drift. The second protocol updates parameters after collecting measurements from definite-outcome circuits (e.g.~syndrome extraction circuits for quantum error correction), balancing efficiency with classical control overheads. We use numerical simulations to demonstrate that both methods can calibrate 1- and 2-qubit gates rapidly and accurately even in the presence of decoherence, state preparation and measurement (SPAM) errors, and parameter drift. We propose and demonstrate effective adaptive strategies for tuning the hyperparameters of both protocols. Finally, we demonstrate the feasibility of real-time in-situ calibration of qubits performing quantum error correction, using only syndrome data, via numerical simulations of syndrome extraction in the [[5,1,3]] code.