Generative model benchmarks for superconducting qubits

Abstract

In this work we experimentally demonstrate how generative model training can be used as a benchmark for small ($<5$ qubits) quantum devices. Performance is quantified using three data analytic metrics: the Kullbeck-Leiber divergence, and two adaptations of the F1 score. Using the $2\times 2$ Bars and Stripes dataset, we determine optimal circuit constructions for generative model training on superconducting qubits by including hardware connectivity constraints into circuit design. We show that on noisy hardware sparsely connected, shallow circuits out-perform denser counterparts.

arXiv commentrevised to correct typos, correct qBAS analysis and update figures

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Cited by in corpus (29)

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• Parameterized quantum circuits as machine learning models
• A generative modeling approach for benchmarking and training shallow quantum circuits
• Training of Quantum Circuits on a Hybrid Quantum Computer
• Gate Set Tomography
• Challenges and Opportunities of Near-Term Quantum Computing Systems
• The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine
• Quantum neuromorphic computing
• Quantum Machine Learning: from physics to software engineering
• Variational inference with a quantum computer
• Style-based quantum generative adversarial networks for Monte Carlo events
• Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack
• Trainability barriers and opportunities in quantum generative modeling
• End-to-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics
• A performance characterization of quantum generative models
• Quantum State Discrimination Using Noisy Quantum Neural Networks
• Unsupervised Quantum Circuit Learning in High Energy Physics
• Quantum Optimization for Training Quantum Neural Networks
• A Continuous Variable Born Machine
• F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
• Variational-quantum-eigensolver-inspired optimization for spin-chain work extraction
• Characterizing randomness in parameterized quantum circuits through expressibility and average entanglement
• Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
• Error-mitigated photonic quantum circuit Born machine
• Extending C++ for Heterogeneous Quantum-Classical Computing
• Cross-platform hardware benchmark of style-based quantum GANs for data augmentation on superconducting and trapped-ion processors
• Enabling Retargetable Optimizing Compilers for Quantum Accelerators via a Multi-Level Intermediate Representation
• Fundamental Machine Learning Routines as Quantum Algorithms on a Superconducting Quantum Computer
• Mode connectivity in the QCBM loss landscape