Cross-platform hardware benchmark of style-based quantum GANs for data augmentation on superconducting and trapped-ion processors
arXiv:2405.04401 · doi:10.1063/5.0322308
Abstract
In the noisy intermediate-scale quantum era, controlled benchmarks of quantum machine-learning workloads across hardware modalities are needed to quantify how given algorithms behave under native provider execution stacks. This work presents such a benchmark for the style-based quantum generative adversarial network (qGAN) on a high-energy physics data-augmentation task. We compare two commercially available gate-model quantum computers: the IBM ibm_torino hardware, based on superconducting transmon qubits from the Heron chip and the IonQ aria-1 hardware, based on trapped-ion qubits. The generator architecture and trained parameters are kept fixed, and built-in mitigation is disabled when possible. We report quality and runtime metrics under each provider's native stack. The workflow uses circuit replication across available qubits, up to 48 on IBM and 24 on IonQ, to reduce the number of submitted jobs required for the target sample set. To our knowledge, this is one of the first controlled style-based qGAN hardware-to-hardware comparisons for this data-augmentation task. We observe that both platforms complete the task successfully, with marginal Kullback-Leibler divergences somewhat lower on aria-1, while end-to-end runtime is significantly shorter on ibm_torino. These results are an application-specific tradeoff benchmark, not a claim of algorithmic novelty.
28 pages, 11 figures, 2 tables. v2: Major revision including change of title to reflect better scope; added affiliation; matches published version
References in corpus (36)
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- Quantum Machine Learning
- Charge insensitive qubit design derived from the Cooper pair box
- Variational Quantum Algorithms
- A Quantum Engineer's Guide to Superconducting Qubits
- Noisy intermediate-scale quantum (NISQ) algorithms
- Parameterized quantum circuits as machine learning models
- Entanglement and quantum computation with ions in thermal motion
- Quantum generative adversarial learning
- Demonstration of the trapped-ion quantum-CCD computer architecture
- Benchmarking an 11-qubit quantum computer
- Quantum generative adversarial networks
- Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
- A simple all-microwave entangling gate for fixed-frequency superconducting qubits
- Fast Scalable State Measurement with Superconducting Qubits
- A generative modeling approach for benchmarking and training shallow quantum circuits
- The Future of Quantum Computing with Superconducting Qubits
- The automation of next-to-leading order electroweak calculations
- Theory of overparametrization in quantum neural networks
- Quantum generative adversarial learning in a superconducting quantum circuit
- Quantum generative adversarial network for generating discrete distribution
- The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine
- Qibo: a framework for quantum simulation with hardware acceleration
- Benchmarking a trapped-ion quantum computer with 30 qubits
- Transmon qubit readout fidelity at the threshold for quantum error correction without a quantum-limited amplifier
- High fidelity state preparation and measurement of ion hyperfine qubits with I > 1/2
- Generative model benchmarks for superconducting qubits
- Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Era
- Error per single-qubit gate below in a superconducting qubit
- Quantum algorithms: A survey of applications and end-to-end complexities
- Style-based quantum generative adversarial networks for Monte Carlo events
- Generative Quantum Learning of Joint Probability Distribution Functions
- Optimization of -Layer Systems for Josephson Junctions from a Microstructure Point of View
- Trainability barriers and opportunities in quantum generative modeling
- Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits
- Parameterized quantum circuits as universal generative models for continuous multivariate distributions