Benchmarking a trapped-ion quantum computer with 30 qubits

• URL: http://arxiv.org/abs/2308.05071v2
• Date: Sat, 02 Nov 2024 22:59:00 GMT
• Title: Benchmarking a trapped-ion quantum computer with 30 qubits
• Authors: Jwo-Sy Chen, Erik Nielsen, Matthew Ebert, Volkan Inlek, Kenneth Wright, Vandiver Chaplin, Andrii Maksymov, Eduardo Páez, Amrit Poudel, Peter Maunz, John Gamble,
• Abstract summary: We benchmark a single-chain 30-qubit trapped-ion quantum computer with all-to-all operations. We build a system-level model to predict the application benchmarking data through direct simulation. This highlights that as quantum computers move toward larger and higher-quality devices, characterization becomes more challenging.
• Score: 0.2276773223605655
• License:
• Abstract: Quantum computers are rapidly becoming more capable, with dramatic increases in both qubit count and quality. Among different hardware approaches, trapped-ion quantum processors are a leading technology for quantum computing, with established high-fidelity operations and architectures with promising scaling. Here, we demonstrate and thoroughly benchmark the IonQ Forte system: configured as a single-chain 30-qubit trapped-ion quantum computer with all-to-all operations. We assess the performance of our quantum computer operation at the component level via direct randomized benchmarking (DRB) across all 30 choose 2 = 435 gate pairs. We then show the results of application-oriented benchmarks and show that the system passes the suite of algorithmic qubit (AQ) benchmarks up to #AQ 29. Finally, we use our component-level benchmarking to build a system-level model to predict the application benchmarking data through direct simulation. While we find that the system-level model correlates with the experiment in predicting application circuit performance, we note quantitative discrepancies indicating significant out-of-model errors, leading to higher predicted performance than what is observed. This highlights that as quantum computers move toward larger and higher-quality devices, characterization becomes more challenging, suggesting future work required to push performance further.

Related papers

• Data augmentation experiments with style-based quantum generative adversarial networks on trapped-ion and superconducting-qubit technologies [0.0]
  This work demonstrates, for the first time, how the quantum generator architecture for the style-based quantum generative adversarial network (qGAN) can be implemented. The style-based qGAN, proposed in 2022, generalizes the state of the art for qGANs and allows for shallow-depth networks. The results obtained on both devices are of comparable quality, with the aria-1 device delivering somewhat more accurate results than the ibm_torino device.
  arXiv  Detail & Related papers  (2024-05-07T15:26:51Z)
• Majorization-based benchmark of the complexity of quantum processors [105.54048699217668]
  We numerically simulate and characterize the operation of various quantum processors. We identify and assess quantum complexity by comparing the performance of each device against benchmark lines. We find that the majorization-based benchmark holds as long as the circuits' output states have, on average, high purity.
  arXiv  Detail & Related papers  (2023-04-10T23:01:10Z)
• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• SupermarQ: A Scalable Quantum Benchmark Suite [3.6806897290408305]
  SupermarQ is a scalable, hardware-agnostic quantum benchmark suite which uses application-level metrics to measure performance. SupermarQ is the first attempt to systematically apply techniques from classical benchmarking methodology to the quantum domain. We envision that quantum benchmarking will encompass a large cross-community effort built on open source, constantly evolving benchmark suites.
  arXiv  Detail & Related papers  (2022-02-22T17:24:07Z)
• Quantum circuit architecture search on a superconducting processor [56.04169357427682]
  Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry. However, the ansatz exploited in modern VQAs is incapable of balancing the tradeoff between expressivity and trainability. We demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique to enhance VQAs on an 8-qubit superconducting quantum processor.
  arXiv  Detail & Related papers  (2022-01-04T01:53:42Z)
• Application-Oriented Performance Benchmarks for Quantum Computing [0.0]
  benchmarking suite is designed to be readily accessible to a broad audience of users. Our methodology is constructed to anticipate advances in quantum computing hardware that are likely to emerge in the next five years.
  arXiv  Detail & Related papers  (2021-10-07T01:45:06Z)
• Scalable Benchmarks for Gate-Based Quantum Computers [5.735035463793008]
  We develop and release an advanced quantum benchmarking framework. It measures the performance of universal quantum devices in a hardware-agnostic way. We present the benchmark results of twenty-one different quantum devices from IBM, Rigetti and IonQ.
  arXiv  Detail & Related papers  (2021-04-21T18:00:12Z)
• Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer [55.41644538483948]
  We implement a quantum-circuit based generative model to learn and sample the prior distribution of a Generative Adversarial Network. We train this hybrid algorithm on an ion-trap device based on $171$Yb$+$ ion qubits to generate high-quality images.
  arXiv  Detail & Related papers  (2020-12-07T18:51:28Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack [0.0]
  Quantum computing systems need to be benchmarked in terms of practical tasks they would be expected to do. We propose 3 "application-motivated" circuit classes for benchmarking: deep, shallow, and square. We quantify the performance of quantum computing system in running circuits from these classes using several figures of merit.
  arXiv  Detail & Related papers  (2020-06-01T21:21:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.