Scalable randomized benchmarking of quantum computers using mirror circuits

• URL: http://arxiv.org/abs/2112.09853v2
• Date: Mon, 10 Oct 2022 15:36:11 GMT
• Title: Scalable randomized benchmarking of quantum computers using mirror circuits
• Authors: Timothy Proctor, Stefan Seritan, Kenneth Rudinger, Erik Nielsen, Robin Blume-Kohout, Kevin Young
• Abstract summary: We show how to perform scalable, robust, and flexible randomized benchmarking of Clifford gates. We show that this technique approximately estimates the infidelity of an average many-qubit logic layer. We then use up to 16 physical qubits of a cloud quantum computing platform to demonstrate that our technique can reveal and quantify crosstalk errors.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The performance of quantum gates is often assessed using some form of randomized benchmarking. However, the existing methods become infeasible for more than approximately five qubits. Here we show how to use a simple and customizable class of circuits -- randomized mirror circuits -- to perform scalable, robust, and flexible randomized benchmarking of Clifford gates. We show that this technique approximately estimates the infidelity of an average many-qubit logic layer, and we use simulations of up to 225 qubits with physically realistic error rates in the range 0.1-1% to demonstrate its scalability. We then use up to 16 physical qubits of a cloud quantum computing platform to demonstrate that our technique can reveal and quantify crosstalk errors in many-qubit circuits.

Related papers

• Supervised binary classification of small-scale digits images with a trapped-ion quantum processor [56.089799129458875]
  We show that a quantum processor can correctly solve the basic classification task considered. With the increase of the capabilities quantum processors, they can become a useful tool for machine learning.
  arXiv  Detail & Related papers  (2024-06-17T18:20:51Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Counting collisions in random circuit sampling for benchmarking quantum computers [0.7252027234425332]
  We show that counting the number of collisions when measuring a random quantum circuit provides a practical benchmark for the quality of a quantum computer. We show that this quantity, if properly normalized, can be used as a "collision anomaly" benchmark or as a "collision volume" test.
  arXiv  Detail & Related papers  (2023-12-07T11:12:30Z)
• 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)
• Faster Born probability estimation via gate merging and frame optimisation [3.9198548406564604]
  Outcome probabilities of any quantum circuit can be estimated using Monte Carlo sampling. We propose two classical sub-routines: circuit gate optimisation and frame optimisation. We numerically demonstrate that our methods provide improved scaling in the negativity overhead for all tested cases of random circuits.
  arXiv  Detail & Related papers  (2022-02-24T14:18:34Z)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)
• Quantum computational advantage attested by nonlocal games with the cyclic cluster state [0.0]
  We propose a set of Bell-type nonlocal games that can be used to prove an unconditional quantum advantage in an objective and hardware-agnostic manner. Our games offer a practical and scalable set of quantitative benchmarks for quantum computers in the pre-fault-tolerant regime as the number of qubits available increases.
  arXiv  Detail & Related papers  (2021-10-08T17:43:38Z)
• Differentiable Model Compression via Pseudo Quantization Noise [99.89011673907814]
  We propose to add independent pseudo quantization noise to model parameters during training to approximate the effect of a quantization operator. We experimentally verify that our method outperforms state-of-the-art quantization techniques on several benchmarks and architectures for image classification, language modeling, and audio source separation.
  arXiv  Detail & Related papers  (2021-04-20T14:14:03Z)
• Simulating the Sycamore quantum supremacy circuits [7.15956388718641]
  We propose a general tensor network method for simulating quantum circuits. As an application, we study the sampling problem of Google's Sycamore circuits.
  arXiv  Detail & Related papers  (2021-03-04T14:55:15Z)
• Laser-annealing Josephson junctions for yielding scaled-up superconducting quantum processors [4.522152274205814]
  We show a nearly ten-fold improvement in the precision of setting qubit frequencies. We identify the types of 'frequency collisions' that will impair a transmon qubit and cross-resonance gate architecture. We find that without post-fabrication tuning, the probability of finding a workable lattice quickly approaches 0.
  arXiv  Detail & Related papers  (2020-09-02T02:03:43Z)
• Boundaries of quantum supremacy via random circuit sampling [69.16452769334367]
  Google's recent quantum supremacy experiment heralded a transition point where quantum computing performed a computational task, random circuit sampling. We examine the constraints of the observed quantum runtime advantage in a larger number of qubits and gates.
  arXiv  Detail & Related papers  (2020-05-05T20:11:53Z)

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.