Scalable tensor-network error mitigation for near-term quantum computing

• URL: http://arxiv.org/abs/2307.11740v2
• Date: Mon, 24 Jul 2023 17:10:49 GMT
• Title: Scalable tensor-network error mitigation for near-term quantum computing
• Authors: Sergei Filippov, Matea Leahy, Matteo A. C. Rossi, Guillermo Garc\'ia-P\'erez
• Abstract summary: We introduce the tensor-network error mitigation (TEM) algorithm, which acts in post-processing to correct the noise-induced errors in estimations of physical observables. TEM does not require additional quantum operations other than the implementation of informationally complete POVMs. We test TEM extensively in numerical simulations in different regimes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Until fault-tolerance becomes implementable at scale, quantum computing will heavily rely on noise mitigation techniques. While methods such as zero noise extrapolation with probabilistic error amplification (ZNE-PEA) and probabilistic error cancellation (PEC) have been successfully tested on hardware recently, their scalability to larger circuits may be limited. Here, we introduce the tensor-network error mitigation (TEM) algorithm, which acts in post-processing to correct the noise-induced errors in estimations of physical observables. The method consists of the construction of a tensor network representing the inverse of the global noise channel affecting the state of the quantum processor, and the consequent application of the map to informationally complete measurement outcomes obtained from the noisy state. TEM does therefore not require additional quantum operations other than the implementation of informationally complete POVMs, which can be achieved through randomised local measurements. The key advantage of TEM is that the measurement overhead is quadratically smaller than in PEC. We test TEM extensively in numerical simulations in different regimes. We find that TEM can be applied to circuits of twice the depth compared to what is achievable with PEC under realistic conditions with sparse Pauli-Lindblad noise, such as those in [E. van den Berg et al., Nat. Phys. (2023)]. By using Clifford circuits, we explore the capabilities of the method in wider and deeper circuits with lower noise levels. We find that in the case of 100 qubits and depth 100, both PEC and ZNE fail to produce accurate results by using $\sim 10^5$ shots, while TEM succeeds.

Related papers

• Application of zero-noise extrapolation-based quantum error mitigation to a silicon spin qubit [0.08603957004874943]
  We report the implementation of a zero-noise extrapolation-based error mitigation technique on a silicon spin qubit platform. This technique has been successfully demonstrated for other platforms such as superconducting qubits, trapped-ion qubits, and photonic processors.
  arXiv  Detail & Related papers  (2024-10-14T09:51:21Z)
• QuTracer: Mitigating Quantum Gate and Measurement Errors by Tracing Subsets of Qubits [8.54896613102673]
  Quantum error mitigation plays a crucial role in the current noisy-intermediate-scale-quantum (NISQ) era. We propose QuTracer, a framework designed to mitigate both gate and measurement errors in subsets of qubits.
  arXiv  Detail & Related papers  (2024-04-30T17:06:04Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Error-aware Quantization through Noise Tempering [43.049102196902844]
  Quantization-aware training (QAT) optimize model parameters with respect to the end task while simulating quantization error. In this work, we incorporate exponentially decaying quantization-error-aware noise together with a learnable scale of task loss gradient to approximate the effect of a quantization operator. Our method obtains state-of-the-art top-1 classification accuracy for uniform (non mixed-precision) quantization, out-performing previous methods by 0.5-1.2% absolute.
  arXiv  Detail & Related papers  (2022-12-11T20:37:50Z)
• Approximation Algorithm for Noisy Quantum Circuit Simulation [3.55689240295244]
  This paper introduces a novel approximation algorithm for simulating noisy quantum circuits. Our method offers a speedup over the commonly-used approximation (sampling) algorithm -- quantum trajectories method.
  arXiv  Detail & Related papers  (2022-11-30T14:20:22Z)
• QuEst: Graph Transformer for Quantum Circuit Reliability Estimation [32.89844497610906]
  Python library called TorchQuantum can construct, simulate, and train PQC for machine learning tasks. We propose to leverage a graph transformer model to predict noise impact on circuit fidelity. Compared with circuit simulators, the predictor has over 200X speedup for estimating the fidelity.
  arXiv  Detail & Related papers  (2022-10-30T02:35:31Z)
• Automated quantum error mitigation based on probabilistic error reduction [0.9236074230806579]
  Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. We present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits.
  arXiv  Detail & Related papers  (2022-10-16T19:09:41Z)
• Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis [42.275148861039895]
  Variational quantum algorithms (VQAs) offer the most promising path to obtaining quantum advantages via noisy processors. gate noise due to imperfections and decoherence affects the gradient estimates by introducing a bias. Quantum error mitigation (QEM) techniques can reduce the estimation bias without requiring any increase in the number of qubits. QEM can reduce the number of required iterations, but only as long as the quantum noise level is sufficiently small.
  arXiv  Detail & Related papers  (2022-09-23T10:48:04Z)
• Measuring NISQ Gate-Based Qubit Stability Using a 1+1 Field Theory and Cycle Benchmarking [50.8020641352841]
  We study coherent errors on a quantum hardware platform using a transverse field Ising model Hamiltonian as a sample user application. We identify inter-day and intra-day qubit calibration drift and the impacts of quantum circuit placement on groups of qubits in different physical locations on the processor. This paper also discusses how these measurements can provide a better understanding of these types of errors and how they may improve efforts to validate the accuracy of quantum computations.
  arXiv  Detail & Related papers  (2022-01-08T23:12:55Z)
• Quantum error mitigation via matrix product operators [27.426057220671336]
  Quantum error mitigation (QEM) can suppress errors in measurement results via repeated experiments and post decomposition of data. MPO representation increases the accuracy of modeling noise without consuming more experimental resources. Our method is hopeful of being applied to circuits in higher dimensions with more qubits and deeper depth.
  arXiv  Detail & Related papers  (2022-01-03T16:57:43Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

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.