Thresholds for the distributed surface code in the presence of memory decoherence
- URL: http://arxiv.org/abs/2401.10770v2
- Date: Sat, 18 May 2024 16:04:12 GMT
- Title: Thresholds for the distributed surface code in the presence of memory decoherence
- Authors: Sébastian de Bone, Paul Möller, Conor E. Bradley, Tim H. Taminiau, David Elkouss,
- Abstract summary: We present a framework for numerical simulations of a memory channel using the distributed toric surface code.
We quantitatively investigate the effect of memory decoherence and evaluate the advantage of GHZ creation protocols tailored to the level of decoherence.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the search for scalable, fault-tolerant quantum computing, distributed quantum computers are promising candidates. These systems can be realized in large-scale quantum networks or condensed onto a single chip with closely situated nodes. We present a framework for numerical simulations of a memory channel using the distributed toric surface code, where each data qubit of the code is part of a separate node, and the error-detection performance depends on the quality of four-qubit Greenberger-Horne-Zeilinger (GHZ) states generated between the nodes. We quantitatively investigate the effect of memory decoherence and evaluate the advantage of GHZ creation protocols tailored to the level of decoherence. We do this by applying our framework for the particular case of color centers in diamond, employing models developed from experimental characterization of nitrogen-vacancy centers. For diamond color centers, coherence times during entanglement generation are orders of magnitude lower than coherence times of idling qubits. These coherence times represent a limiting factor for applications, but previous surface code simulations did not treat them as such. Introducing limiting coherence times as a prominent noise factor makes it imperative to integrate realistic operation times into simulations and incorporate strategies for operation scheduling. Our model predicts error probability thresholds for gate and measurement reduced by at least a factor of three compared to prior work with more idealized noise models. We also find a threshold of $4\cdot10^2$ in the ratio between the entanglement generation and the decoherence rates, setting a benchmark for experimental progress.
Related papers
- Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory [87.62730694973696]
STEEL is the first provably sample-efficient algorithm for learning the controllable dynamics of an Exogenous Block Markov Decision Process from a single trajectory.
We prove that STEEL is correct and sample-efficient, and demonstrate STEEL on two toy problems.
arXiv Detail & Related papers (2024-10-03T21:57:21Z) - Non-local resources for error correction in quantum LDPC codes [0.0]
Surface code suffers from a low encoding rate, requiring a vast number of physical qubits for large-scale quantum computation.
hypergraph product codes present a promising alternative, as both their encoding rate and distance scale with block size.
Recent advancements have shown how to deterministically perform high-fidelity cavity enabled non-local many-body gates.
arXiv Detail & Related papers (2024-09-09T17:28:41Z) - Fast Point Cloud Geometry Compression with Context-based Residual Coding and INR-based Refinement [19.575833741231953]
We use the KNN method to determine the neighborhoods of raw surface points.
A conditional probability model is adaptive to local geometry, leading to significant rate reduction.
We incorporate an implicit neural representation into the refinement layer, allowing the decoder to sample points on the underlying surface at arbitrary densities.
arXiv Detail & Related papers (2024-08-06T05:24:06Z) - Compressed gate characterization for quantum devices with
time-correlated noise [0.0]
We present a general framework for quantum process tomography (QPT) in the presence of time-correlated noise.
As an application of our method, we perform a comparative theoretical and experimental analysis of silicon spin qubits.
We find good agreement between our theoretically predicted process fidelities and two qubit interleaved randomized benchmarking fidelities of 99.8% measured in recent experiments on silicon spin qubits.
arXiv Detail & Related papers (2023-07-26T18:05:49Z) - Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized
Language Model Finetuning Using Shared Randomness [86.61582747039053]
Language model training in distributed settings is limited by the communication cost of exchanges.
We extend recent work using shared randomness to perform distributed fine-tuning with low bandwidth.
arXiv Detail & Related papers (2023-06-16T17:59:51Z) - Importance sampling for stochastic quantum simulations [68.8204255655161]
We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients.
We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage.
Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
arXiv Detail & Related papers (2022-12-12T15:06:32Z) - Performance of surface codes in realistic quantum hardware [0.24466725954625884]
Surface codes are generally studied based on the assumption that each of the qubits that make up the surface code lattice suffers noise that is independent and identically distributed (i.i.d.)
We introduce independent non-identically distributed (i.ni.d.) noise model, a decoherence model that accounts for the non-uniform behaviour of the docoherence parameters of qubits.
We consider and describe two methods which enhance the performance of planar codes under i.ni.d. noise.
arXiv Detail & Related papers (2022-03-29T15:57:23Z) - Scaling Structured Inference with Randomization [64.18063627155128]
We propose a family of dynamic programming (RDP) randomized for scaling structured models to tens of thousands of latent states.
Our method is widely applicable to classical DP-based inference.
It is also compatible with automatic differentiation so can be integrated with neural networks seamlessly.
arXiv Detail & Related papers (2021-12-07T11:26:41Z) - SignalNet: A Low Resolution Sinusoid Decomposition and Estimation
Network [79.04274563889548]
We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples.
We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions.
In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
arXiv Detail & Related papers (2021-06-10T04:21:20Z) - Deterministic generation of multidimensional photonic cluster states
using time-delay feedback [9.83302372715731]
Cluster states are useful in many quantum information processing applications.
This work proposes a protocol to deterministically generate multidimensional photonic cluster states using a single atom-cavity system and time-delay feedback.
arXiv Detail & Related papers (2021-01-19T18:36:51Z) - DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic
Convolution [136.7261709896713]
We propose a data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances.
The proposed method achieves promising results on both ScanetNetV2 and S3DIS.
It also improves inference speed by more than 25% over the current state-of-the-art.
arXiv Detail & Related papers (2020-11-26T14:56:57Z)
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.