Improved autonomous error correction using variable dissipation in small logical qubit architectures

• URL: http://arxiv.org/abs/2006.12633v3
• Date: Fri, 23 Oct 2020 01:34:52 GMT
• Title: Improved autonomous error correction using variable dissipation in small logical qubit architectures
• Abstract summary: This work presents several approaches to finding preferential parameter configurations for small logical qubits. We first look at state stabilization of a single qubit using dissipation via coupling to a lossy object. We then translate this approach to more efficient state stabilization to an abstracted three-qubit flip code, and end by looking at the Very Small Logical Qubit (VSLQ)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coherence times for superconducting qubits have greatly improved over time. Moreover, small logical qubit architectures using engineered dissipation have shown great promise for further improvements in the coherence of a logical qubit manifold comprised of few physical qubits. Nevertheless, optimal working parameters for small logical qubits are generally not well understood. This work presents several approaches to finding preferential parameter configurations by looking at three different cases of increasing complexity. We begin by looking at state stabilization of a single qubit using dissipation via coupling to a lossy object. We look at the limiting factors in this approach to error correction, and how we address those by numerically optimizing the parametric coupling strength with the lossy object having an effective time-varying dissipation rate---we call this a pulse-reset cycle. We then translate this approach to more efficient state stabilization to an abstracted three-qubit flip code, and end by looking at the Very Small Logical Qubit (VSLQ). By using these techniques, we can further increase logical state lifetimes for different architectures. We show significant advantages in using a pulse-reset cycle over numerically optimized, fixed parameter spaces.

Related papers

• Transversal Gates and Magic State Distillation in an Optimally Synthesized Spin-Qubit Shuttling Bus [1.62107954358883]
  Fault-tolerant quantum computing requires reliable logical qubit storage and high-fidelity logical operations between error-corrected qubits.<n>We propose a multi-qubit spin-qubit shuttling bus architecture that addresses both requirements simultaneously.<n>Results establish a principled co-design framework that bridges the physical, error-correction, and logical computation layers of the quantum stack.
  arXiv  Detail & Related papers  (2026-09-02T14:17:47Z)
• The Gain-Engineered Transmon [68.89136228609398]
  We introduce and experimentally realize a noise-biased qubit in a standard transmon-readout resonator circuit.<n>Our results open a path towards using this system as a simple building-block for hardware-efficient quantum error detection and correction schemes.
  arXiv  Detail & Related papers  (2026-08-26T17:00:42Z)
• Not All Qubits are Utilized Equally [0.6015898117103067]
  We analyze average qubit utilization of a quantum hardware as a means to identify how various transpiler configurations change utilization patterns.<n>We present the preliminary results of this analysis using IBM's 27-qubit Falcon R4 architecture on the Qiskit platform.
  arXiv  Detail & Related papers  (2025-09-23T17:01:39Z)
• Demonstration of a Logical Architecture Uniting Motion and In-Place Entanglement: Shor's Algorithm, Constant-Depth CNOT Ladder, and Many-Hypercube Code [23.109092385238526]
  We propose and experimentally realize an alternative architecture which unites qubit motion and in-place entanglement via nearest-neighbor gates.<n>Our approach maintains all-to-all connectivity, while minimizing qubit motion overheads.
  arXiv  Detail & Related papers  (2025-09-16T16:55:42Z)
• Orthogonal Finetuning Made Scalable [87.49040247077389]
  Orthogonal finetuning (OFT) offers highly parameter-efficient adaptation while preventing catastrophic forgetting, but its high runtime and memory demands limit practical deployment.<n>We identify the core computational bottleneck in OFT as its weight-centric implementation, which relies on costly matrix-matrix multiplications with cubic complexity.<n>We propose OFTv2, an input-centric reformulation that instead uses matrix-vector multiplications (i.e., matrix-free computation), reducing the computational cost to quadratic.<n>These modifications allow OFTv2 to achieve up to 10x faster training and 3x lower GPU memory usage without compromising performance.
  arXiv  Detail & Related papers  (2025-06-24T17:59:49Z)
• Pushing the Limits of Low-Bit Optimizers: A Focus on EMA Dynamics [65.37942405146232]
  We present a novel type of overload that carries with extremely lightweight state elements, achieved through ultra-low-precision quantization. The proposed SOLO achieves substantial memory savings (approximately 45 GB when training a 7B model) with minimal accuracy loss.
  arXiv  Detail & Related papers  (2025-05-01T06:47:45Z)
• ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization [58.84018707089315]
  We present a unified framework for rigorous comparisons across 1-bit, 1.58-bit, 2-bit, 3-bit, and 4-bit quantization settings. We show that ternary, 2-bit, and 3-bit quantization maintains comparable performance in the size-accuracy trade-off. Considering hardware constraints, 2-bit quantization offers promising potential for memory reduction and speedup.
  arXiv  Detail & Related papers  (2025-02-04T18:59:26Z)
• Symmetry-enhanced Counterdiabatic Quantum Algorithm for Qudits [2.8606554662852846]
  We propose a symmetry-enhanced digitized counterdiabatic quantum algorithm utilizing qudits instead of qubits. First, compression in the circuit depth is achieved by counterdiabatic protocols. Second, information about the problem is compressed by replacing qubits with qudits, allowing for a more efficient representation of the problem.
  arXiv  Detail & Related papers  (2024-10-09T09:30:25Z)
• A framework of partial error correction for intermediate-scale quantum computers [0.7046417074932257]
  We show that brick-layered circuits display on average slower concentration to the "useless" uniform distribution. We find that this advantage only comes when the number of error-corrected qubits passes a specified threshold.
  arXiv  Detail & Related papers  (2023-06-27T15:00:57Z)
• UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation [93.88170217725805]
  We propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features. Our evaluations on five benchmarks, Synapse, BTCV, ACDC, BRaTs, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy.
  arXiv  Detail & Related papers  (2022-12-08T18:59:57Z)
• Global Vision Transformer Pruning with Hessian-Aware Saliency [93.33895899995224]
  This work challenges the common design philosophy of the Vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a model stage. We derive a novel Hessian-based structural pruning criteria comparable across all layers and structures, with latency-aware regularization for direct latency reduction. Performing iterative pruning on the DeiT-Base model leads to a new architecture family called NViT (Novel ViT), with a novel parameter that utilizes parameters more efficiently.
  arXiv  Detail & Related papers  (2021-10-10T18:04:59Z)
• 8-bit Optimizers via Block-wise Quantization [57.25800395197516]
  Statefuls maintain statistics over time, e.g., the exponentially smoothed sum (SGD with momentum) or squared sum (Adam) of past values. This state can be used to accelerate optimization compared to plain gradient descent but uses memory that might otherwise be allocated to model parameters. In this paper, we develop first gradients that use 8-bit statistics while maintaining the performance levels of using 32-bit gradient states.
  arXiv  Detail & Related papers  (2021-10-06T15:43:20Z)
• Progress towards analytically optimal angles in quantum approximate optimisation [0.0]
  The Quantum Approximate optimisation algorithm is a $p$ layer, time-variable split operator method executed on a quantum processor. We prove that optimal parameters for $p=1$ layer reduce to one free variable and in the thermodynamic limit, we recover optimal angles. We moreover demonstrate that conditions for vanishing gradients of the overlap function share a similar form which leads to a linear relation between circuit parameters, independent on the number of qubits.
  arXiv  Detail & Related papers  (2021-09-23T18:00:13Z)
• Dynamic Convolution for 3D Point Cloud Instance Segmentation [146.7971476424351]
  We propose an approach to instance segmentation from 3D point clouds based on dynamic convolution. We gather homogeneous points that have identical semantic categories and close votes for the geometric centroids. The proposed approach is proposal-free, and instead exploits a convolution process that adapts to the spatial and semantic characteristics of each instance.
  arXiv  Detail & Related papers  (2021-07-18T09:05:16Z)
• Optimal qubit assignment and routing via integer programming [0.22940141855172028]
  We consider the problem of mapping a logical quantum circuit onto a given hardware with limited two-qubit connectivity. We model this problem as an integer linear program, using a network flow formulation with binary variables. We consider several cost functions: an approximation of the fidelity of the circuit, its total depth, and a measure of cross-talk.
  arXiv  Detail & Related papers  (2021-06-11T15:02:26Z)
• GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training [59.160154997555956]
  We present GradInit, an automated and architecture method for initializing neural networks. It is based on a simple agnostic; the variance of each network layer is adjusted so that a single step of SGD or Adam results in the smallest possible loss value. It also enables training the original Post-LN Transformer for machine translation without learning rate warmup.
  arXiv  Detail & Related papers  (2021-02-16T11:45:35Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Improving performance of logical qubits by parameter tuning and topology compensation [1.8352113484137624]
  We discuss tuning of the transformation for the cases of clique, biclique, and cubic lattice problems on the D-Wave 2000Q quantum computer. Inhomogeneities in effective coupling strength arising from minor-embedding are shown to be mitigated by an efficient reweighting of programmed couplings.
  arXiv  Detail & Related papers  (2020-06-08T20:03:34Z)

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.