Simulating Sycamore quantum circuits classically using tensor network algorithm.

Overview

Simulating the Sycamore quantum supremacy circuit

This repo contains data we have obtained in simulating the Sycamore quantum supremacy circuits with $n=53$ qubits, $m=20$ cycles using the tensor network method proposed in arXiv:2103.03074.

We plan to release the code soon.

Explanation of data

  1. data/circuit_n53_m20_s0_e0_pABCDCDAB.py is the circuit file which has been download from the Google's data repository for the Sycamore circuits.
  2. data/bipartition_n53_m20_s0_ABCD_s24_simplify_.txt is the initial bipartition of the simplified tensor network corresponding to Sycamore circuit with 53 qubits, 20 cycles, seed 0, elide 0 and ABCDCDAB sequence. There are two lines in the file, the first line indicates the tail partition which includes 21 open qubits, while the second line includes the head partition with 32 closed qubits. The simplification of the tensor network is done by sequentially contracting tensors with 2 or less dimensions.
  3. data/n53_m20_s0_ABCD_s24_simplify_gpulimit_30_edges.txt contains the 23 slicing edges which splits the overall contraction task into $2^{23}$ subtasks, each of which has space complexity $2^{30}$ hence can be contracted using fit into 32G memory.
  4. data/n53_m20_s0_ABCD_s24_simplify_gpulimit_30_ordernew.txt includes the contraction order. For each edge in the contraction order, say $i, j$, the $i$th and $j$th tensor in the head partition will be contracted by tracing out the shared indices. Then the resulting tensor will be put back into the $i$th position.
  5. vector.pt contains the cut tensor of of the head partition whose overall dimension is $2^{23}$ and the annotations of corresponding dimensions. The file is saved using pytorch, one can use torch.load to load the data.
  6. The obtained $2^{21}$ samples for the Sycamore circuits with $n=53$ qubits and $m=20$ cycles and their probabilities and amplitudes are listed in probs.txt file. Notice that the configuration we assigned to all closed qubits are fixed to $\underbrace{0,0,0,\cdots,0}_{32}$, and the open qubit ids are 11, 12, 13, 19, 20, 21, 22, 23, 28, 29, 30, 31, 32, 37, 38, 39, 40, 41, 44, 45, 46.

Notice

We noticed that in our paper arXiv:2103.03074 we have a misprint in the first row of Tab.III, where the amplitude should be |amplitude|. Neverthless, we put the refined table below.

image-20210308101302534

The $2^{21}$ bitstrings with amplitudes and probabilities can be download here.

Owner
Feng Pan
PHD candidate on theoretical physics. Personal interest in learning theory by statistical physics approaches.
Feng Pan
An example of semantic segmentation using tensorflow in eager execution.

Semantic segmentation using Tensorflow eager execution Requirement Python 2.7+ Tensorflow-gpu OpenCv H5py Scikit-learn Numpy Imgaug Train with eager e

Iñigo Alonso Ruiz 25 Sep 29, 2022
Mixed Neural Likelihood Estimation for models of decision-making

Mixed neural likelihood estimation for models of decision-making Mixed neural likelihood estimation (MNLE) enables Bayesian parameter inference for mo

mackelab 9 Dec 22, 2022
Implementation of ICCV21 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers

Implementation of ICCV 2021 paper: PnP-DETR: Towards Efficient Visual Analysis with Transformers arxiv This repository is based on detr Recently, DETR

twang 113 Dec 27, 2022
Learning Optical Flow from a Few Matches (CVPR 2021)

Learning Optical Flow from a Few Matches This repository contains the source code for our paper: Learning Optical Flow from a Few Matches CVPR 2021 Sh

Shihao Jiang (Zac) 159 Dec 16, 2022
Self-training with Weak Supervision (NAACL 2021)

This repo holds the code for our weak supervision framework, ASTRA, described in our NAACL 2021 paper: "Self-Training with Weak Supervision"

Microsoft 148 Nov 20, 2022
Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

Fully Convolutional Networks for Semantic Segmentation This is the reference implementation of the models and code for the fully convolutional network

Evan Shelhamer 3.2k Jan 08, 2023
SMIS - Semantically Multi-modal Image Synthesis(CVPR 2020)

Semantically Multi-modal Image Synthesis Project page / Paper / Demo Semantically Multi-modal Image Synthesis(CVPR2020). Zhen Zhu, Zhiliang Xu, Anshen

316 Dec 01, 2022
Intrinsic Image Harmonization

Intrinsic Image Harmonization [Paper] Zonghui Guo, Haiyong Zheng, Yufeng Jiang, Zhaorui Gu, Bing Zheng Here we provide PyTorch implementation and the

VISION @ OUC 44 Dec 21, 2022
Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks

Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks Requirements python 0.10+ rdkit 2020.03.3.0 biopython 1.78 openbabel 2.4

Neeraj Kumar 3 Nov 23, 2022
Demos of essentia classifiers hosted on replicate.ai

essentia-replicate-demos Demos of Essentia models hosted on replicate.ai's MTG site. The models Check our site for a complete list of the models avail

Music Technology Group - Universitat Pompeu Fabra 12 Nov 14, 2022
Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".

nvdiffrec Joint optimization of topology, materials and lighting from multi-view image observations as described in the paper Extracting Triangular 3D

NVIDIA Research Projects 1.4k Jan 01, 2023
A library for optimization on Riemannian manifolds

TensorFlow RiemOpt A library for manifold-constrained optimization in TensorFlow. Installation To install the latest development version from GitHub:

Oleg Smirnov 83 Dec 27, 2022
A little software to generate and save Julia or Mandelbrot's Fractals.

Julia-Mandelbrot-s-Fractals A little software to generate and save Julia or Mandelbrot's Fractals. Dependencies : Python 3.7 or more. (Also possible t

Olivier 0 Jul 09, 2022
CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation

CSKG: The CommonSense Knowledge Graph CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation: AT

USC ISI I2 85 Dec 12, 2022
Official Repo of my work for SREC Nandyal Machine Learning Bootcamp

About the Bootcamp A 3-day Machine Learning Bootcamp organised by Department of Electronics and Communication Engineering, Santhiram Engineering Colle

MS 1 Nov 29, 2021
🏖 Keras Implementation of Painting outside the box

Keras implementation of Image OutPainting This is an implementation of Painting Outside the Box: Image Outpainting paper from Standford University. So

Bendang 1.1k Dec 10, 2022
Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

RealTime Sign Language Detection using Action Recognition Approach Real-Time Sign Language is commonly predicted using models whose architecture consi

Rishikesh S 15 Aug 20, 2022
[NeurIPS'21 Spotlight] PyTorch code for our paper "Aligned Structured Sparsity Learning for Efficient Image Super-Resolution"

ASSL This repository is for a new network pruning method (Aligned Structured Sparsity Learning, ASSL) for efficient single image super-resolution (SR)

Huan Wang 47 Nov 28, 2022
Bunch of different tools which helps visualizing and annotating images for semantic/instance segmentation tasks

Data Framework for Semantic/Instance Segmentation Bunch of different tools which helps visualizing, transforming and annotating images for semantic/in

Bruno Fernandes Carvalho 5 Dec 21, 2022
Towards uncontrained hand-object reconstruction from RGB videos

Towards uncontrained hand-object reconstruction from RGB videos Yana Hasson, Gül Varol, Ivan Laptev and Cordelia Schmid Project page Paper Table of Co

Yana 69 Dec 27, 2022