scholarly journals Three-point functions in ABJM and Bethe Ansatz

2022 ◽  
Vol 2022 (1) ◽  
Author(s):  
Peihe Yang ◽  
Yunfeng Jiang ◽  
Shota Komatsu ◽  
Jun-Bao Wu
Keyword(s):  
Bethe Ansatz ◽  
Spin Chain ◽  
Scalar Fields ◽  
Closed Form Expression ◽  
Matrix Product ◽  
Product State ◽  
Abjm Theory ◽  
Matrix Product State ◽  
Structure Constants ◽  
Single Trace

Abstract We develop an integrability-based framework to compute structure constants of two sub-determinant operators and a single-trace non-BPS operator in ABJM theory in the planar limit. In this first paper, we study them at weak coupling using a relation to an integrable spin chain. We first develop a nested Bethe ansatz for an alternating SU(4) spin chain that describes single-trace operators made out of scalar fields. We then apply it to the computation of the structure constants and show that they are given by overlaps between a Bethe eigenstate and a matrix product state. We conjecture that the determinant operator corresponds to an integrable matrix product state and present a closed-form expression for the overlap, which resembles the so-called Gaudin determinant. We also provide evidence for the integrability of general sub-determinant operators. The techniques developed in this paper can be applied to other quantities in ABJM theory including three-point functions of single-trace operators.

scholarly journals Geometric entanglement from matrix product state representations

2011 ◽  
Vol 13 (9) ◽  
pp. 093041 ◽  
Author(s):  
Bing-Quan Hu ◽  
Xi-Jing Liu ◽  
Jin-Hua Liu ◽  
Huan-Qiang Zhou
Keyword(s):  
Matrix Product ◽  
Product State ◽  
Matrix Product State

scholarly journals Optimising Matrix Product State Simulations of Shor's Algorithm

Quantum ◽  
2019 ◽  
Vol 3 ◽  
pp. 116 ◽  
Author(s):  
Aidan Dang ◽  
Charles D. Hill ◽  
Lloyd C. L. Hollenberg
Keyword(s):  
Dimensional Structure ◽  
Matrix Product ◽  
Product State ◽  
One Dimensional ◽  
Matrix Product State ◽  
Shor's Algorithm ◽  
The One ◽  
High Level ◽  
Space Requirements ◽  
Factoring Algorithm

We detail techniques to optimise high-level classical simulations of Shor's quantum factoring algorithm. Chief among these is to examine the entangling properties of the circuit and to effectively map it across the one-dimensional structure of a matrix product state. Compared to previous approaches whose space requirements depend on r, the solution to the underlying order-finding problem of Shor's algorithm, our approach depends on its factors. We performed a matrix product state simulation of a 60-qubit instance of Shor's algorithm that would otherwise be infeasible to complete without an optimised entanglement mapping.

scholarly journals Matrix Product State–Based Quantum Classifier

2019 ◽  
Vol 31 (7) ◽  
pp. 1499-1517 ◽  
Author(s):  
Amandeep Singh Bhatia ◽  
Mandeep Kaur Saggi ◽  
Ajay Kumar ◽  
Sushma Jain
Keyword(s):  
Performance Metrics ◽  
Quantum Computer ◽  
Binary Classification ◽  
Learning Ability ◽  
Matrix Product ◽  
Product State ◽  
Data Set ◽  
Matrix Product State ◽  
Tensor Network ◽  
Network States

Interest in quantum computing has increased significantly. Tensor network theory has become increasingly popular and widely used to simulate strongly entangled correlated systems. Matrix product state (MPS) is a well-designed class of tensor network states that plays an important role in processing quantum information. In this letter, we show that MPS, as a one-dimensional array of tensors, can be used to classify classical and quantum data. We have performed binary classification of the classical machine learning data set Iris encoded in a quantum state. We have also investigated its performance by considering different parameters on the ibmqx4 quantum computer and proved that MPS circuits can be used to attain better accuracy. Furthermore the learning ability of an MPS quantum classifier is tested to classify evapotranspiration (ET[Formula: see text]) for the Patiala meteorological station located in northern Punjab (India), using three years of a historical data set (Agri). We have used different performance metrics of classification to measure its capability. Finally, the results are plotted and the degree of correspondence among values of each sample is shown.

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