The Interlink Between Quantum Theory and Machine Learning - Phdassistance
                     The Interlink  
Between 
Quantum  Theory 
and  Machine 
LAn Aecadeamic prrensentiantion gby
Dr. Nancy Agnes, Head, Technical Operations, 
Phdassistance  Group www.phdassistance.com
Email: [email protected]
TODAY'S DISCUSSION
Outline
Introduction
Quantum neural networks representation 
 Quantum-based machine learning
Future scope
INTRODUC
TION
M achine learning is a branch of computer science 
 that aims to create programs that can find useful  
knowledge and make assumptions about data.
It's at the heart of artificial intelligence (AI), and  
it's powering anything from facial recognition to  
natural language processing to automatic self-  
driving vehicles.
Dimensionality is the most challenging machine  
learning problem; in general, the number of  
training data sets needed for the machine to learn  
the desired information is exponential in  
dimension d.
Contd...
If a data set is located in a high-dimensional space, it becomes 
 computationally uncontrollable.
This level of sophistication is comparable to quantum  
mechanics, where an infinite number of data is needed to  
explain a quantum many-body state completely.
This article will explain the scientific interlink between quantum  
theory and m achine learning.
QUANTUM NEURAL 
NETWORKS  
REPRESENTATION
Artificial neural networks (ANNs) are models  
used in grouping, regression, compression,  
generative modelling, and statistical inference.
The alternation of linear operations with  
nonlinear transformations (e.g. sigmoid  
functions) in a theoretically hierarchical manner  
is their unifying feature.
In Quantum Machine Learning (QLM), NNs have  
been extensively studied
Contd...
The main research directions have been to speed up classical  
models' training and build networks with all constituent  
components, from single neurons to training algorithms, running  
on a quantum computer (a so-called quantum neural network).
Along with the rapid development of machine-learning  
algorithms for determining phases of matter, artificial neural  
networks have made significant progress in describing quantum  
states and solving important quantum many-body problems.
Completely defining an arbitrary many-body state in quantum  
mechanics necessitates an exponential number of data.
Contd...
Contd...
For computational simulations of quantum many-body
structures on a classical machine, the exponential difficulty  
presents a huge challenge—describing even a few qubits  
necessitates a massive amount of memory.
Moreover, only a small part of all Hilbert's Quantum space, such  
as the ground states of many-body Hamiltonians, can enter  
physical states of concern and be depicted with fewer details.
Compact models of quantum many-body states must be  
constructed while maintaining their basic physical properties to  
solve quantum many-body problems using classical computers.
Contd...
The tensor-network representation [2], in which each qubit is given a tensor,  
and these tensors together characterise the many-body quantum state, is a  
well-known explanation for such states.
Since the volume of data required is only polynomial instead of exponential, the 
 system's size is the most accurate description of the physical condition.
QUANTUM-
BASED
MMAostC qHuanItuNm Em a chine learning algorithms  
LEneAcessRitatNe faIulNt-tolGerant quantum computing,  which necessitates the aggregation of millions  
of qubits on a wide scale, which is currently  
unavailable.
Quantum machine learning (QML), however,
includes the first breakthroughalgorithms  
 applied on commercially viable noisy 
intermediate-scale quantum  
computers. (NISQ)
Contd...
Many interesting breakthroughs were made at the crossroads of  
quantum mechanics and machine learning.
Machine learning has been successfully used in many-body  
quantum mechanics to speed up calculations, simulate phases  
of matter, and find vibrational analysis for many-body quantum  
states, for example.
In quantum computing, machine learning has recently shown  
performance in quantum control and error correction.
Finding the system's ground state or the dynamics of the  
system's time evolution is typically the first step in solving  
quantum many-body problems.
Contd...
Carleo and Troyer proposed the RBM representation, used  
an RBM-based variational learning algorithm to do this.
They applied the technique to two prototypical quantum spin  
models: the Ising model in a transverse magnetic field and  
the antiferromagnetic Heisenberg model.
They discovered that it accurately captured the ground state  
and time evolution for both.
Contd...
Contd...
A series of new recent algorithms covering core  
areas of machine learning (supervised,  
unsupervised, and reinforcement learning), as well  
as other quantum-classical d ata and algorithms  
(QQ, QC, CQ models), is still to be done.
Scaling up the algorithms to the limits of actual  
hardware while doing an effective scaling study of  
performances and corresponding errors is important.
Finally, one should determine if a quantum speedup  
using quantum machine learning models operating  
on NISQ machines is technically and experimentally  
feasible.
FUTURE 
SCIn OtheP cEase of quantum algorithms for linear  
algebra, where robust guarantees are already  
possible, data access issues and limitations on  
the types of problems that can be solved can  
impede their success in practice.
Indeed, developments in quantum hardware  
growth in the coming future would be critical for  
empirically assessing the true potential of these  
techniques.
It's worth noting that the bulk of the Q ML 
literature  has come from within the quantum 
culture.
Contd...
Further advancements in the area are expected to arrive only after major contacts between 
 the two cultures.
The interdisciplinary field of mixing machine learning and quantum physics is increasingly  
expanding, with promising results.
The points raised above are just the tip of the iceberg.
CONTACT 
US
UNITED KINGDOM
+44-1143520021
INDIA
+91-4448137070
EMAIL
[email protected]