Quantum Machine Learning Interviews
A glimpse into the knowledge prerequisites for quantum computing internships, lab positions, or careers
 UPDATED
This section is clearly an outlier compared to the other entries in this series. The math section is obviously a prerequisite for any kind of learning. It’s important to know how ML works outside of just neural networks. There are tons of topics to cover in deep learning. And obviously, you need to know how to build and scale a deep learning system outside of a Jupyter Notebook.
But quantum machine learning? That came out of nowhere! You may be asking “Matt, is this just another clickbait article with both quantum and ML in the title”?
To which I respond NO (well, not anymore at least). Yes, quantum computing is nearing the peak of the hype cycle at the moment, and by extension so is quantum machine learning. It is clearly not ready for widespread realworld practical use yet, but there is still a lot of value in putting together a list of the fundamentals. For one, there are plenty of marketing people raving about quantum computers with little understanding beyond $\text{quantum} = \text{MAGIC}$. Many genuine research labs and companies in the space need to sift through crowds of professional BSartists to find people who actually know what they’re talking about. As these organizations standardize their practices, many talented undergrads and grad students (and eventually, even just talented senior engineers who want to switch into a new field) will need to make sure they’re up to speed on all the important topics.
With that in mind, I assembled this guide for use by both organizations and individuals. It’s a combination of pieces of information from reading material, papers, as well as questions friends of mine have received while interviewing for places like Strangeworks, Zapata Computing, Coldquanta, QC Ware, IBM, Rigetti Computing DWave Systems, Bleximo, 1QBit, Xanadu, Intel, Isara, Google, Microsoft, Atom Computing, ProteinQure. If that last name is any indication, quantum computing and quantum machine learning is being thrown at drug discovery a lot.
If any of this does reach practical usability status, there will likely be a flood of researchers scrambling to incorporate these techniques into their research (and likely long before any of them are intellectually prepared to do something with it). Deep learning progressed slowly as a field for years and decades with incremental progress, but around 2011 to 2012 many of these techniques became fast enough to actually use an the “deep learning revolution” exploded forth. As such, this is a list of important fundamentals and concepts for my own use as well, should I ever need it (Update April 08, 2020: Looks like I might need it sooner rather than later).
If you have any suggestions or feedback, please let me know. This resource will likely expand a lot in the coming months.
A curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language).
Table of Contents

 Atom Structure
 Photon wave
 Electron Fluctuation or spin
 States
 SuperPosition
 SuperPosition specific for machine learning(Quantum Walks)
 Classical Bit
 Quantum Bit or Qubit or Qbit
 Basic Gates in Quantum Computing
 Quantum Diode
 Quantum Transistor
 Quantum Processor
 Quantum Registery QRAM
 Quantum Entanglement

 Complex Numbers
 Tensors
 Tensors Network
 Oracle
 Hadamard transform
 Hilbert Space
 eigenvalues and eigenvectors
 Schr¨odinger Operators
 Quantum lambda calculus
 Quantum Amplitute Phase
 Qubits Encode and Decode
 convert classical bit to qubit
 Quantum Dirac and Kets
 Quantum Complexity
 Arbitrary State Generation

Quantum Machine Learning Algorithms
 Quantum KNearest Neighbour
 Quantum KMeans
 Quantum Fuzzy CMeans
 Quantum Support Vector Machine
 Quantum Genetic Algorithm
 Quantum Hidden Morkov Models
 Quantum state classification with Bayesian methods
 Quantum Ant Colony Optimization
 Quantum Cellular Automata
 Quantum Classification using Principle Component Analysis
 Quantum Inspired Evolutionary Algorithm
 Quantum Approximate Optimization Algorithm
 Quantum Elephant Herding Optimization
 Quantumbehaved Particle Swarm Optimization
 Quantum Annealing ExpectationMaximization

 Quantum perceptrons
 Qurons
 Quantum Auto Encoder
 Quantum Annealing
 Photonic Implementation of Quantum Neural Network
 Quantum Feed Forward Neural Network
 Quantum Boltzman Neural Network
 Quantum Neural Net Weight Storage
 Quantum Upside Down Neural Net
 Quantum Hamiltonian Neural Net
 QANN
 QPN
 SAL
 Quantum Hamiltonian Learning
 Compressed Quantum Hamiltonian Learning
Highlevel Overview
What is the motivation behind Quantum Machine Learning?
Machine Learning(ML) is just a term in recent days but the work effort start from 18th century.
What is Machine Learning ? , In Simple word the answer is making the computer or application to learn themselves . So its totally related with computing fields like computer science and IT ? ,The answer is not true . ML is a common platform which is mingled in all the aspects of the life from agriculture to mechanics . Computing is a key component to use ML easily and effectively . To be more clear ,Who is the mother of ML ?, As no option Mathematics is the mother of ML . The world tremendous invention complex numbers given birth to this field . Applying mathematics to the real life problem always gives a solution . From Neural Network to the complex DNA is running under some specific mathematical formulas and theorems.
As computing technology growing faster and faster mathematics entered into this field and makes the solution via computing to the real world . In the computing technology timeline once a certain achievements reached peoples interested to use advanced mathematical ideas such as complex numbers ,eigen etc and its the kick start for the ML field such as Artificial Neural Network ,DNA Computing etc.
Now the main question, why this field is getting boomed now a days ? , From the business perspective , 810 Years before during the kick start time for ML ,the big barrier is to merge mathematics into computing field . people knows well in computing has no idea on mathematics and research mathematician has no idea on what is computing . The education as well as the Job Opportunities is like that in that time . Even if a person tried to study both then the business value for making a product be not good.
Then the top product companies like Google ,IBM ,Microsoft decided to form a team with mathematician ,a physician and a computer science person to come up with various ideas in this field . Success of this team made some wonderful products and they started by providing cloud services using this product . Now we are in this stage.
So what’s next ? , As mathematics reached the level of time travel concepts but the computing is still running under classical mechanics . the companies understood, the computing field must have a change from classical to quantum, and they started working on the big Quantum computing field, and the market named this field as Quantum Information Science .The kick start is from Google and IBM with the Quantum Computing processor (DWave) for making Quantum Neural Network .The field of Quantum Computer Science and Quantum Information Science will do a big change in AI in the next 10 years. Waiting to see that… .(google, ibm).
What is Quantum Mechanics?
In a single line study of an electron moved out of the atom then its classical mechanic ,vibrates inside the atom its quantum mechanics
 WIKIPEDIA  Basic History and outline
 LIVESCIENCE.  A survey
 YOUTUBE  Simple Animation Video Explanining Great.
What is Quantum Computing?
A way of parallel execution of multiple processess in a same time using qubit ,It reduces the computation time and size of the processor probably in neuro size
 WIKIPEDIA  Basic History and outline
 WEBOPEDIA.  A survey
 YOUTUBE  Simple Animation Video Explanining Great.
What are some differences between Quantum Computing vs Classical Computing?

LINK  Basic outline
Quantum Computing
Atom Structure
one line : Electron Orbiting around the nucleous in an eliptical format
 YOUTUBE  A nice animation video about the basic atom structure
Photon Wave
one line : Light nornmally called as wave transmitted as photons as similar as atoms in solid particles
 YOUTUBE  A nice animation video about the basic photon 1
 YOUTUBE  A nice animation video about the basic photon 2
Electron Fluctuation or spin
one line : When a laser light collide with solid particles the electrons of the atom will get spin between the orbitary layers of the atom
 YOUTUBE  A nice animation video about the basic Electron Spin 1
 YOUTUBE  A nice animation video about the basic Electron Spin 2
 YOUTUBE  A nice animation video about the basic Electron Spin 3
States
one line : Put a point on the spinning electron ,if the point is in the top then state 1 and its in bottom state 0
 YOUTUBE  A nice animation video about the Quantum States
SuperPosition
two line : During the spin of the electron the point may be in the middle of upper and lower position, So an effective decision needs to take on the point location either 0 or 1 . Better option to analyse it along with other electrons using probability and is called superposition
 YOUTUBE  A nice animation video about the Quantum Superposition
SuperPosition specific for machine learning(Quantum Walks)
one line : As due to computational complexity ,quantum computing only consider superposition between limited electrons ,In case to merge more than one set quantum walk be the idea
 YOUTUBE  A nice video about the Quantum Walks
Classical Bits
one line : If electron moved from one one atom to other ,from ground state to excited state a bit value 1 is used else bit value 0 used
Qubit
one line : The superposition value of states of a set of electrons is Qubit
Basic Gates in Quantum Computing
one line : As like NOT, OR and AND , Basic Gates like NOT, Hadamard gate , SWAP, Phase shift etc can be made with quantum gates
 YOUTUBE  A nice video about the Quantum Gates
Quantum Diode
one line : Quantum Diodes using a different idea from normal diode, A bunch of laser photons trigger the electron to spin and the quantum magnetic flux will capture the information
 YOUTUBE  A nice video about the Quantum Diode
Quantum Transistors
one line : A transistor default have Source ,drain and gate ,Here source is photon wave ,drain is flux and gate is classical to quantum bits
Quantum Processor
one line : A nano integration circuit performing the quantum gates operation sorrounded by cooling units to reduce the tremendous amount of heat
 YOUTUBE  Well Explained
Quantum Registery QRAM
one line : Comapring the normal ram ,its ultrafast and very small in size ,the address location can be access using qubits superposition value ,for a very large memory set coherent superposition(address of address) be used
 PDF  very Well Explained
QuantumcomputingML Bridge
Complex Numbers
one line : Normally Waves Interference is in n dimensional structure , to find a polynomial equation n order curves ,better option is complex number
 YOUTUBE  Wonderful Series very super Explained
Tensors
one line : Vectors have a direction in 2D vector space ,If on a n dimensional vector space ,vectors direction can be specify with the tensor ,The best solution to find the superposition of a n vector electrons spin space is representing vectors as tensors and doing tensor calculus
Tensors Network
one line : As like connecting multiple vectors ,multple tensors form a network ,solving such a network reduce the complexity of processing qubits
 YOUTUBE  Tensors Network Some ideas specifically for quantum algorithms
Quantum Machine Learning Algorithms
Quantum KNearest Neighbour
info : Here the centroid(euclidean distance) can be detected using the swap gates test between two states of the qubit , As KNN is regerssive loss can be tally using the average
 PDF1 from Microsoft  Theory Explanation
 PDF2  A Good Material to understand the basics
 Matlab  Yet to come soon
 Python  Yet to come soon
Quantum KMeans
info : Two Approaches possible ,1. FFT and iFFT to make an oracle and calculate the means of superposition 2. Adiobtic Hamiltonian generation and solve the hamiltonian to determine the cluster
 PDF1  Applying Quantum Kmeans on Images in a nice way
 PDF2  Theory
 PDF3  Explaining well the Kmeans clustering using hamiltonian
 Matlab  Yet to come soon
 Python  Yet to come soon
Quantum Fuzzy CMeans
info : As similar to kmeans fcm also using the oracle dialect ,but instead of means,here oracle optimization followed by a rotation gate is giving a good result
Quantum Support Vector Machine
info : A little different from above as here kernel preparation is via classical and the whole training be in oracles and oracle will do the classification, As SVM is linear ,An optimal Error(Optimum of the Least Squares Dual Formulation) Based regression is needed to improve the performance
 PDF1  Nice Explanation but little hard to understand :)
 PDF2  Nice Application of QSVM
 Matlab  Yet to come soon
 Python  Yet to come soon
Quantum Genetic Algorithm
info : One of the best algorithm suited for Quantum Field ,Here the chromosomes act as qubit vectors ,the crossover part carrying by an evaluation and the mutation part carrying by the rotation of gates
 PDF1  Very Beautiful Article , well explained and superp
 PDF2  A big theory :)
 PDF3  Super Comparison
 Matlab  Simulation
 Python1  Simulation
 Python2  Yet to come
Quantum Hidden Morkov Models
info : As HMM is already state based ,Here the quantum states acts as normal for the markov chain and the shift between states is using quantum operation based on probability distribution
 PDF1  Nice idea and explanation
 PDF2  Nice but a different concept little
 Matlab  Yet to come
 Python1  Yet to come
 Python2  Yet to come
Quantum state classification with Bayesian methods
info : Quantum Bayesian Network having the same states concept using quantum states,But here the states classification to make the training data as reusable is based on the density of the states(Interference)
 PDF1  Good Theory
 PDF2  Good Explanation
 Matlab  Yet to come
 Python1  Yet to come
 Python2  Yet to come
Quantum Ant Colony Optimization
info : A good algorithm to process multi dimensional equations, ACO is best suited for Sales man issue , QACO is best suited for Sales man in three or more dimension, Here the quantum rotation circuit is doing the peromene update and qubits based colony communicating all around the colony in complex space
 PDF1  Good Concept
 PDF2  Good Application
 Matlab  Yet to come
 Python1  Yet to come
 Python2  Yet to come
Quantum Cellular Automata
info : One of the very complex algorithm with various types specifically used for polynomial equations and to design the optimistic gates for a problem, Here the lattice is formed using the quatum states and time calculation is based on the change of the state between two qubits ,Best suited for nano electronics
Quantum Neural Networks
one line : Its really one of the hardest topic , To understand easily ,Normal Neural Network is doing parallel procss ,QNN is doing parallel of parallel processess ,In theory combination of various activation functions is possible in QNN ,In Normal NN more than one activation function reduce the performance and increase the complexity
Quantum perceptrons
info : Perceptron(layer) is the basic unit in Neural Network ,The quantum version of perceptron must satisfy both linear and non linear problems , Quantum Concepts is combination of linear(calculus of superposition) and nonlinear(State approximation using probability) ,To make a perceptron in quantum world ,Transformation(activation function) of non linearity to certain limit is needed ,which is carrying by phase estimation algorithm
Quantum Statistical Data Analysis
one line: An under research concept ,It can be seen in multiple ways, one best way if you want to apply n derivative for a problem in current classical theory its difficult to compute as its serialization problem instead if you do parallelization of differentiation you must estimate via probability the value in all flows ,Quantum Probability Helps to achieve this ,as the loss calculation is very less . the other way comparatively booming is Quantum Bayesianism, its a solution to solve most of the uncertainity problem in statistics to combine time and space in highly advanced physical research
References and Resources
In the ML community, and especially in the quantum computing community, it’s important to give credit where credit is due, and not just slap our name on someone else’s hard work cough. Here is a list of the references and resources I used for various equations, concepts, explanations, examples, and inspiration for visualizations:
Cited as:
@article{mcateer2020qmli,
title = "Quantum Machine Learning Interviews",
author = "McAteer, Matthew",
journal = "matthewmcateer.me",
year = "2020",
url = "https://matthewmcateer.me/blog/mlresearchinterviewquantumml/"
}
If you notice mistakes and errors in this post, don’t hesitate to contact me at [contact at matthewmcateer dot me] and I would be very happy to correct them right away!
See you in the next post 😄