We're excited to announce our Weekly Thread dedicated to all your career, job, education, and basic questions related to our field. Whether you're exploring potential career paths, looking for job hunting tips, curious about educational opportunities, or have questions that you felt were too basic to ask elsewhere, this is the perfect place for you.

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• Careers: Discussions on career paths within the field, including insights into various roles, advice for career advancement, transitioning between different sectors or industries, and sharing personal career experiences. Tips on resume building, interview preparation, and how to effectively network can also be part of the conversation.
• Education: Information and questions about educational programs related to the field, including undergraduate and graduate degrees, certificates, online courses, and workshops. Advice on selecting the right program, application tips, and sharing experiences from different educational institutions.
• Textbook Recommendations: Requests and suggestions for textbooks and other learning resources covering specific topics within the field. This can include both foundational texts for beginners and advanced materials for those looking to deepen their expertise. Reviews or comparisons of textbooks can also be shared to help others make informed decisions.
• Basic Questions: A safe space for asking foundational questions about concepts, theories, or practices within the field that you might be hesitant to ask elsewhere. This is an opportunity for beginners to learn and for seasoned professionals to share their knowledge in an accessible way.

I've found arXiv email update format quite unreadable so I've built a simple webpage that presents last day submissions (https://arxiv.org/list/quant-ph/new) in a hopefully cleaner way. Below is the link:

https://arxiv.archeota.org/

It is free and I do not plan to put this behind any sort of paywall. In future I'd like to add more features to help researches and hobbyists increase signal to noise ratio when going through the papers. I'd be glad if you could drop me a DM (or simply a comment below) what features you'd like to see added.

when a photon is emitted and sent to Bob and Alice along with an intruder Eve, there is 3 people now. It is said (sauce: sciencephile the ai) that quantum entanglement can be established between 2 or 3 particles where the nature of one affects the others. So for 3 members there should be no problem in generating a quantum key. It doesn't make sense how the intruder cannot be a part of this network.

in a network of 3 people, a 4th member makes sense to be detected.

https://preview.redd.it/0u6usz9i1lzc1.png?width=1200&format=png&auto=webp&s=9c2a0c369e0344b7476f0497a01aa599439b69d8

I am quite worried that I’m having to keep looking at solutions and cannot seem to do many of the proofs myself?

For some background I have a masters in physics where I took some quantum computing and I got this book to refresh myself before starting a second masters later this year - but I’ve spent 3 years in finance and feel like I’m extremely rusty with my problem solving / memory of all the things I previously learned.

I used to learn by doing exercises in my head and going straight to solutions to see if the method was correct or not and this seemed to help me gain an understanding faster than if I tried to answer questions properly. 3 years laters and I’m trying to go through the exercises in this book the “right” way i.e. by trying to write down solutions then see if they are right but I’m not sure why I cannot seem to get some of the more simpler proofs correct? It’s discouraging me if I’m honest but my interest is not wavering.

As soon as I look at the solution it becomes obvious and I keep wondering “how come I didn’t think of it that way!”.

My main question here is should I revert back to going straight to solutions as I did during my previous degree or should I attempt questions (with the risk of me not covering things fast enough before my course starts). Also how do I stop beating myself up when I don’t get the answer after it seems so obvious!

Any advice?

I'm currently in my final year of BTech and my project involves creating a "Quantum-inspired neural network for Handwritten character recognition." So far, I've managed to develop a Convolutional Neural Network (CNN) for this purpose using Keras TensorFlow.

However, I'm struggling with the quantum aspect of the project. I haven't been able to find good YouTube tutorials or resources to guide me through this.

I'd really appreciate your help and guidance on how to get started with the quantum part of my project or any alternative approaches I could explore. Your support would mean a lot to me.

Hey all - I posted a quantum computing coding tutorial going line by line through the deutsch jozsa algorithm.

I hope someone finds it useful!

How to Code a Simple Quantum Algorithm https://youtu.be/WifWdbXjG00

Looking for clarification for the lesson 'Entanglement' in Brillinant.org's Quantum Computing course. Towards the end of the lesson, there is a chart that shows the number of product states vs entangled states for n-qubits and at that point I felt like I was completely lost. How do you calculate the number of entangled states vs product states?

https://preview.redd.it/tf94si553syc1.png?width=1122&format=png&auto=webp&s=9eee9aaa4b55c5ab7d406dfa1262b6175e4d9088

I heard companies including IBM and Google have released quantum computers for public access and research. As an aspiring cryptographer I intend to practice developing cryptanalysis tools on quantum machines to test the validity of post-quantum safe cryptosystems. What commercial quantum computers would you recommend I practice on?

It seems Python and C/C++ are the most supported? For those of you who had written computer programs that were executed on real Quantum computers before what would you say is the best programming language to get started with programming quantum computers?

How useful are courses of IBM quantum computing. Also has anyone tried IBM quantum computing challenge, can you share the experience

We're excited to announce our Weekly Thread dedicated to all your career, job, education, and basic questions related to our field. Whether you're exploring potential career paths, looking for job hunting tips, curious about educational opportunities, or have questions that you felt were too basic to ask elsewhere, this is the perfect place for you.

​

• Careers: Discussions on career paths within the field, including insights into various roles, advice for career advancement, transitioning between different sectors or industries, and sharing personal career experiences. Tips on resume building, interview preparation, and how to effectively network can also be part of the conversation.
• Education: Information and questions about educational programs related to the field, including undergraduate and graduate degrees, certificates, online courses, and workshops. Advice on selecting the right program, application tips, and sharing experiences from different educational institutions.
• Textbook Recommendations: Requests and suggestions for textbooks and other learning resources covering specific topics within the field. This can include both foundational texts for beginners and advanced materials for those looking to deepen their expertise. Reviews or comparisons of textbooks can also be shared to help others make informed decisions.
• Basic Questions: A safe space for asking foundational questions about concepts, theories, or practices within the field that you might be hesitant to ask elsewhere. This is an opportunity for beginners to learn and for seasoned professionals to share their knowledge in an accessible way.

I was trying to understand Grovers algo and I am unable to figure out why do we need to mark the target state multiple times ? I mean we have already marked it once can't we simply just process it though diffusion operator again?

Also how does 2|0><0| - I decrease the amplitude of all states while increasing the amplitude of the target?

I recently discovered a French startup, Alice&Bob, that is trying to build its own Quantum computer. I don't know a lot about the topic, but I heard that they made a major improvement in the field of error correction, and/or maybe in something else.

So I was wondering if these relatively small companies stand a chance against giants like Google, IBM, or Intel. Have any of them made significant progress? If not, is it the universities? Were there any notable advances in the last few years?

I have just started my journey into quantum computing and I tried to do a calculation on my own. The idea I just learnt was the implementing CNOT gates does not always indicate that the target bit will change, the control bit may change too. Basically, this circuit:

https://preview.redd.it/c7b1yetizdxc1.png?width=356&format=png&auto=webp&s=14c19e1767a62346d7216b92a65eff983b2fbeb5

To try and understand it better, I decided to do a calculation of my own but I am not sure if it is the right way.

https://preview.redd.it/y5bwr6s93exc1.png?width=292&format=png&auto=webp&s=c558b693e4ac0ab74dcde6ec3bc3d844af983294

If it is right, I am not sure how to go from the 4th line to 5th. I figured it because, in a way I knew what the output should be. But in another case, how would you about splitting it into its original counterparts?

Hello, I recently came into contact into bb84 and it's really interesting stuff, however I'm confused at how effective or better it is than normal encryption. I understand they use random polarization of photons to create keys, however if the polarization of the photons end up representing classica bits then wouldn't that be the same as a key formed using other conventional cryptography methods? Is there something I'm missing and not understanding about qkd?

I have been trying to implement this paper on identity block initialisation strategy for barren plateau mitigation but I don't really understand how one would apply it to a parameterised circuit with many parameters and specifically *how the initialisation of the parameters is obtained*.

If I have understood it correctly, the idea is to use these identity blocks to obtain a list of parameters that then could be used as a starting point in training the circuit. Now, say I have an ansatz consisting of 2 parameters (a circuit of 2 qubits consisting of a RX gate on the first qubit and a CRZ controlled by the first and acting on the second qubit). My only reasonable understanding of the implementation is that I define a single trainable parameter y_1, say for the RX gate, and choose a random value for the CRZ gate to then undo the circuit by applying the gates in reverse with the negative values of the CRZ parameter and some random value for the RX parameter? That would then be one block, the rest would be have parameters chosen randomly without any trainable parameters, and then being undone by their adjoint to create the second block. The circuit would then be trained using Qiskit's NeuralNetworkRegressor.fit() function and the value for the trainable parameter is then stored in a list before moving on to the next gate and repeating this procedure to store that parameter value in the list. The list would then be used as the initial_point in the NeuralNetworkRegressor.fit().

In summary: I don't understand how one can obtain a set of initial values for the parameters of the gates of the circuit using this strategy. Does it involve training the circuit on a single parameter as I explained above, or am I completely lost?

We're excited to announce our Weekly Thread dedicated to all your career, job, education, and basic questions related to our field. Whether you're exploring potential career paths, looking for job hunting tips, curious about educational opportunities, or have questions that you felt were too basic to ask elsewhere, this is the perfect place for you.

​

• Careers: Discussions on career paths within the field, including insights into various roles, advice for career advancement, transitioning between different sectors or industries, and sharing personal career experiences. Tips on resume building, interview preparation, and how to effectively network can also be part of the conversation.
• Education: Information and questions about educational programs related to the field, including undergraduate and graduate degrees, certificates, online courses, and workshops. Advice on selecting the right program, application tips, and sharing experiences from different educational institutions.
• Textbook Recommendations: Requests and suggestions for textbooks and other learning resources covering specific topics within the field. This can include both foundational texts for beginners and advanced materials for those looking to deepen their expertise. Reviews or comparisons of textbooks can also be shared to help others make informed decisions.
• Basic Questions: A safe space for asking foundational questions about concepts, theories, or practices within the field that you might be hesitant to ask elsewhere. This is an opportunity for beginners to learn and for seasoned professionals to share their knowledge in an accessible way.

​

Hello :D

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So I'm working in juptyer lab comp. chem and I need to visualize schrodinger's equation at a specific energy level but I can't fix a mistake in the equation. For some reason, even though the specific resonance changed along with the energy values, the formula isn't changing its input. The output has to be in cylindrical coordinates as well. This is a similar to another equation posted but it didn't have the exact stuff I needed and the solutions for fixing the equation didn't seem to work.

here is the code, thank you in advance!

​

import numpy as np
from scipy import sparse
import matplotlib.pyplot as plt
import scipy.integrate as integrate
from matplotlib.animation import FuncAnimation
from matplotlib import animation
from IPython import display

dx    = 0.02                      
a= 15
deltak = 0.2                      

E=3.48
x0= 2

             
A=1.0 / (deltak * np.sqrt(np.pi)) # normalization  

hbar = 1 

dt = 0.1  # time interval for snapshots
t0 = 0.0    # initial time
tf = 1.0    # final time

t_eval = np.arange(t0, tf, dt)
k =  np.sqrt(2 * E) 

y0=15
x=np.linspace(-15,17,745)

A=1.0 / (deltak * np.sqrt(np.pi)) 

V=  7.5 * x**2 * np.exp(-np.abs(x))

def psi_t2(t,psi):
    return  (-1j * (((-0.5 * D2.dot(psi)) + V * psi)))
   
psi0_ = np.sqrt(A) * np.exp(-(x-y0)**2 / (2.0 * deltak**2)) * np.exp(1j * k * x) 

sol_ = integrate.solve_ivp(psi_t2, t_span = [t0, tf], y0 = psi0_, t_eval = t_eval,method="RK23")

fig=plt.figure()
ax=plt.subplot(1,1,1)

ax.set_xlim(-15, 20)
ax.set_ylim(0, 5)
title = ax.set_title('')


line1, = ax.plot([], [], "k--") 
line2, = ax.plot([], [])

def init():
    line1.set_data(x, V)
    return line1,

def animate(i):
    line2.set_data(x, np.abs(sol_.y[:,i])**2)
    title.set_text('Time = {0:1.3f}'.format(sol_.t[i])) 
    return line1,

anim = animation.FuncAnimation(fig, animate, init_func=init,frames=len(sol_.t), interval=50, blit=True)

video = anim.to_html5_video()
html = display.HTML(video)
display.display(html)
plt.close()

​

If there are 2 million particles entangled with one another, 1 million particles are in box B1 and 1 million in box B2 . Probability of particles in B1 to be upspin is 66 % and downspin to be 34% same with B2 now if B1's particles were measured and were found to be approx 66 % upspin and 34% downspin particles , B2's particles will be 34% upspin and 66% downspin as entangled particles are mirror image of each other and they are oppsite in spin to the other particle , when the B1's particles were measured the probability changed for B2's particles. Are question , answer and reasonings corret? Pease review my question were the facts I provided were practical or this situation can be explained or not .

Suppose an object is moving along positive x axis with velocity V and radiates a photon parallel to Y-axis , the photon will travel with Veocity C in Y-axis but will it's velocity in X - axis be V or 0 . What will be trajectory of the photon that is ommited by an object travelling with some velocity?

If an electron is in a superposition of spin up and down, it's not really in both states at the same time, is it? Or is it just oscillating so fast that it appears to be that way.

Suppose 2 particles are entangled one is measured upspin the second particle will be 100 % downspin , but if additional energy is provided to particle 1,it changes it's spin. Now will the second particle's probability distribution will change or will it be down spin?

Suppose a particle is quantum particle is in superposition if it is measured it is found to be upspin after measuring will it again be in superposition or be in upspin state forever

I’m a physics and mathematics undergrad at a university where a lot of physics research is focused on quantum computing. As you probably guessed by my double major in math, I am interested in theoretical physics.

The head of my university’s school of physics does research in theoretical quantum computing, but what does that mean? What research is actually being done in theoretical quantum computing?

Thanks :)