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

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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.
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I have some troubles when I follow the documentation. https://docs.sqcircuit.org/index.html

Do you recommend other libraries to explore Superconducting circuits?

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?

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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.
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Hello :D

​

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?

Alice and Bob have qubits A and B, initialized at states |0⟩ and |−⟩ respectively. Charlie has two qubits A′ and B′ at states |+⟩ and |1⟩ respectively. Charlie first goes to Alice and applies a CNOT gate at qubit A′ keeping qubit A as control. Then he goes to Bob and again applies another CNOT operation where B is control qubit, B′ is the target qubit. After the described operation, the quantum state is described as |ψ⟩AA′BB′.

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 :)

I just wanted to provide a very realistic but somehow not really mentioned suggestions for beginners in this subreddit that, in order to learn Quantum computing, we do need to be good at handling linear algebra with complex scalar field. Not only just knowing, be good at it.

Linear algebra is literally the alphabet of QC and quantum mechanics in general. Superposition, interference, Unitary gates, etc., all become super handy once you know linear algebra. It is highly recommended to go thoroughly over Hermitian, spectral decomposition, Unitary, basis transformation, and matrix exponentiation.

For sure, some of them can be picked up as you learn Quantum information. For example, a lot of people encounter tensor products for the first time when they learn what entanglement is(this is actually where one of the weirdest things in learning QC happens: people have difficulty in understanding tensor product, and gets confused if it's the entanglement that's difficult to understand. No, true weirdness in entanglement happens only after local measurements are accompanied and trying to interpret the result.), or direct sum when they learn angular momentum addition.

However, I think it is a very good practice to have a mastery for the basic tool and make clear what you are struggling with, since it is often the case that the difficulty comes from not being familiar with new mathematical tools or notations, not necessarily understanding the concepts themselves(It is a bit tricky claim: it is nearly impossible to understand the core concepts without understanding the math behind it. e.g. how are you going to tell the difference between classical correlation between random variables and entanglement between qubits, without involving math? It's something that pop.sci have failed for decades)

I think there's a much weaker argument like this when it comes to learning actual quantum mechanics or differential equation since not every people interested in QC is interested in implementation of quantum devices or quantum error correction. But I would like to say linear algebra is the minimum requirement.

So in Shor’s algorithm I was taught that |1> is a sum of all possible eigenvectors for gate U. This allows for phase estimation of a random eigenvalue of one of those eigenvectors which we can use to find r etc etc… But why does using |1> work this way? I’ve seen proofs showing how |1> is a sum of eigenvectors and can understand the math behind it but I don’t understand how phase kickback can occur if |1> itself is not an eigenvector. U|1> = |a mod n>, not |1>, so how does phase kickback even happen?

Any help with this would be much appreciated. I’ve been stuck trying to wrap my head around this for almost a week now 😅

Edit: for clarification the unitary gate I’m referring to here is U|y> = |ay mod n>

I have posted this to the weekly thread but no one answered so i am posting here

i have been thinking about the similarities between Non-Deterministic finite state machines and Quantum computers , now when i researched about this Quantum computers can't be compared to Non-Deterministic finite state machines because they are probabilistic but why does that mean it can't be Non-Deterministic ? i mean Non-Deterministic transitions in finite state machines at its core is defined by Changing to random states regardless of the input , and according to my understanding is that in Quantum mechanics outputs don't get affected by any seed values(otherwise it would be pseudo-random like coin-flips/rolling Dies or a standard computer RNG) so even tho it is probabilistic it doesn't depend on any seed values therefore i can't see any difference between it and Non-Deterministic Finite State Machines. now IF someone argues that Non-Determinism can't have probabilistic outcomes then couldn't i argue that Quantum mechanics isn't random as it isn't Non-Deterministic therefore Deterministic (unless we consider randomness a spectrum and Quantum computers aren't high enough on the spectrum to be modeled by NDFSMs ?)my background is mainly in Computer science & Engineering so there might be something here about Quantum mechanics i don't understand?

Update on April 18: Step 9 of the algorithm contains a bug, which I don’t know how to fix. See the updated version of eprint/2024/555 - Section 3.5.9 (Page 37) for details. I sincerely thank Hongxun Wu and (independently) Thomas Vidick for finding the bug today.
​    Now the claim of showing a polynomial time quantum algorithm for solving LWE with polynomial modulus-noise ratios does not hold. I leave the rest of the paper as it is (added a clarification of an operation in Step 8) as a hope that ideas like Complex Gaussian and windowed QFT may find other applications in quantum computation, or tackle LWE in other ways.

https://preview.redd.it/mqpdmz4v7ivc1.png?width=673&format=png&auto=webp&s=c005802844603b6d7fce7f35803c709dcd71d603

Author homepage （updated）：

http://www.chenyilei.net/

Updated paper：

https://eprint.iacr.org/2024/555

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.
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Hey fellas! I am an undergraduate electrical engineering student (1st year) and I proposed to my professor to start a research project on quantum technology but due to lack of infrastructure available for quantum hardware and technology at uni he insisted that rather we should work on quantum error correction.Given the fact that I am still a newbie in this field and I have a natural tendency towards quantum hardware.What do you guys suggest should I start working with him or it will not provide any relevant skills that I need to have . Any further suggestions are really appreciated.Besides, he told me that he will individually teach me all the prerequisites and skills for error correction.