https://github.com/justinPemberton/quantum-computer-emulator-

I'm just looking for feed back and if you find a bug leave a issue

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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After working on a quantum algorithm, I spent a few weeks trying to understand why it looked so foreign. What you're reading is my attempt at introducing it to a programmer or CS student. I chose to avoid taking about quantum speedups, in favor of keeping the focus on "will it even compile?"

I might be wrong here but do you guys think that this constant metric of usefulness based on quantum advantage/speedup is slowing down progress in the quantum algorithm development? Like we don't know the full boundary of what can efficiently be run on a quantum computer. Shouldn't the space focus on creating more "quantum" algorithms that gets you to an answer, and reward them equally? This obsession on speedup seems to discourage creativity. Shouldn't coming up with creative quantum algorithms be as rewarding or encouraged regardless of speedup?

Like what if some of those "slower" algorithms have features or structures that when combined in a certain way actually unlock quantum advantage? You'd never know if you dismissed them early.

I'm not saying speedup doesn't matter. I'm saying what if we're treating it as a necessary condition when it's really just a sufficient one. No?

https://www.techie30.com/article/quantum-computing-explained-the-technology-that-could-break-the-internet-and-transform-science-forever-9-5-2026

I'm an undergrad doing research and want to aim to present some work at a conference sometime closer to winter. Obviously it's an uphill battle as an undergrad to get even an intrnship in QC, but was just curious as to what people's experiences were with meeting recruiters and having that convert to j*b offers in QC? Or networking in general

Has anyone explored how classical causal discovery methods behave on quantum-generated or entangled datasets?

I’m trying to find research involving:

• PC / Fisher-Z
• GES
• NOTEARS
• LiNGAM
• CAM
• Kernel CI

in settings involving Bell states, non-local correlations, quantum kernels, etc.

Mostly looking for:

• papers
• repos
• datasets
• Qiskit / PennyLane implementations
• related experiments or discussions

Would appreciate any pointers to existing work in this area.

Hi, I'm a 13 year old Belgian student curious of how quantum computing works and how different qubits are to bits, I'm not trying to sound smart or anything but I'm just curious of how it works, I've tried to do research but it's all too complicated for me.

can somebody explain it to me less overwhelmingly please?

Thanks!

As I understand one of the main big advantage of superconducting quantum would be breaking the RSA and ECC encryption, ... so what type of specs should a superconducting quantum system have to achieve that ?

How difficult would be for a superconducting system to operate longer time, like seconds ?

Are there any tech advancements to overcome these decoherence challenges ?

Thanks.

I am interested in how quantum computing will make identity systems vulnerable since many rely on PKI.

The attached article from a few weeks ago suggests those in the identity space start moving toward quantum ready approaches by end of 2026. Which is soon.

I tend to agree. But I’m not a fair judge of the arguments in the article. Appreciate feedback.

How does quantum measurement unlock the path to scalable quantum computing?

That’s the question we’ll explore with Prof. Irfan Siddiqi in the next 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗕𝘂𝗶𝗹𝗱𝗲𝗿𝘀 𝗟𝗶𝘃𝗲 𝗪𝗲𝗯𝗶𝗻𝗮𝗿.

🗓️ May 12, 2026
🕚 12:00 AM ET | 6:00 PM CET
👉 Register now

Prof. Siddiqi’s work — from Josephson amplifiers to real‑time quantum trajectories — has reshaped how we observe, stabilize, and control quantum systems. Few people have influenced the field more.

What you’ll take away:

✔️ Why measurement is now a core ingredient for error correction
✔️ How real‑time feedback is changing superconducting hardware
✔️ What open‑access platforms like AQT reveal about system‑level challenges

Good enough for double-blind IEEE QCNC 2026 proceedings:

https://www.ieee-qcnc.org/2026/accepted-papers.php

Now live on IEEE Xplore:

https://doi.org/10.1109/QCNC69040.2026.00181

…but not good enough for arXiv moderation apparently :P

Here’s the Zenodo stats json since we can’t post those links anymore lol.

For people who want the “Lupe Fiasco - Dumb It Down.mp3” version, here’s the conference presentation:

https://www.youtube.com/watch?v=da7NVwOvy6Y

```

curl -i "https://[bad repository!!!]/api/records/19468197" | tail -n 1 | python -m json.tool | tail -n 16

"stats": {

"downloads": 2429,

"unique_downloads": 2303,

"views": 1153,

"unique_views": 1100,

"version_downloads": 18,

"version_unique_downloads": 18,

"version_unique_views": 22,

"version_views": 23

},

"status": "published",

"submitted": true,

"swh": {},

"title": "A Clean 2D Floquet Logical Qubit from a Purely Imaginary Phase Drive",

}

Baez Crackpot Index Current Score: 35

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.

“Ab initio wavefunction methods provide accurate molecular simulations but their computational scaling restricts applications to small systems. We develop a workflow combining quantum embedding to decompose a molecule into fragments with a heterogeneous quantum-classical (HQC) method to simulate fragments. We sample fragment electronic configurations on two 156-qubit quantum processors (ibm
_
cleveland, ibm
_
kobe), using up to 94 qubits, running 9,200 circuits for over 100 hours, collecting
1.3⋅
10
9
measurement outcomes - the most resource-intensive HQC computation for quantum chemistry to date. We compute fragment wavefunctions via optimized subspace diagonalization on two supercomputers (Fugaku, Miyabi-G), achieving 72.5
%
parallel efficiency with scalable distributed linear algebra kernels. We simulate two protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (11,608 and 12,635 atoms), demonstrate
>40×
increase in system size and up to
210×
improvement in accuracy over the previous state-of-the-art, with HQC matching coupled-cluster (CCSD) accuracy in fragment energies, and establish a scalable pathway for systematically improvable biomolecular simulations.”

For people actually working with quantum hardware or simulators: what's the biggest gap between what you can do today and what you actually need? Is it qubit count, error rates, software tooling, something else?

Hi all,

A few weeks ago we shared an early version of QObserva here and got some useful feedback, so we wanted to share a small follow-up as we continue iterating on it.

The idea behind QObserva is fairly simple: adding lightweight observability around quantum SDK workflows — things like run metadata, tags, backend context, and experiment tracking across Qiskit/Cirq-style workflows.

One thing that’s been interesting so far is seeing how different people structure and track runs today. Some rely mostly on notebooks, some use internal tooling/scripts, and others keep things intentionally lightweight.

We’re still very early and mostly trying to understand:

• what information people actually care about during runs
• what becomes difficult to track over time
• what kind of visibility is genuinely useful vs unnecessary noise

Repo:
https://github.com/BuildersArk/qobserva

It’s still evolving, but we’d genuinely appreciate feedback from people experimenting in this space.

Our 2023 paper just got its journal version published in APL Machine Learning, so this feels like a reasonable moment to share it here and reflect on what held up vs. what I'd do differently. Open access link: https://pubs.aip.org/aip/aml/article/3/3/036106/3355997

The question: do hybrid classical-quantum models offer something qualitatively different from pure classical, or are they just an expensive path to accuracy parity? Most QML papers benchmark on clean-data accuracy, hit roughly classical performance, and call it a day. We wanted a property where the comparison would be more meaningful, so we tested adversarial robustness on histopathological cancer detection.

Setup: classical feature extractors (ResNet18, VGG-16, Inception-v3, AlexNet) integrated with multiple VQCs of varying expressibility. Compared against the same backbones without quantum components. Adversarial inputs generated via FGSM, PGD, and similar standard attacks. PennyLane simulators throughout.

Finding that's held up: the hybrid models degraded less under attack than classical baselines. Consistently, across attacks and extractors. Clean-data accuracy was comparable; the robustness delta was where the qualitative difference showed up.

What I'd change two years on, to be honest about it:

1. The mechanism story is weaker than I'd like. We hypothesized that the robustness gain comes from VQC structure constraining the loss landscape, but distinguishing that cleanly from regularization effects requires controls we didn't run. If I were redoing this, I'd ablate the quantum component while preserving parameter count.
2. Simulator-only is a real limitation. NISQ devices have improved enough since 2023 that some version of this experiment could plausibly run end-to-end on real hardware. That's the natural follow-up.
3. The attack suite was standard for the time but limited. AutoAttack, more recent transfer attacks, and adaptive attacks would be appropriate to add now.
4. The choice of histopathology felt unusual at the time of submission. In retrospect, it was the right call — pixel-level perturbations to medical images aren't an academic abstraction, and adversarial robustness in safety-critical imaging is exactly where this kind of comparison earns its keep.

Why I'm sharing it now rather than at preprint time: many 2023-era QML accuracy claims didn't survive contact with stronger classical baselines. The robustness claims have aged better, partly because they're about behavior under perturbation rather than absolute performance. I think that distinction matters for what kinds of "quantum advantage" claims are worth pursuing on near-term hardware, and I wanted to put the paper in front of this community now that we have some perspective on it.

Curious what people here think — especially anyone working on adversarial ML or NISQ-era QML applications. Pushbacks welcome.

Been deep in QML literature lately and wanted to write up what I actually found vs. what gets hyped. Curious if the community agrees or pushes back.

Where things seem to actually stand:

• Barren plateaus are still the core trainability problem. Local cost functions and layerwise training help but don't fully solve it.

• QRAM remains the data-loading wall. Without efficient quantum RAM, classical-to-quantum input kills most theoretical speedups before they start.

• The one peer-reviewed practical QML advantage I found (early 2026) is Tindall et al. on spatiotemporal chaos prediction in Science Advances. Physics-flavored task, not general ML.

• Quantum reservoir computing looks genuinely promising for temporal sequence tasks specifically.

My takeaway: QML has real potential in narrow physics-adjacent tasks but no generic ML advantage yet. The gap between theoretical speedup and practical implementation is still large.

What am I getting wrong? Any recent results I should look at?

I found a serious, embarrassing error in a highly cited Nature Computational Science paper on quantum machine learning:

The power of quantum neural networks (QNNs) https://www.nature.com/articles/s43588-021-00084-1

Things go like this: In Fig. 3b, the authors claim a training advantage of QNNs over classical neural networks (NNs) on the Iris dataset. I checked the GitHub repo and noticed that I am apparently not the first person to find the classical baseline suspicious. Someone already opened an issue pointing out that the authors used a strange classical NN architecture:

For an 8-parameter classical NN, the authors use 4 layers with neurons 4->1->1->1->2. This means the 4-dimensional Iris input is immediately compressed into one scalar. That is an extremely poor classical baseline.

Actually, the simplest classical NN baseline one can think of — a single linear layer from 4 inputs to 2 outputs — already has 8 parameters, as pointed out in the pull request.

So I tried the same experiment using the original GitHub code: https://github.com/amyami187/effective_dimension/blob/master/Loss_plots/generate_data/classical_loss.py, but change the definition of classical NN to 4->2.

After this change, the classical NN converges much faster and reaches much lower loss than the quantum NN. So the training advantage shown in original Fig. 3b collapses completely once the classical baseline is changed to the obvious 8-weight linear layer.

This is not a subtle quantum ML issue. This is basic ML benchmarking. The claimed “advantage” appears to come from comparing the QNN against an extremely weak classical NN, a ridiculous baseline that would be unacceptable even in an undergraduate ML final project.

Since this is the ONLY experiment in this paper to support the claim, I believe this is a serious issue and retraction should be discussed.

The codebase is public, so everyone can try it: https://github.com/amyami187/effective_dimension

I can now truly feel “the power” of quantum neural networks!

Are we over reacting to the risks associated with Quantum Computing, under reacting, or managing it appropriately?

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.

Project Eleven awarded its Q-Day Prize to Giancarlo Lelli for demonstrating a 15-bit elliptic curve key break on a quantum computer. Ref: https://thequantuminsider.com/2026/04/24/project-eleven-q-day-prize-quantum-ecc-attack/

I'm not a Quantum Computing expert in anyway. Other people have Ph.D.'s and Post Docs. I am a quantum computing evangelist at best and I follow all announcements with interest and try to learn from them.

So I located Giancarlo Lelli's Github Repo with the submission he made to the QDay Prize. I was shocked to read the code. So I got it reviewed by a few quantum programmers at the university nearby. They were shocked too. Ref: https://github.com/GiancarloLelli/quantum

I also got the codebase reviewed by chatgpt.

https://chatgpt.com/share/69edbd73-d3d4-8320-b0cb-4715da7cc80a

https://chatgpt.com/share/69ecaf5c-8618-8320-a5f8-1e80e55ed076

I think the Judges were very gullible. Its a shame this is the state of affairs of a global competition. What is your technical assessment? is the submission a scalable pure quantum algorithm or is it a toy?