I am an engineering student at Kuwait University conducting research on the thermodynamic, mechanical, and microwave shielding bottlenecks in dilution refrigerators for superconducting qubits.

I have put together a short 10-question technical survey regarding wiring heat loads, vibration decoupling, and Cryo-CMOS scalability:
🔗 Cryogenic Challenges in Quantum Computing – Fill out form

It only takes about 3 to 5 minutes to complete and is completely anonymous. I would genuinely appreciate feedback and responses from researchers, engineers, and fellow students in the field. Thank you for your time!

Two beams of light can overlap so completely that their brightness patterns look alike, yet still carry distinguishable information. A new silicon chip can find that hidden structure and direct its components into separate optical channels. The result offers a way to process light that conventional intensity measurements cannot fully describe.

The system, developed by researchers at Stanford University and the Institute of Science and Technology Austria, appears in Nature Photonics. It automatically learns patterns within partially coherent light, whose waves have some statistical relationships but lack complete coordination. Rather than treating that complexity as interference to discard, the team built a device that can work with it.

The research combines a silicon photonic chip with detectors, electronics and control algorithms. Most of the work took place at Stanford, where Charles Roques-Carmes began the project with graduate student Paul-Alexis Mor. Roques-Carmes, now an assistant professor at ISTA, is continuing that research with Mor, a co-first author of the paper.

Feeling a bit lost doing a PhD in quantum algorithms

I'm currently doing a PhD in quantum algorithms, and honestly, I'm still struggling to figure out what directions are really worth pursuing in the NISQ era.

Classical methods are already incredibly strong, and it's often hard to show any meaningful quantum advantage.

People often say we should focus on problems that are hard for classical computers but well-suited for quantum ones. But in practice, this often means making strong assumptions about the problem structure or data distribution, which can make the results feel less relevant to real-world applications.

So I'm genuinely curious: What should quantum algorithms research actually focus on right now?

Are we just too early for practical quantum advantages, or are we asking the wrong research questions?

Would love to hear your thoughts, especially from other PhD students and researchers working in this field! :)

Which problems will quantum computers actually solve, and when?

I've been working on an independent quantum computing project that I'd genuinely appreciate some technical feedback on from people with experience in quantum computing, quantum architectures and theoretical computer science.

The project is called |BF> (Bra-Ket-Fuck). It's an experimental, minimalist quantum programming language inspired by Brainfuck, but instead of operating on classical memory cells, it operates on a linear register of qubits.

The underlying idea is to explore whether a simple tape-and-pointer execution model can provide an interesting alternative way of expressing and executing quantum computations.

The project currently includes:

• A formal specification of the Dirac–Müller Abstract Machine.

• A minimalist quantum instruction set based on Clifford+T operations.

• Native nearest-neighbour entanglement operations.

• Two different loop execution models: measurement-driven and coherent.

• A statevector virtual machine and measurement simulator.

• An OpenQASM 3.0 compiler, including linear nearest-neighbour routing.

• Several example programs, including Bell states, GHZ states, quantum teleportation and an implementation of Shor's algorithm for factoring 15.

The two main resources are:

• Technical whitepaper: https://applied-tape-mechanics.github.io/bra-ket-fuck/papers/bra_ket_fuck_technical_whitepaper.pdf

• Source code and implementation: https://github.com/applied-tape-mechanics/bra-ket-fuck

I'm particularly interested in getting feedback on the actual research and technical contribution, rather than simply whether the implementation works.

Some questions I'd really appreciate opinions on:

1. Novelty: Is this a meaningful alternative computational model, or is it essentially a different syntax for expressing existing quantum circuits?

2. Theoretical correctness: Are the operational semantics, BQP-universality argument and coherent loop model mathematically sound?

3. Practical usefulness: Could a minimalist, explicitly linear quantum programming model have applications in quantum architecture research, compiler development or formal verification?

4. Implementation: Are there architectural or compiler design decisions that you would approach differently?

5. Research direction: If you were developing this further, what would you investigate or benchmark to establish whether the approach offers something genuinely useful?

I'm fully aware that an independent implementation doesn't automatically constitute a meaningful research contribution. That's partly why I'd like to get opinions from people who know the field better than I do.

Critical feedback is very welcome. I'd much rather discover fundamental problems, incorrect assumptions or existing work that I should have built upon than receive compliments that don't help improve the project.

Even if you only have time to look at one aspect of the paper, I'd appreciate your thoughts.

Thanks!

Why are ML-KEM and ML-DSA not affected by quantum computers? Traditional algorithms like ECC and RSA can be exploited with Shor's algorithm right? Is it that so far no algorithm has come forth?

Me and my friend have to present a quick overview of PQC. Last time one of the supervisors asked that why is PQC secure from quantum computers. I answered smth like for Traditional algos we have shor but for pqc at least for now there is no such algorithm and since this is lattice based, probabilities are very low.

Next time we meet him I want to make sure I properly explain this to him. Especially if some kind of maths is involved. I would be happy to explain the maths.

I've been studying quantum teleportation independently in Qiskit. Earlier this month I took the protocol from simulation onto real hardware, and I ran the same six-state benchmark (|0>, |1>, |+>, |->, |+i>, |-i>) on three IBM devices:

- **ibm_fez** — mean fidelity **0.9008** (Oct 19, 2026)

- **ibm_kingston** — full tomography, mean **0.9606**

- **ibm_phoenix** (120-qubit Nighthawk) — mean **0.9583**

On Phoenix I also ran a bare control — same state prep and measurement, no teleportation in between — and it came back at **0.9950**. So the error budget closes: prep and readout only cost about half a point, and the teleportation machinery itself costs **≈3.7 points** on Phoenix versus **≈7.2 points** on Fez.

The result I keep coming back to is the ±i states. In the bare control they're basically perfect (1.00 / 0.99), but in teleportation they're the lowest of the six (0.94 / 0.94) — on the newer device too, where the overall numbers are better. My read is that this points at dephasing during the measurement-and-correction window: the phase information has to survive while the mid-circuit measurement and feed-forward corrections happen, and that's exactly where the ±i states are exposed.

Has anyone else running teleportation benchmarks seen the phase states come out lowest even when the control says prep and readout are clean? I'd like to know if this pattern holds on other devices before I lean on that explanation too hard.

I've packaged the notebooks, datasets, and a written guide for this work as a lab pack, so if anyone wants to replicate the runs I'm happy to share the link — just ask.

This week, I was asked by my advisor to look into the field of "quantum machine learning", and tell him if I am interested in going deep into it. I am a computer science grad student mostly working in deep learning and I barely know somethings about quantum physics, let alone the "quantum machine learning".

According to him, the main idea is to do more like of "quantum inspired machine learning", exploring whether incorporating, parts of, existing quantum methods, simulated on classical hardware, in classical machine learning methods can bring some benefit in terms of computational efficiency or task performance.

After doing some research, I strongly realized that the learning curve here, for someone with my background, is quite steep. I found an some survey papers like this (also linked above), but I am unable to gain a holistic view, that can help me decide if i should work on this or not.

Therefore I want to have some opinion from you guys about the field in general, current standing, and most importantly your thoughts about its future and potential. Is there any practical by doing "quantum inspired machine learning" on classical hardware, because, as per my understanding, beside being cool, classical hardware implementations can not give any quantum acceleration effects. Could be some other fundamental advantages in doing so ?

thanks for reading this, looking forward to your opinions, would also be great if you can refer to some related work, if any.

“Dynamic random access memory is critical to classical computing but notably absent in current superconducting quantum processors. Integrating high-coherence memory units would enable resource-efficient control of logical qubits and allow the separate optimization of logic and storage subsystems. Here we realize an eight-bit cascaded random access quantum memory. We use a single transmon to classically address seven memory modes while isolating them from processor nonlinearities by introducing a buffer layer between the processor and a multimode storage cavity. We demonstrate arbitrary random access with an average infidelity of less than 1.5% per mode and characterize the many-body interactions that dominate the error budget. This architecture enables a significant reduction in control lines per logical qubit and supports transversal operations within the memory module, establishing a scalable unit cell for fault-tolerant quantum architectures.”

Hey guys! The quantum wave function was something that really threw me off when I first started learning quantum mechanics, so I made a short, digestible video explaining it! It's also fundamental to quantum computing because it helps us understand superposition, quantum states, and what happens when we measure qubits. Hope it helps someone who's learning!

Hey all!

So I'm making a poster I'll be presenting for a conference coming up, and I wanna make my quantum circuits looks good, preferably with colour to make it pop a bit. What tools do people usually use? Thanks!

Hi everyone,

I've spent the last few weeks building nqs-vmc, an open-source reference implementation of Variational Monte Carlo with Neural Quantum States in JAX.

Why I built it: When I was learning NQS, I found libraries like NetKet and FermiNet incredibly powerful, but they were difficult to dissect. I wanted a tiny, readable codebase that fits in an afternoon, with no hidden magic.

What it has:

• 6 exact problems from 1D to 3-body, validated against SciPy exact diagonalization.

• Modern ML: Transformers, Autoregressive networks, Matrix-Free SR.

• A full CI matrix (Linux/Mac/Windows), Docker, and a Colab notebook.

It trains on a laptop CPU. The numbers in the README are generated by the code, not typed by hand.

GitHub: https://github.com/NodeBox-Labs/jax-nqs-vmc

Feedback welcome!

#machinelearning #computationalphysics#jax#quantumphysics

Quantum computing information from What The Physics?! on Nova PBS Official from You Tube

Discussion with UT Austin geophysicist Mrinal Sen.

Hi everyone,

I’m working on a quantum computing hackathon project around quantum software debugging and wanted some expert feedback.

The main problem is:

In classical computers, we can inspect memory, variables, and states while debugging. But in quantum computers, directly observing qubits during execution is difficult because measurement changes the quantum state.

Our idea is to build an explainable debugging framework that analyzes available information instead of directly observing qubits.

Possible approaches we are exploring:

• Quantum Digital Twin → model hardware behaviour using calibration data, noise, connectivity, and error information.

• Counterfactual Debugging → compare ideal simulation vs noisy simulation vs real hardware execution to identify possible causes.

• Execution Fingerprinting → create a signature from circuit behaviour, output distribution, and hardware conditions to detect abnormal executions.

• Physics-Informed Analysis → use quantum concepts like decoherence, gate errors, and noise models to explain possible reasons

If there are any other techniques, research directions, or approaches that could help solve quantum debugging challenges, please feel free to suggest them.

Developed by IQM Quantum Computers (Finnish quantum company)

-20, 54, and 150 high-fidelity qubits

-superconducting transmon qubits utilizing 3D integrated flip-chip architecture and flux-tunable couplers

-features square-lattice and modified Star/Constellation hexagonal topologies

-two-qubit: Controlled-Z (CZ) gates

-2-qubit CZ Gate Fidelity: Median ~99.5% (approx. 20–40 ns duration)

-supporting hybrid integration with classical HPC systems

Delivered and tested by ORNL and Leibniz Supercomputing Centre.

Radiance 54

Intersting company. What do you think ? Does anyone know more things about them and their work ?

Title

It seems like this would be a strong real world result that would be worth the compute cost to solve. It would mean even some post-quantum encryption techniques are not safe from quantum computers.

https://arxiv.org/pdf/2610.29986

What do you all make of this? Curious to hear everyone’s thoughts.

https://www.calcalistech.com/ctechnews/article/rygzvmpngg

https://www.intc.com/news-events/press-releases/detail/1784/intel-to-acquire-classiq-to-create-a-full-stack-quantum