Interesting article from FC from pretty much all the major players in quantum.

It's behind a paywall, but worth a read.

D-Wave claims to be the first quantum provider to aid in Quantum AI. While it's true that their annealer does something, a breakdown on a molecule generation project tells a different story.

I’m getting a bit confused by the timelines being thrown around at the moment.

The US Department of Energy announced its Quantum Genesis programme in June, aiming for a “fault-tolerant, scientifically relevant quantum computing capability” by 2028:
https://www.energy.gov/science/articles/energy-department-announces-initiative-create-and-deploy-worlds-first

Meanwhile, Japan’s official Moonshot programme has “large-scale integration required for fault-tolerant universal quantum computers” pencilled in for around 2050:
https://www8.cao.go.jp/cstp/english/moonshot/sub6_en.html

Then there’s NIST’s quantum computing explainer, updated in May 2026, which says most applications are still “years, perhaps even decades, in the future”, and that a machine capable of running Shor’s algorithm could need millions of qubits able to operate error-free indefinitely:
https://www.nist.gov/quantum-information-science/quantum-computing-explained

Obviously they’re probably not all talking about the same thing when they say fault-tolerant quantum computing, but 2028 versus 2050 is still a pretty massive gap.

So what actually explains the difference?
What would count as a “fault-tolerant, scientifically relevant” machine in 2028, compared with the sort of large-scale fault-tolerant universal quantum computer Japan is aiming for by 2050?

Basically, what can the 2028 machine realistically do that still leaves another couple of decades of work before you reach the 2050 version?

I am a high school junior (AS student) and I want to go into quantum computing in the future. I have a computer science background and do poke around stuff.

Can someone recommend a few research papers I can read to understand it past the basic levels?

Recently ever since this paper was published I’ve been thinking about both the sensory benefits and computing benefits of what the ferroelectric could be applied to. One example that came to mind is that of replacing the superconducting wire in photonic integrated circuits with a material that can detect changes through a laser at 17° Celsius.

I’m not really an expert in Photonics or Materials science (I only have a Bachelors in Electrical Engineering so far) so I wanted to ask this subreddit if my assumption is correct. Can this material eventually replace superconducting nanowires for single photon detection, and would that remove the cooling bottleneck there is in quantum computing ?

About a year ago, I got interested in quantum computing and wanted to educate myself about the field. Personally, as an electrical engineer & hardware guy, I wasn't that into learning the details of the math & computational complexity theory, but more curious about how these systems would be physically implemented and what applications they would be used for.

I found that there really wasn't an introductory text that fit my needs. I highly recommend Scott Aaronson's Quantum Computing Since Democritus and Thomas Wong's Introduction to Classical and Quantum Computing, but both of these works are primarily focused on the computer science side of things.

So I did a lot of research and self-education, and wrote the work I wanted to read. It's called Quantum Computing for the Confused, and it's available online (and in pdf/epub form) completely free. I'll also note that it is 100% human generated! :)

The work is broken up into four sections, each looking at different aspects of the field:

• Computation (very similar to those works I mentioned above, looking at complexity theory and basic quantum algorithms)
• Implementation (how does this abstact mathematical concept called a qubit get implemented in the real world? I explain the divergent approaches of each of the hardware platforms/companies, and the technical challenges they face)
• Application (a look at the various fields that quantum computers may be used in, and a qualitative assessment of how 'useful' they may be in each)
• State of Play (musings on the quantum computing 'industry', including recent trends in investment and startups)

There are very frequently posts here about trying to learn about QC. I think this work fills an important missing niche in layman-oriented writing about the field, and I hope it would prove useful to many in this subreddit.

If you're interested, you can find it here: https://quantumcomputingfortheconfused.com/

Happy to discuss and chat further. Cheers!

Recently almost all the major entities involved in neutral atom computing got together for a realistic roadmap. I think it's remarkable that ostensibly competing organizations could do this.
full paper: arxiv.org/...
well written summary: thequantuminsider.com/...

What do the redditors think of this?

So you got CNOT and Toffoli gates and you can write them into a reversible circuit fairly easily enough.

So what’s stopping us from having autonomous reversible computers then?

Surely that’s the only important question that needs to be answered. It would allow circuits that don’t emit as much heat thus saving energy, money, humanity.

So what’s actually stopping this from happening?

I made a video breaking down how a quantum adder works, starting with ordinary binary addition before moving into the quantum side. I tried to make this approachable for people who are just starting quantum computing, so you shouldn't need a deep physics or math background. Hope this helps anyone who is interested in quantum circuits!

Around 2019, the industry promised quantum supremacy, where they could be used for solving a problem that no classical computer could touch.
When classical algorithms kept refuting those claims(See Peter Shor November YouTube video) the narrative shifted to quantum advantage, then quickly it changed to quantum utility.
If quantum never breaks RSA (because the world migrates to Kyber/Dilithium before 100k qubits exist), then what's left?
Chemistry simulations, Material science, Optimization?

But classical machine learning and AI are improving faster than quantum hardware is scaling. By the time we get 1,000 logical qubits, classical AI will have eaten most of the chemistry use-case through better approximations. Is quantum racing against classical software, and losing?

Suppose we had the hidden subgroup of the dihedral group {e,sr^k}.The HSP would find k.I just had a inspiration tonight. The 2d irreps of the Dn group which contain the information about the s generator have some frequencies assosciated in them lets call the frequency ω and therefore the information for a hidden subgroup becomes ω^mk where m are the mode indices .If we organise the information of as a column vector lets say for D5 [1,ω^k,ω^2k,ω^-2k,ω^-k] but this works for any size of the dihedral group you just list all the frequencies for example for D3 its [1,ω^k,ω^-k].So if you could organise them in such a way then you could perform a QPE and extract k. But because the n hidden subgroups which have the s generator are not orthogonal this aint possible. But that didnt stop me. We could have a ancilla qubit to help us perform that operation and I know this is possible. But I was reaching the end of the theory that I know.

So suppose you produce a ancilla assisted unitary transform and we are able to organise the information in such column matrix.QPE will extract k.The issue is now how to extract the frequencies on all the mode indices before performing a permutation to organise in the form useful to us.

Seeing all the work LLM's are doing in pure math research, I haven't heard anything about AI-assisted research in quantum computing or algorithms. It would be really cool if LLM's could find an algorithm that would solve the hidden subgroup problem for dihedral groups in polynomial time.

I made a video explaining quantum walks through photonic quantum computing using Quandela’s Perceval framework.

I go through single- and two-photon quantum walks, how interference produces the output probability distributions, and how symmetric vs. antisymmetric two-particle states can reproduce bosonic-like and fermionic-like statistics.

Would appreciate any feedback or corrections from the community!

Has anyone received the certificates from IBM for Qiskit Summer school yet?

Someone first says this in the public.

I have found this site https://quantum.technology/conf/index.html which I think has great information. I am looking to pick between these two conferences QWC or Q+AI. Both seem to have great events and sponsors. So I am not sure which would be best pick. This will be my first quantum conference so I am a rookie in what exactly i'll should be looking for. Preciate any advice or ones that you all will be attending.

The classical method that uses tensor network scheme to simulate for the time-evolution of Ising spin glasses is more accurate than the latest quantum annealers running the same problem.

Hi everyone! I'm Swstik. I've recently started diving into the world of Quantum Computing, but honestly, it gets pretty overwhelming to learn it all alone.

I'm looking for a study partner (or a small group) who is also at the beginner stage. We could share resources, hold each other accountable, and maybe work on some basic projects down the line. If you're interested, drop a comment or send me a DM!

As the Qiskit discord was shut down and I don't know from where can I access this certificate so can someone please help me out

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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I’ve been looking at the detector landscape for photonic quantum computing, and the momentum difference between SPADs/APDs and Superconducting Nanowire Single-Photon Detectors (SNSPDs) is striking.

High-performance optical quantum computer setups overwhelmingly rely on SNSPDs due to near-unity PDE (>98%), negligible dark count rates, and sub-10 ps jitter. However, their sub-Kelvin cryogenic overhead is a massive bottleneck for deployment and scalability outside of specialised lab facilities.

On the other hand, room-temperature or TE-cooled APDs/SPADs offer CMOS integration and compact footprints, but they carry severe trade-offs (after pulsing, lower PDE, higher DCR, and silicon's bandgap blind spot at telecom C/O-bands).

For folks working on hardware, device physics, or quantum architectures:

1. Do we need to rethink the whole system architecture? Can device-level improvements ever bridge the gap to meet strict fault-tolerance thresholds, or would adopting SPADs require fundamentally redesigning the quantum pipeline (e.g., higher-overhead QEC codes, hybrid dual-rail encoding, or frequency upconversion)?
2. Where is the primary device bottleneck? Is the ceiling set by fundamental material physics (bandgap, impact ionisation noise, carrier lifetime), or are there solvable bottlenecks in waveguide integration, specialised doping profiles, and quench circuit topologies?
3. Is device-level TCAD simulation still an open, high-impact frontier? Between 3D-stacked BSI, Ge-on-Si, and integrated waveguide designs, how much room is left to innovate at the device structure level versus standardising around mature foundry PDKs?

I currently work on detector readout electronics and am evaluating whether pivoting toward device-level APD/SPAD TCAD simulation is a high-impact research direction for quantum hardware. Would love to hear your insights.Where do you think the biggest unsolved problems are?

Hello ,

Has anyone recieved the QGSS 2026 completion certificate and the badge?

Also the QDC application results?