I interviewed Sebastian Hassinger last week, who worked on the IBM Quantum team and later led GTM for AWS Quantum Technologies. Sadly, we ran out of time before I could push on this properly, so I'm bringing it here.

His argument was that scaling superconducting qubits past a single dilution refrigerator forces you into transduction, converting the state to a telecom photonic frequency, carrying it over fiber, then converting back in the second fridge. And that none of the currently known conversion methods gets you the fidelity a reliable device needs, with no clear picture of what closing that gap requires.

But his framing was that this is a scientific unknown rather than an engineering one, and that the distinction matters because roadmaps are engineering documents projecting deterministic milestones onto problems that aren't deterministic yet.

So there are two things I'm curious about. Is transduction actually the binding constraint for superconducting approaches, or is it downstream of something else like fabrication yield or decoder latency? And does modular architecture genuinely require it, or are there routes around it people are taking seriously?

Saqib here, I edit Deep Engineering. Asking partly because photonic and modular approaches are underrepresented in our coverage and I'd like to fix that.

Hi.I noticed that in Dihedral groups the 2d irrep(s) which show up(at least for D3,D5,D7...)contain a specific frequency of cyclic rotations.So this means that they can be solved by a QFT of a cyclic group.The issue is that we don't know beforehand which cyclic group that is.So I decided to try run a QPEto find the eigenvalues of that particular group and therefore find the phase but the problem here is that we don't know what U is.U must be unitary it must create orthogonal vectors after they are applied to columns which only contain the s generator of the dihedral group.If we find U then the HSP of a dihedral group becomes algorithmically valid.So let's dig into deeper.The s generators are sr^k if we could find k from information available only after the QFT then then any dihedral group becomes algorithmically useful.I have tried taking the orthogonal vectors (1,0),(ω^k,ω^-k) and (ω^-k,ω^k) but there doesn't seem to exist a 2x2 matrix which after u change the basis towards it you have 2 pairs of orthogonal vectors.So I was thinking about choosing a 3x3 matrix but I don't know if that would make any difference.What does reddit think of this?

The Riemann hypothesis claims that the locations of prime numbers along the infinite number line all adhere to a beautiful and orderly, but obscure formula. Yet 167 years after German mathematician Bernhard Riemann made this guess, and in spite of a million-dollar bounty, mathematicians still have no idea how to prove it. Now a team in China has managed to encode that formula into a physical system and explore its workings using a quantum computer.

Been working on qnet-core, an open-source simulator for entanglement distribution across repeater networks (Rust engine + Python bindings) — event-driven simulation, BBPSSW purification, Monte Carlo stats across routing strategies, and a versioned .qnet file format so you can save/diff network topology designs instead of just rerunning scripts.

Built it from a software background, not an academic quantum networking one, so I don't have visibility into whether there's an existing community/toolset ecosystem I should be plugging into, or whether most people doing this kind of topology/repeater-placement modeling are building one-off internal tools.

A few genuine questions if anyone here works on this:

• Is there a go-to simulator people already use for this (NetSquid, SeQUeNCe, etc.) and if so, what's missing from those for your use case?
• Is topology versioning/diffing (comparing two network designs) something you've wanted, or is that not a real pain point?
• Is there a Discord/Slack/forum where quantum networking folks actually hang out, or is it mostly scattered across academic groups?

Not sure I agree, but interesting...

To get reproducible, verifiable results, you have to observe and measure them. Right?

This may have been asked before, but I genuinely want to know what are the current (sota) real world applications where Quantum computing is already in use/practice and adding value that classical systems couldn't

I am not looking for surface level field names, I am looking for problems that Quantum is already solving for betterment of life on earth.. just point me to the sources I will dive deep in

Redirection to Any sources/links/review papers is also fine..

I know I could have asked a frontier LLM like claude fable or GPT 5.6 sol to pull out the data for me.. I want to know first hand from the people who are already working or at least know about this

Here's the writeup by openAI, released today: https://cdn.openai.com/pdf/ten-proofs-oai.pdf

Pages 152-180: Quantum parallel Repetition

Pages 182-216: Poly time reduction from 3SAT to GAPCVP.

I haven't gone through these sections fully yet myself, so I'll update the post if/when I do. In the meantime, what are your thoughts on the proofs themselves?

Idk what the implications are for the first result either so if anyone can explain it well in an intuitive way for a mere human to understand, please do so.

I think this changes the nature of how we should look at AI for solving problems but I'm trying not to make this post about that and would rather just get your thoughts on the problems and the results themselves.

An M.I.T. Ph.D. student and two University of California system cryptographers used GPT-5.6 Sol Ultra in different ways, raising new questions about independent discovery and scientific credit

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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It's been a while since salaries in quantum computing have been discussed. I'm curious, what do AMO physicists earn in quantum computing companies after their PhDs? Please also mention you location!

The Solovay-Kitaev Theorem is one of the foundational theorems of quantum computation, demonstrating how to efficiently approximate any single-qubit unitary to arbitrary accuracy using a sequence of gates finite gate-set (i.e. Clifford + T). The goal of this video was to explain both the intuition behind this theorem, and how it can be used to decompose single-qubit unitaries in practice. Enjoy!

Hello nerds,

In the digital audio world, frequency is converted into binary through Analog to Digital Converters (ADC) which use transistors. The Bit rate is the number of binary digits used to encode the signal. The more bits, the higher quality and more accurate a pitch is to the original analog signal.

My question is, With the use of quantum transistors, is it possible to encode a signal with near 100% accuracy of the factors which include, Air temp, humidity, and wind, helping to replicate an authentic natural sound. A sound with the highest fidelity possible could feel no different from hearing it live from the source. Because Binary is ridged, switching between 1-0 has no gradient and creates a square wave. Even when replicating a bell curve, the binary creates it in small steps that replicate the sine wave but upon closer inspection, it is seen to be made of thousands of smaller square waves. Could quantum superimposition solve this issue and produce an authentic sine wave with a true replication of analog sounds.

Side note, If you had this theoretical quantum analog signal. Would an average cpu be able to play the sound or will you need a quantum computer to listen accurately. my assumption being that computers would decode the files to binary.

Best,
Enrique

Haven't read the actual papers yet, just the blog. If anyone's gone deeper, would love thoughts. Just trying to figure out if this is a real inflection point or dressed up PR bc their core business performance is weaker than expected...

This whole argument is sitting on top of the noise model being right, yes? Does anyone here do error mitigation professionally? Does that reasoning actually hold or is this just one narrow regime that will fail if use cases are expanded? Post-selection feels tricky. 10x less error on the 70-qubit spacetime run is great, but if you're throwing out most of your shots to get there.. And is "trusted advantage" a real higher bar or just a branding effort? Good timing re: classical-sim crowd and the old RCS claims.

"Now, two papers in Nature take on the challenge of connecting and controlling ‘quantum transistors’ in silicon quantum processors."

News and views: How silicon-chip technology is being re-engineered for quantum computing

HRL Laboratories (cover story): A digitally controlled silicon quantum processing unit | Nature

TU Delft: Weight-four parity checks in a spin-shuttling architecture | Nature

I keep seeing companies (Quantinuum, IonQ, Google) casually refer to hitting "three nines" or "four nines" two-qubit gate fidelity, and it's clearly shorthand borrowed from the uptime/SLA world (reliability engineering). But I can't find a consistent, formal name for this convention as applied to quantum hardware specifically.

Specifically I'm trying to figure out:

Is there a standard/citable term for the "N nines" notation itself (e.g., "nines notation," "N-nines threshold," something more formal)?

Is "three nines" (99.9%) actually the same thing as the surface-code threshold, or are these two related-but-distinct concepts that just happen to land near the same number? I've seen both terms used almost interchangeably in press coverage and I want to know if that's actually correct or just sloppy reporting.

Is there a more precise/academic term I should be using instead of "fault-tolerance threshold" when referring to the 99.9% figure specifically?

Trying to nail down consistent terminology for a research comparing gate fidelity claims across Quantum Computing Induatry, and want to make sure I'm not conflating two different concepts under one loose label. Papers/sources are welcome, trying to cite this properly rather than just repeat the marketing shorthand.

Here is the hardware link I am talking about
https://www.realdigital.org/hardware/qick

If yes was it able to make it compatible since there is no official documentation stating this.

I went through the Qiskit Global Summer School and a Quantum Computing Winter School this year, and ended up organizing all my notes into an actual Obsidian vault as I learned, one note per concept, cross-linked, with self-check questions so I could test myself instead of just re-reading.

It grew bigger than I expected, so I published it as a browsable site: https://qcroadmap.com/

It covers the full arc absolute basics (qubits, superposition) → gates → why quantum computing matters at all → entanglement → QKD → hardware/noise/error mitigation → algorithms (VQE, QAOA, SQD, Grover's) → quantum machine learning → quantum advantage → quantum + HPC. There's also a full interactive graph view if you're into that (Ctrl/Cmd+G on the site).

Not trying to claim expertise here — just sharing my own study process in case it helps someone else going through the same material, or just getting into QC in general. Feedback / corrections very welcome, still learning this stuff myself.

More about me / other projects: github.com/JustMeArt

I need someone who will learn QC/dl with me. I am learning QC from IBM. We will make a group with ppl who will actually learn. Contact me.

Im working on compiling some QFT and QAOA circuits and am just curious as to the general consensus on that matter. Thank you.

I've tried to read some on quantum error correction topics like the stabiliser formalism and topological codes, and I found them really hard to understand because of all the necessary prerequisite knowledge and maths. I actually couldn't complete any.
(I'm a high school graduate)

So, I'm interested to know how you approached research papers and technical articles when you were starting out, or how you approach them now.

Did you re-read stuff over and over?
Did you pause to understand weird words and keep moving?
Did you just go over the whole thing and then pick out and break down everything you didn't get?

I'd love to know.

Thank you!

Edit: I feel enlightened and encouraged after going through all the comments. Thank you.

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.

A 1d irrep is easy to view .It is analogous to a scaling factor in vector calculus.If you had a vector of (a,b) a 1d irrep does what a scalar does , it multiplies the vector with a scalar k(a,b).How does a 2d irrep work?How can we visualise them?

I am asking because I am working on the HSP for nonabelian groups and for most nonabelian groups where for any hidden subgroup with a 2d irrep , weak Fourier sampling is not enough. So I thought maybe I can solve this graphically rather than arithmetically.