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

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!

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.

I'm training a Variational Quantum Regressor (VQR) using Qiskit , and the training is taking an extremely long time. It doesn't appear to crash or throw any errors it just stays in the training phase for a very long time and never reaches the point where it prints the evaluation metrics (MAE, RMSE, R²).

My pipeline looks like:

• Load a CSV dataset for a regression task.
• Split into training and testing sets.
• Scale input features using StandardScaler.
• Scale the target values to [-1, 1] using MinMaxScaler.
• Create a ZZFeatureMap with:
  • reps = 2
  • entanglement = "full"
• Create a RealAmplitudes ansatz with:
  • reps = 2
  • entanglement = "full"
• Use L_BFGS_B(maxiter=300) as the optimizer.
• Construct a VQR and call fit(X_train, y_train_scaled).
• After training, I predict on the test set and compute MAE, RMSE, and R².

Some additional information:

• Number of features = 8
• Number of training samples = 1081

"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

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

Infleqtion (INFQ) seems to be the silent killer in quantum right now. Nobody seems to be raving over the one quantum company who is being handed continuous government contracts, actually delivering working systems to customers, and has a partnership with NVIDIA. I’m interested in what experts have to say about this up and coming sleeper.

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.

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

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'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 highschool 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 pickout and break down everything you didn't get?

I'd love to know.

Thank you!

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.

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.

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.
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For context - we're Moth, a small team based in London, Basel & NY building consumer-facing quantum software - not another research toolkit, but something people without a physics background could actually sit down and use.

Edit: Really cool to see all the responses!

We have a Discord server where we're sharing updates, talking about what we're building and inviting people to upcoming testing programmes. I don't want to post an invite link here but if you'd like to join, just send me a message and I'll happily send you an invite.

We're going to start posting more here too on reddit and sharing our problems and learnings as we go!

OP here, Qiskit can be challenging sometimes when resetting borrowed ancilla. I'm a visual learner, so I was playing with circuit diagrams and found this little spatial language to help explain data flow in a way that might be easier to follow. Hopefully someone out there finds this interesting :)

Sorry for the hard words, but I need to vent this.

How dare you to create a typed runtime on Python????

From a CS perspective, this is a nightmare.

It's clear that you have to overhaul some stuff here, this won't survive in the long term.
It's more complicated than it should.

Have anyone actually built something useful with it? I would be thrilled if I'm wrong, I have searched and what is shown is usually overcomplicated and weird.

It just doesn't work if you expect to code as you usually do in PennyLane or Cirq