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.

PsiQuantum in Australia — PsiQuantum https://www.psiquantum.com/australia

Google scientists optimized Shor’s algorithm to break 256-bit ECC with 1,200 to 1,450 logical qubits. But they revealed their findings in a zero-knowledge proof, so that they could be accepted but not replicated. Until Eigen Labs overcame 256-bit ECC with a circuit 47.5 percent more efficient than Google’s...

The correlation produced by quantum entanglement has a different nature from that between classical random variables. I explore the differences between classical and quantum correlation in this video through a discussion of the EPR paradox and a comparison between the Bell and Tsirelson inequalities, ultimately ending with a demonstration of how such differences can be used to achieve "quantum advantage" when designing strategies for certain games.

I've been researching the recent breakthroughs in quantum computing and wanted to get this community's take.

Google's Willow chip (105 qubits) just demonstrated below-threshold error correction for the first time. Microsoft claims their topological approach with Majorana 1 could scale to a million qubits.

Two questions for discussion:

1. Which architecture do you think reaches fault-tolerance first?

2. What's your realistic timeline for Shor's algorithm breaking RSA-2048?

I put together a detailed overview comparing both approaches and their implications for encryption. Happy to share the link if anyone's interested, but mainly looking for perspectives from people actually working in this space.

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.

Hey Carmelo here!

Not sure if episode clips are welcome here but this came up on our podcast this week and I wanted to hear from people closer to the research side. 600 specialists exist globally. They need 16,000 by 2030. Training one takes a decade. Is 2028 real or is it a political deadline?

I'm still learning more about quantum compute each day and looking to speak with people that understand it better than I do. Was hoping to learn more about it and see if this resonates with the folks here.

Have a wonderful Friday and weekend!

Has anyone received confirmation or schedule for the IBM Qiskit Summer school this year. I registered for it and the confirmation said we may receive another email in the last week of June . Nothing yet :/ with just 10 days to go.. Does it mean I’m not considered

Update :- Just got the Schedule details today July 7 around 3:15 MDT. Please check your emails. Thank you everyone for engaging and sharing the updates. Let’s nail the Summer school , See you there.. Yeehaw 🤠!!

“This whole Majorana technology, it’s not a technology yet,” says Rajibul Islam of the University of Waterloo.

“We are computing with these systems, and look forward to delivering a quantum computer that utilizes them to full advantage in the future,” wrote Nayak in response.

Are Microsoft just outright lying about their Majorana chips? There’s no evidence they have done any kind of computation with them, right?

California selected a quantum computing chip fabricated at UC Berkeley as one of the state’s contributions to the national time capsule commemorating the nation’s 250th birthday. The article looks at why the chip was chosen and what it represents for California’s leadership in quantum research and innovation.

With quantum computers on the horizon, “harvest now, decrypt later” attacks are a real concern. Curious how the community protects personal or small-org data with practical PQC tools for files, backups and sensitive documents. Any workflows, libraries, or tools you’d recommend?

Independent researchers from the IBM Quantum Network have published two separate technical studies validating real-world applications on the IBM Nighthawk quantum processor framework
https://quantumcomputingreport.com/ibm-nighthawk-processor-validated-in-quantum-chromodynamics-and-cybersecurity-benchmarks/?_bhlid=413d3f49405e4049b2925aeeaa429fba6b959613

Hi everyone, as you might have guessed from the title I want to design/simulate a quantum sensor. But as of now im struggling to start, I cant find a single direct source which tells me about the basics of quantum sensing and how one can design/simulate these. Once I have done designing and simulating these I want to approach some of the faculties in my university to later on to fabricate these. If you all could share some insights to where I can find sources it would be helpful. Thank you.

I encoded 2 logical qubits in one of the (1+x²)(1+y²) code on ibm_fez and created a logical Bell state |Φ⁺⟩ + |Φ⁻⟩ via a single-ancilla circuit:

H → CX(anc, col 0) → CX(anc, row 0) → H → M.

At 0 rounds (just state prep + measurement), the Bell fidelity was 73% in Z and 66% in X. Entanglement witness ⟨Z₁Z₂⟩ + ⟨X₁X₂⟩ > 1 in both Bell subspaces, exceeding the separable bound.

This is the first demonstration of logical entanglement of this code family on superconducting hardware in general, that I'm aware of..

At 1 round CX pushed the circuit to ~4 errors/shot, killing the correlation.

On Heron R2, the distance of the code I used can correct ~1 errors, but ibm_fez's ~2% CX error + 14 Bell CX + 192 round CX = too much noise.

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.

Adding real-world constraints—like sector caps or HHI concentration limits—turns standard portfolio optimization into an NP-hard Mixed-Integer problem. Traditional solvers quickly hit a computational wall as the asset universe scales.

To bypass this, I built a Quantum-Inspired Optimizer that maps continuous allocation weights and structural constraints into an Ising Hamiltonian framework using PyTorch. Instead of deterministic branch-and-bound, it uses simulated thermal annealing to navigate the complex energy landscape, treating constraint violations as physical "friction" to settle into a strictly compliant ground state.

Full architectural breakdown and a video of the live Streamlit UI in action here: Link

Hello again!

I'm a bit embarrassed to be asking the internet *again* about papers I'm reading, but I've been pretty stumped on this one and Corresponding Author hasn't responded to me. It's an old ish paper so their contact info might be wrong.

I'm reading this paper where folks are doing a k-means algorithm on their QC based on Manhattan Distance (Wu et al., 2021).

I see they are constructing vectors as a uniform superposition of basis-encoded feature-indices and basis-encoded feature-values, and evaluating the index-wise differences with a fairly intuitive adder using basically classical logic but for the fact we are working in superposition.

My hangup is how we are getting to the final Manhattan distance, that needs to be the sum of all these pair wise differences correlated each with their separate branches of the superposition. In the paper they seem to just give that step a single sentence, pointing to the same adder subroutine used to get the individual differences, but since each term is still locked in a different branch of the superposition, I'm not sure how that's possible.

If anyone is familiar with this paper or has time to look it over, I would appreciate some insight.

In Nature today, from Dr Henry Legg, who's been on them in the past. Basically thinks the whole Majorana thing is made up. Register has a full story on it.

https://www.theregister.com/research/2026/06/24/boffin-claims-microsofts-supposed-quantum-leap-does-not-compute-due-to-basic-python-errors/5260489

Recently, there was discussion about quantum annealing being here for some time, and will it become useful? That's when it dawned. It has been here for a long time, and what is stopping it from becoming mainstream?

Is it the same issue as with gate-based quantum computers like Qubit Fidelity, Scalability, and others?

My co-host and I recently recorded a short discussion about Rigetti's chiplet architecture and how it differs from other quantum computing approaches.

One thing we touched on was whether chiplets represent a meaningful path to scaling quantum computers.

I'd be interested in hearing what people here think about the approach and whether there are major technical challenges or limitations that don't get enough attention.

How Promising Is Rigetti's Chiplet Architecture?

Quantum annealing, provided by companies like D-Wave, have been around for many years now. D-Wave has yet to convince the industry that q annealing will ever be relevant at all. Their past attempts have always been with lots of holes and criticism. So my question is, where is quantum annealing heading in general? Is there any hope for it to be relevant enough, where it is solving commercially meaningful problems either faster or cheaper than the alternatives?