Hope this kind of discussion is okay here. We covered the DOE announcement on our show this week and the error correction workforce gap stood out more than the hardware target itself. Would love a reality check from people who actually work in this space.
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
“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.
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.
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
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.
For more details :
This links takes you to the more information https://srikaleeswarar.home.blog/osl-quantum-lectures/
i was listening to a biohacking podcast the other day and the host started talking about this new wave of quantum enhanced supplements. basically they claim that by treating vitamins or plant extracts with specific frequencies or light technology it changes their molecular structure and makes them a hundred times more bioavailable to your cells. it sounds wild and kind of cool on paper but the scientific explanations they gave were so vague and full of buzzwords that my scam radar immediately started going off.
i tried looking up some actual studies on it but all i found were articles from the companies themselves selling these high priced drops and powders. they are charging almost triple what a normal high quality supplement costs just because it has the word quantum slapped on the label. part of me wants to believe that there is some cool new science happening with quantum biology but another part of me feels like it is just regular old vitamins repackaged for people with too much money to spend.
Hey r/quantumcomputing and r/ArtificialIntelligence,
We’re TGDK LLC, a small team working on OliviaAI™ QuantumSDK—an open-source Python toolkit for integrating quantum computing with AI. We’re excited to share it with the community and get your feedback!
🔹 Why OliviaAI™?
Most quantum tools (Qiskit, Cirq, PennyLane) focus on pure quantum computing or research applications. OliviaAI™ is designed to bridge quantum and classical AI, making it easier to:
- Simulate quantum entanglement for distributed systems.
- Implement post-quantum encryption (e.g., quantum_tunneling_shield).
- Optimize AI models with quantum-inspired algorithms (e.g., self_optimizing_quantum_ai_kernel).
- Experiment with neuromorphic processing and hybrid quantum-classical workflows.
🔹 Key Features:
- Modular Design: QuantumCore, QuantumSecurity, QuantumComputation, QuantumStorage, and QuantumAI classes for flexible integration.
- Security-First: Built-in methods for quantum-safe encryption and data partitioning.
- AI-Optimized: Tools for quantum-enhanced machine learning (e.g., tensor flux processing, adaptive interference cancellation).
- Easy to Use: Pure Python with NumPy/SciPy dependencies—no quantum hardware required.
🔹 Open-Source License:
We’re releasing OliviaAI™ under a custom open-source license (BFE-TGDK-022ST). This allows for:
✅ Free use for research, education, and non-commercial applications.
✅ Modifications and distributions (with attribution).
❌ No commercial use without prior approval (we’re open to collaborations—reach out!).
🔹 GitHub Repository:
👉 TGDK-ST108/OliviaAI-SO
(Note: We’re still uploading the full codebase and documentation. The repo will be fully populated.)
🔹 What We’re Looking For:
- Feedback: What features would make this more useful for your work?
- Contributors: Interested in helping? We’d love PRs for bug fixes, new algorithms, or documentation.
- Use Cases: How would you use a toolkit like this? We’re especially curious about applications in finance, cryptography, or optimization.
- Collaborations: If you’re working on something similar, let’s chat!
🔹 Roadmap:
- v0.1 (Now): Core quantum-AI modules + basic encryption.
- v0.2 (Q3 2026): Advanced optimization tools + Jupyter notebook examples.
- v0.3 (Q4 2026): Plugin system for custom quantum backends (e.g., IBMQ, Rigetti).
💬 Let’s Discuss!
- What do you think of the proprietary-friendly open-source model?
- Any features you’d like to see in future releases?
- Would you use this for research, teaching, or prototyping?
We’re here to listen and improve—thanks for being part of the journey!
— The TGDK LLC Team
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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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?
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 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.
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.
Do people use GPUs for their research in quantum computing? If so what do you use it for? Error mitigation, error correction, simulating larger systems?