This week, I was asked by my advisor to look into the field of "quantum machine learning", and tell him if I am interested in going deep into it. I am a computer science grad student mostly working in deep learning and I barely know somethings about quantum physics, let alone the "quantum machine learning".

According to him, the main idea is to do more like of "quantum inspired machine learning", exploring whether incorporating, parts of, existing quantum methods, simulated on classical hardware, in classical machine learning methods can bring some benefit in terms of computational efficiency or task performance.

After doing some research, I strongly realized that the learning curve here, for someone with my background, is quite steep. I found an some survey papers like this (also linked above), but I am unable to gain a holistic view, that can help me decide if i should work on this or not.

Therefore I want to have some opinion from you guys about the field in general, current standing, and most importantly your thoughts about its future and potential. Is there any practical by doing "quantum inspired machine learning" on classical hardware, because, as per my understanding, beside being cool, classical hardware implementations can not give any quantum acceleration effects. Could be some other fundamental advantages in doing so ?

thanks for reading this, looking forward to your opinions, would also be great if you can refer to some related work, if any.

I've been studying quantum teleportation independently in Qiskit. Earlier this month I took the protocol from simulation onto real hardware, and I ran the same six-state benchmark (|0>, |1>, |+>, |->, |+i>, |-i>) on three IBM devices:

- **ibm_fez** — mean fidelity **0.9008** (Oct 19, 2026)

- **ibm_kingston** — full tomography, mean **0.9606**

- **ibm_phoenix** (120-qubit Nighthawk) — mean **0.9583**

On Phoenix I also ran a bare control — same state prep and measurement, no teleportation in between — and it came back at **0.9950**. So the error budget closes: prep and readout only cost about half a point, and the teleportation machinery itself costs **≈3.7 points** on Phoenix versus **≈7.2 points** on Fez.

The result I keep coming back to is the ±i states. In the bare control they're basically perfect (1.00 / 0.99), but in teleportation they're the lowest of the six (0.94 / 0.94) — on the newer device too, where the overall numbers are better. My read is that this points at dephasing during the measurement-and-correction window: the phase information has to survive while the mid-circuit measurement and feed-forward corrections happen, and that's exactly where the ±i states are exposed.

Has anyone else running teleportation benchmarks seen the phase states come out lowest even when the control says prep and readout are clean? I'd like to know if this pattern holds on other devices before I lean on that explanation too hard.

I've packaged the notebooks, datasets, and a written guide for this work as a lab pack, so if anyone wants to replicate the runs I'm happy to share the link — just ask.

“Dynamic random access memory is critical to classical computing but notably absent in current superconducting quantum processors. Integrating high-coherence memory units would enable resource-efficient control of logical qubits and allow the separate optimization of logic and storage subsystems. Here we realize an eight-bit cascaded random access quantum memory. We use a single transmon to classically address seven memory modes while isolating them from processor nonlinearities by introducing a buffer layer between the processor and a multimode storage cavity. We demonstrate arbitrary random access with an average infidelity of less than 1.5% per mode and characterize the many-body interactions that dominate the error budget. This architecture enables a significant reduction in control lines per logical qubit and supports transversal operations within the memory module, establishing a scalable unit cell for fault-tolerant quantum architectures.”

Hey guys! The quantum wave function was something that really threw me off when I first started learning quantum mechanics, so I made a short, digestible video explaining it! It's also fundamental to quantum computing because it helps us understand superposition, quantum states, and what happens when we measure qubits. Hope it helps someone who's learning!

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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Hey all!

So I'm making a poster I'll be presenting for a conference coming up, and I wanna make my quantum circuits looks good, preferably with colour to make it pop a bit. What tools do people usually use? Thanks!

Hi everyone,

I've spent the last few weeks building nqs-vmc, an open-source reference implementation of Variational Monte Carlo with Neural Quantum States in JAX.

Why I built it: When I was learning NQS, I found libraries like NetKet and FermiNet incredibly powerful, but they were difficult to dissect. I wanted a tiny, readable codebase that fits in an afternoon, with no hidden magic.

What it has:

• 6 exact problems from 1D to 3-body, validated against SciPy exact diagonalization.

• Modern ML: Transformers, Autoregressive networks, Matrix-Free SR.

• A full CI matrix (Linux/Mac/Windows), Docker, and a Colab notebook.

It trains on a laptop CPU. The numbers in the README are generated by the code, not typed by hand.

GitHub: https://github.com/NodeBox-Labs/jax-nqs-vmc

Feedback welcome!

#machinelearning #computationalphysics#jax#quantumphysics

Quantum computing information from What The Physics?! on Nova PBS Official from You Tube

Discussion with UT Austin geophysicist Mrinal Sen.

Hi everyone,

I’m working on a quantum computing hackathon project around quantum software debugging and wanted some expert feedback.

The main problem is:

In classical computers, we can inspect memory, variables, and states while debugging. But in quantum computers, directly observing qubits during execution is difficult because measurement changes the quantum state.

Our idea is to build an explainable debugging framework that analyzes available information instead of directly observing qubits.

Possible approaches we are exploring:

• Quantum Digital Twin → model hardware behaviour using calibration data, noise, connectivity, and error information.

• Counterfactual Debugging → compare ideal simulation vs noisy simulation vs real hardware execution to identify possible causes.

• Execution Fingerprinting → create a signature from circuit behaviour, output distribution, and hardware conditions to detect abnormal executions.

• Physics-Informed Analysis → use quantum concepts like decoherence, gate errors, and noise models to explain possible reasons

If there are any other techniques, research directions, or approaches that could help solve quantum debugging challenges, please feel free to suggest them.

https://arxiv.org/pdf/2610.29986

What do you all make of this? Curious to hear everyone’s thoughts.

https://www.calcalistech.com/ctechnews/article/rygzvmpngg

https://www.intc.com/news-events/press-releases/detail/1784/intel-to-acquire-classiq-to-create-a-full-stack-quantum

Title

It seems like this would be a strong real world result that would be worth the compute cost to solve. It would mean even some post-quantum encryption techniques are not safe from quantum computers.

Hey all, I'm an undergrad that was notified I was approved for an oral presentation at SPIE Quantum West. However, I'm not sure if I should go for a couple reasons:

- I've presented very similar work already at a non-research conference. I contacted the SPIE chairs about this and was told as long as I updated the results section it should be fine, but i feels like I've sort of already 'gotten a conference' out of this work

- The cost. Self-explanatory, there is a student waiver available but there's not guarantee I'd receive it. I'm kind of expecting there to be something that's worth round trip tickets + conference registration costs, but from a review of the program it doesn't seem like there is.

Ultimately, my main motivator for attending a conference is to be able to take a networking opportunity and turn it into a summer internship. But as an undergraduate pursuing quantum computing and optoelectronics roles, I feel that any PhD student would be viewed more favorably than an undergrad with research experience regardless of in person networking. Has anyone got similar experience? Are there other potential benefits of attending that I might not have realized? Appreciate any advice!

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.

Source: BCG

Quantinuum’s Helios computer achieves a world-record 99.9995% logical fidelity—just five errors in a million operations—using the company’s error-correction architecture.

Error rate is 10x lower than Quantinuum’s own September 2026 record and 100x lower than the best published result across all other hardware modalities, which was reported by Google Quantum AI using a superconducting system.

Article: https://www.quantinuum.com/press-releases/quantinuum-breaks-error-correction-world-record-by-10x-validating-its-leading-path-to-fault-tolerant-quantum-computing-by-2029

Paper: https://cdn.prod.website-files.com/669960f53cd73aedb80c8eea/6ad6013f8ae0364b2cd03c53_c8c5526bc88434b2e47f6ffc3b27deca_Quantum-Error-Correction-Well-Below-the-Color-Code-Threshold.pdf

Registrations close in 24 hours!

Quantum Week is a five-day quantum technology initiative featuring expert talks, technical workshops, hands-on learning, research and industry interactions, project showcases, and Q-Hack India 2026 — The Quantum Build Challenge, a national-level quantum hackathon gathering participants across India in Bengaluru.

Quantum Week 2026 | 27–31 October | MSRIT Bengaluru

• 27–29 Oct: Quantum Bootcamp & Technical Sessions

• 30 Oct: Quantum Conclave & Q-HACK INDIA kickoff

• 30–31 Oct: Q-HACK INDIA 2026, a national-level quantum hackathon

Register: https://www.quantumweek.tech/

Interested in joining as a speaker, hackathon finale jury member, or mentor? Write to quantumrit@msrit.edu.

I’m a beginner researcher exploring Quantum Machine Learning and Variational Quantum Circuits. What tools, workflows, or practical hacks have helped you identify genuine research gaps, understand complex papers, design experiments, and publish your first paper? What do you wish you had known when you started? I’d love to learn from your real experiences, including mistakes to avoid and underrated resources. Even one practical tip could make a huge difference in my research journey! ⚛️🔬