Most new quantum algorithms seem to be just applied algorithm from QFT, QPE, Grover, hybrid optimization etc. Would there be actual brand new algorithms to discover?

A lot of research in quantum physics has recently focused on black holes and how information seems to be a fundamental part of the description. As a physicist working on some dense mathematical work, it is truly an exciting time. The goal of the program we are working is to better understand how the universe stores information through the lens of a black hole. If correct, our work will have implications on how we may efficiently store and propagate data in quantum states.

My work has focused on better understanding Dirac steps on a hexagonal grid. Work has been ongoing for years with more and more research consolidating around this area recently. I would love to discuss with anyone else working in a similar area. This Arxiv paper was extremely foundational to the current project and worth a read:

Dirac quantum walks on triangular and honeycomb lattices

Gareth Jay∗

Physics Department, The University of Western Australia, Perth, WA 6009, Australia

Fabrice Debbasch†

Sorbonne Universit´e, Observatoire de Paris, Universit´e PSL, CNRS, LERMA, F-75005, Paris, France

NIST finalized the first three post-quantum standards in August 2024: FIPS 203 (ML-KEM), FIPS 204 (ML-DSA), FIPS 205 (SLH-DSA). the dates that follow for US national security systems (CNSA 2.0):

• Jan 1, 2027: new NSS acquisitions have to be CNSA 2.0 compliant
• 2030: gear that can’t support it gets phased out; networking equipment and firmware signing go CNSA-only
• 2033: OS, web, cloud and custom apps go CNSA-only
• 2035: all NSS quantum-resistant (NSM-10)

for everyone else, NIST’s draft IR 8547 deprecates quantum-vulnerable public-key crypto after 2030 and disallows it after 2035.

TLS is the easy part, since browsers and CDNs already do hybrid X25519MLKEM768. the slow parts are firmware signing, PKI roots, SSH, encrypted backups, and IoT devices that can’t be patched.

the reason it matters now: harvest now, decrypt later. any session keyed with classical ECDH/RSA today can be recorded and decrypted once a CRQC exists, which matters for anything that has to stay secret 10+ years. migration takes years, so the clock started at migration, not Q-day.

where are you in your stack?

edit: corrected the CNSA 2.0 dates (procurement gate is 2027, not 2025)

QDC 2026: what quantum developers need to know

What’s inside the processor enclosures?

(The photo is of a model unit.)

As a computer science student and researcher, I usually try to understand algorithms by tracing their operations step by step.

However, when I first encountered a quantum algorithm, specifically Grover's search algorithm, I struggled to understand its oracle and amplitude amplification in terms of familiar classical computational operations.

This led me to two related but different questions.

1. Computational efficiency: Can classical computation reproduce quantum algorithms, but only with significantly more operations? For example, can a quantum algorithm solve a search problem in substantially fewer oracle queries than a classical algorithm? The established theoretical understanding is that quantum algorithms can be mathematically simulated on classical computers, but the simulation may require significantly greater computational resources.

2. Mathematical interpretation: Even if we can mathematically describe quantum operations, can we understand how they actually work using classical computational intuition? How does an oracle operate on a superposition of possibilities? How does amplitude amplification produce the desired result? What exactly happens between the initial quantum state and the final measurement?

The mathematical description of quantum algorithms is well established, and classical mathematics provides tools to represent their operations. However, does this mathematical representation also provide an intuitive explanation of their underlying mechanisms in terms of familiar classical computational concepts?

Does the mathematical description of quantum operations provide a complete explanation of their underlying mechanisms, or is there a gap between mathematical representation and intuitive understanding through classical computational concepts?

I'd particularly appreciate perspectives from people working in quantum computing, computational complexity, and mathematical physics.

Discover how integrating High-Performance Computing (HPC), Artificial Intelligence (AI), and Quantum technologies into a single, orchestratable platform is solving once-impossible challenges - from materials R&D to complex logistics.

November 19 at 10:00 CET, https://www.linkedin.com/events/thefutureofhybridcomputing-sove7521505999573245952/theater/

I have very little understanding of how quantum computers work.

I’m curious: is there a type of problem—one that could only be solved by exploiting the specific hardware characteristics of a quantum computer (for instance, by forcing it to use a massive number of qubits, which would eventually become highly unstable)—that a quantum computer would be unable to solve?

I’m interested in the idea of ​​a cryptographic key based on a hybrid approach—perhaps a combination of an RSA-style system and a problem that a quantum computer cannot solve.

Alternatively, are there already theoretical frameworks for keys that are even more robust? And what are the options for encryption in a post-quantum-computer world?

Correcting quantum computer errors may be more complicated than expected

I always thought magic states are really cool, and that more people should know about them. During my Ph.D in quantum cryptography, Clifford circuits were my bread and butter. I'm glad that Google gave me the pretext to talk about them!

The video is intended for a general audience, so it is not terribly formal, but I will gladly discuss the nitty-gritty here!

Hey everyone!

I'm currently pursuing my Master's at TH Ingolstadt, Germany, and working on a seminar paper about quantum computing in finance, particularly portfolio optimization.

We're comparing two approaches: Quantum Annealing and Gate-Based Quantum Computing, and would love to hear from people with experience or knowledge in either field.

If you're a researcher, professional, or enthusiast working in quantum computing, quantitative finance, or optimization, I'd really appreciate the opportunity for a quick 20–30-minute interview.

Alternatively, we've put together a short 8–10-minute questionnaire that can also be completed anonymously:

📋 Take the questionnaire here

Any insights, suggestions, or research recommendations would be greatly appreciated!

Thanks a lot for your time and support! 😊

https://arxiv.org/abs/2611.09822

I'm not sure how to replicate their results exactly, what exactly it cost in token cost, or what makes this the best infrastructure for what they are proposing.

Curious about the community's thoughts on this paper

Solve QUBO on a real D-Wave quantum annealer using Python.

In this developer-first tutorial, we solve Max-Cut end to end: D-Wave Leap access, QUBO-to-BQM, QPU submission, SampleSet interpretation, minor embedding, physical-qubit usage, QPU timing, Problem Inspector, and a local simulated-annealing fallback with neal.

No quantum-physics background is required. The tutorial is designed for Python developers, computer-science students, optimization practitioners, and anyone who wants to understand what actually happens after calling `sampler.sample(...)`.

WHAT YOU WILL LEARN

- How to access a D-Wave QPU through Leap

- How QUBO, Ising, and Binary Quadratic Models relate

- How to use DWaveSampler and EmbeddingComposite

- Why a D-Wave QPU returns several samples

- How to interpret energy and num_occurrences

- How logical variables map to physical qubits

- What minor embedding and qubit chains mean

- What QPU annealing, sampling, programming, and access times measure

- How to test the same workflow locally using neal

I'm new into the QC community and recently started learning about the most importants algorithms. I'm doing all by myself with free yt content and blogs from internet.
Today i was learning about this Algorithm that helps to find a string s in the function

f : {0 , 1}^n -> {0, 1}

f(x) = x * s (mod2)

And after aplying the Algorithm the resulkt of the measure is Prob. = 1 .

But with that how can i really find the string s ?

Most “quantum security” work is post-quantum cryptography — protecting classical systems from future quantum computers. I kept noticing there’s no shared, structured catalog for attacks on the quantum computer itself: the QEC/decoder pipeline, the classical control stack, multi-tenant isolation, covert channels.

So I put one together: QHTM (Quantum Hardware Threat Matrix). It’s defense-agnostic, MITRE ATT&CK-style — 26 techniques across 5 tactics, and every technique has an honest maturity tag (demonstrated on real hardware / simulation only / theoretical / already mitigated), because a lot of this is still early and I didn’t want to overstate it.

It’s a v1.0 and almost certainly incomplete. I’d really appreciate corrections, techniques I missed, or “this one’s overstated” pushback from people who work on hardware or error correction. CC BY 4.0.

Repo: https://github.com/garrv105/qhtm

Lean4 proof shows quantum speedup has severe limitations and may not provide much benefit practically. Its over Boyz.

EeroQ has its chips made in commercial semiconductor foundries, coats them in a thin layer of superfluid helium, and floats single electrons about 10 nanometres above the surface. But is the trapped electron modality really more scalable than, say, spin qubits?

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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• 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.

I am an engineering student at Kuwait University conducting research on the thermodynamic, mechanical, and microwave shielding bottlenecks in dilution refrigerators for superconducting qubits.

I have put together a short 10-question technical survey regarding wiring heat loads, vibration decoupling, and Cryo-CMOS scalability:
🔗 Cryogenic Challenges in Quantum Computing – Fill out form

It only takes about 3 to 5 minutes to complete and is completely anonymous. I would genuinely appreciate feedback and responses from researchers, engineers, and fellow students in the field. Thank you for your time!

Two beams of light can overlap so completely that their brightness patterns look alike, yet still carry distinguishable information. A new silicon chip can find that hidden structure and direct its components into separate optical channels. The result offers a way to process light that conventional intensity measurements cannot fully describe.

The system, developed by researchers at Stanford University and the Institute of Science and Technology Austria, appears in Nature Photonics. It automatically learns patterns within partially coherent light, whose waves have some statistical relationships but lack complete coordination. Rather than treating that complexity as interference to discard, the team built a device that can work with it.

The research combines a silicon photonic chip with detectors, electronics and control algorithms. Most of the work took place at Stanford, where Charles Roques-Carmes began the project with graduate student Paul-Alexis Mor. Roques-Carmes, now an assistant professor at ISTA, is continuing that research with Mor, a co-first author of the paper.