News item covering scientific criticism of Microsoft's claimed topological qubit result (the Majorana-based device announced in early 2025). Reported objections centered on whether the measurements presented actually demonstrate Majorana zero modes rather than alternative explanations. No abstract or technical detail accompanies the item.
Why it matters: Topological qubit claims remain contested, so teams tracking Microsoft's roadmap should treat the Majorana milestone as unresolved pending independent replication.
Hardware: spin & topologicalIndustry, funding & policyoverview
A Hacker News discussion item pointing to continued skepticism about Microsoft's claimed topological qubit (Majorana-based) results, arguing that the published evidence does not establish the existence of the claimed topological qubits. No new experimental data is presented; the piece is commentary on the state of the evidence.
Why it matters: Topological qubits are Microsoft's central hardware bet, so unresolved doubts about the underlying Majorana signatures affect how much weight to give that roadmap.
Hardware: spin & topologicalIndustry, funding & policyoverview
Pasqal released the Quantum Evolution Kernel, an open-source Python library implementing a graph machine learning kernel based on quantum evolution dynamics on neutral-atom registers. It runs on emulators as well as hardware, and ships a tutorial applying the kernel to molecular toxicity prediction.
Why it matters: It gives engineers a concrete, runnable entry point into neutral-atom quantum graph ML without hardware access, though it is an early-stage library with no benchmark claims against classical graph kernels.
Quantum machine learningSoftware & toolingHardware: neutral atomapplied
Original abstract
Hi HN,<p>We're proud to announce the release of the Quantum Evolution Kernel! It's still an early version, so feedback is very much welcome.<p># What is it?<p>Quantum evolution kernel is an open-source library designed for anyone interested in applying quantum computing to graph machine learning -- and you don’t even need a quantum computer in your living room to start using it! It has a wide range of graph machine learning applications, including prediction of molecular toxicity, as shown in the tutorial.<p># Why is it exciting?<p>Quantum computing has huge potential, but it needs to be accessible and practical to make a real impact. This library is a step toward building a quantum tools ecosystem that researchers, developers, and innovators can start using today.<p># What's next?<p>We're working with researchers to turn more quantum research papers into usable open-source libraries. We hope to build an open ecosystem where developers, researchers, and enthusiasts can experiment, contribute, and shape the future of quantum computing together.<p># Links<p>- Code: <a href="https://github.com/pasqal-io/quantum-evolution-kernel" rel="nofollow">https://github.com/pasqal-io/quantum-evolution-kernel</a><p>- Doc: <a href="https://pasqal-io.github.io/quantum-evolution-kernel/latest/" rel="nofollow">https://pasqal-io.github.io/quantum-evolution-kernel/latest/</a><p>- Our community portal & slack: <a href="https://community.pasqal.com/" rel="nofollow">https://community.pasqal.com/</a>