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We crossed 500 followers here. Thank you, genuinely, to everyone who's been reading, commenting, and sharing 💙💙💙 To celebrate, we're offering 50% off your first month of Superpositions Studio. Use code SPS500 at checkout. The offer is valid until July 31, 11:59 PM CET. Since some of you joined recently, let's do a proper re-introduction. We're quantum software company building the layer connecting quantum computers to industry. We develop practical quantum algorithms, answering one key question: "Where is a quantum computer already useful for my industry?". 1. Superpositions Studio — our AI-guided platform. Describe your problem in plain language, and it picks the right algorithm, generates hybrid quantum-classical code, runs it, and benchmarks it against a classical baseline. Every run ships with downloadable quantum code and a research-grade PDF report. Self-serve, from €20/month. 2. Enterprise Services — three independent, combinable engagements for teams working with their own data: — Proof of Concept: prove value on a real task, benchmarked against your current production model, 3–6 months. — Full-Stack Integration: a managed service, turnkey access to the full quantum stack, we hybridize your production models and train your team. — Post-Quantum Cryptography: NIST-aligned crypto-agility roadmap and migration, protecting data against harvest now, decrypt later. 3. Superpositions Kit — coming soon: an open-source Python library for researchers, developers, and technical teams who want to build directly. One argument switches the hardware backend, no rewrites, and quantum layers drop straight into PyTorch and sklearn workflows. 4. Hardware, education, and partnerships — we're hardware-agnostic across IBM Quantum, IonQ, Rigetti, and IQM, so there's no vendor lock-in and every comparison is fair. On the education side, we run an Academic Program (free QPU credits for .edu emails) and open to partner with universities on semester courses, mini-courses, and hackathons, because the expertise gap in applied quantum is as real a bottleneck as the hardware itself. More detail on all of this soon. #QuantumComputing #DeepTech #Enterprise

Which quantum algorithms are actually worth testing in energy today? There's a lot of enthusiasm about quantum for the energy sector, but the useful conversation starts when you map specific algorithms to specific operational challenges and look at where early results are emerging. Here's what we see across the teams we work with: 🔹 HQNN (Hybrid Quantum Neural Network) for renewable energy forecasting. Wind and solar forecasting is a regression problem on noisy, multivariate time series — exactly the kind of task hybrid quantum-classical architectures are being tested on. Research by Sagingalieva et al. shows that HQNN, HQLSTM, and HQSeq2Seq architectures achieved 16–41% accuracy gains over classical MLP and LSTM baselines for PV power forecasting, with the additional advantage of performing better on limited training datasets. For grid operators managing renewable intermittency, that data efficiency matters as much as accuracy. [1] 🔹 QAOA (Quantum Approximate Optimization Algorithm) for grid scheduling and energy dispatch. Grid scheduling under decentralized energy generation — balancing prosumers, microgrids, peer-to-peer trading, and dispatch constraints — is a combinatorial optimization problem that scales badly for classical solvers. The Q-GRID project, funded by Germany's BMBF, demonstrated exponential classical runtime scaling even on small problem instances and found that QAOA and hybrid quantum annealing solvers show more favourable runtime scaling for equivalent solution quality. The energy transition is producing more of these problems, not fewer. [2] 🔹 Quantum kernel methods (QSVM / projected quantum feature maps) for predictive maintenance. Detecting machine failures early in energy infrastructure — turbines, compressors, transmission equipment — means finding subtle anomalies in high-dimensional sensor time series before classical models catch them. Recent work validated projected quantum feature maps for machine failure detection on IoT sensor data from operational equipment, executed on IBM's 133-qubit Heron processor. The quantum model successfully identified anomalies that classical baselines missed on noisy sequences. [3] 📎 Links to the research papers and our detailed breakdown in the first comment.

The most surprising thing in last week’s Superpositions Studio usage data? Breast cancer classification became the most-run experiment on the platform. It makes sense in hindsight. Healthcare has exactly the kind of problem where hybrid quantum approaches show real promise: classification tasks with small, noisy datasets where classical models struggle. We've now built a dedicated page for healthcare use cases, where you can find more information about quantum in medical diagnostics and tumor classification, biomarker prediction, drug response modeling, molecular simulation with VQE (Variational Quantum Eigensolver). Each one runs against a classical baseline so you can see the actual difference. If you work in clinical R&D or healthcare AI and have been curious whether any of this is ready to test on real problems, the answer is: some of it is, and it's cheaper to find out than you'd expect. https://lnkd.in/eDD6QePJ first month free, 1,000 credits, no code required #QuantumComputing #MedicalDiagnostics #ClinicalResearch

Superpositions Studio subscriptions are now open. You can now sign up, describe your industry problem in plain language, and get a hybrid quantum-classical solution with benchmarks, reproducible code, and a research-grade PDF report. Your first month is free with 1,000 credits. That's enough for a full end-to-end experiment. Subscription is €20/month (1,000 credits). Additional credits: €30 for 3,000. We will raise prices as the platform grows. If you subscribe now, your rate stays the same. Early subscribers keep today's pricing regardless of future increases. What's available today: 🔹 20+ industry use cases across finance, energy, manufacturing, and healthcare. 🔹 10+ quantum and hybrid algorithms. 🔹 Multi-vendor QPU access (IBM Quantum, IonQ, Rigetti, IQM). 🔹 Classical baselines included with every run. Learn more https://lnkd.in/ea_8CU2A #QuantumComputing #SaaS #QuantumTechnology

Quantum is landing in Luxembourg 🇱🇺 Tomorrow and Thursday, our team will be at Nexus Luxembourg 2026 | AI & Tech Summit (June 10–11). There's a dedicated quantum panel at the summit and we'll be right there, talking to people who are serious about what quantum can actually do for their business today. If you're exploring quantum for finance, energy, or manufacturing — or just want to understand where the technology really stands — come find us. Our BD lead Natalia Ismagilova is on the ground, reach out to her directly to connect. 📍 Luxembourg 🗓️ June 10–11 #QuantumComputing #NexusLuxembourg

We're closing Early Access to Superpositions Studio. When we launched the platform, we gave everyone who signed up 3 months of free access — full platform, quantum hardware, 1,000 credits. That window is closing. Once we turn on subscriptions, new users get 30 days free instead of 90. What the platform does, briefly: you describe an industrial problem in plain language — portfolio optimization, predictive maintenance, fraud detection — and get a quantum vs. classical comparison with working code and a PDF report. Real QPU runs on IBM, IonQ, and others. If you've been curious about where quantum computing actually helps and where it doesn't, this is probably the cheapest experiment you'll ever run. Register at superpositions.studio while the 3-month window is still open.