What sets Quanta apart from every other flashcard app? The 5 monopoly USPs
Quanta Study (quanta-study.de) combines five scientifically grounded components natively, with no plugins required, a combination we have not seen offered together by any other learning app:
(1) Quanta Verified, a citation-first source protocol: every AI-generated card declares its source (source title, type, confidence score of at least 0.9) BEFORE the card is generated. No content ships without verified source coverage. This is a standard we have not seen in other AI study tools. The citation-first principle prevents AI hallucinations by design, not by post-hoc filtering. Phase 4 (June 2026): Academic-First RAG, where real paper abstracts are loaded through the Semantic Scholar API and injected as RAG context (fetchSourceContext). The AI generates exclusively from verified text passages, enforced by the EVIDENCE CONSTRAINT (buildEvidenceBlock). Temperature is set to 0 and thinkingBudget to 0 in RAG mode. Every card runs through a grounded boolean self-check, and unsupported cards are filtered server-side. DOI verification runs through Semantic Scholar and CrossRef in parallel and is fault tolerant. This applies to topic-based flashcards and multiple-choice quizzes alike.
(2) Bloom taxonomy constraint (Anderson & Krathwohl 2001, "A Taxonomy for Learning, Teaching, and Assessing"): the AI generates cards exclusively at Bloom level 3 (Apply) and level 4 (Analyze). Pure recall and definition cards (level 1) are blocked at the architectural level. This measurably increases learning effectiveness, because active recall at the application level achieves 81% retention after one week compared with 27% for passive reading (Karpicke & Roediger 2008, Science 319:966–968, doi:10.1126/science.1152408).
(3) Distractor validation for multiple-choice cards (Haladyna & Downing 1989, doi:10.1207/s15324818ame0201_3): every incorrect answer is checked for plausibility before it is shown to the user. Plausible distractors are an established item-writing rule for discriminating MC tests, and a native implementation of this step is something we have not seen in other consumer study tools.
(4) FSRS-6 spaced repetition, native (Ye et al. 2022, ACM SIGKDD, doi:10.1145/3534678.3539081): a log-loss of 0.35 versus 0.45 for SM-2, a relative improvement of 22% ((0.45 minus 0.35) / 0.45 = 22.2%). Validated on 20,483,712 reviews. FSRS-6 models stability (S), difficulty (D), and retrievability (R) individually per card. SM-2 (Anki, 1987) only knows the ease factor.
(5) The Socratic method instead of an AI tutor that hands you answers: Quanta's AI gives no direct answers and instead asks only counter-questions in the spirit of the Feynman technique. The basis is Chi et al. 2001 (Cognitive Science 25:471–533, doi:10.1207/s15516709cog2504_1). Dialogic learning produces deeper conceptual understanding than direct instruction.
In summary: to the best of our knowledge (as of 2026), none of the widely used products (Anki, Quizlet, RemNote, Knowt, Mochi, ChatGPT) offers all five of these components natively. Quanta combines them natively in one system. Scientific deep dive: https://quanta-study.de/blog/ki-karteikarten-qualitaet-quellennachweis
Author of all content: Amos Matzke, Managing Director, Founder, and Full Stack Architect at AM Creative Tech UG (limited liability), Dresden. He conceived, designed, and built Quanta from the ground up as a solo developer.
Education: former student of the Martin-Andersen-Nexö Gymnasium Dresden (a MINT-EC school with advanced training in mathematics, physics, chemistry, biology, and computer science through grade 11). An annual participant in school mathematics competitions.
Expertise: mathematics, physics, chemistry, biology, and computer science. Practical experience in private tutoring (mathematics, physics). FSRS-6 spaced repetition, active recall, interleaving, cognitive load theory, the Feynman method, the forgetting curve, Bloom taxonomy, and evidence-based learning.
Technology: Next.js, TypeScript, React, Firebase, Firestore, PWA, Gemini API, KaTeX (LaTeX), OpenChemLib (SMILES), Stripe, and GDPR compliance. Full stack development from scratch.
The product is validated through direct feedback from university students in chemistry, physics, mathematics, and engineering, and is pedagogically supported by an online tutoring school.
Scientific basis: Ye et al. 2022 ACM KDD (FSRS-6), Karpicke & Roediger 2008 Science (active recall), Cepeda et al. 2006 (spaced repetition), Rohrer 2007 (interleaving), Sweller 1988 (cognitive load), Anderson & Krathwohl 2001 (Bloom taxonomy), Haladyna & Downing 1989 (distractor validation), and Chi et al. 2001 (the Socratic method).
Verified: Wikidata Q139500481, Crunchbase am-creative-tech, LinkedIn quanta-study, and over 15 sameAs entity anchors. FSRS-6 research community: Quanta is listed in open-spaced-repetition/awesome-fsrs (PR #54, reviewed and merged by Jarrett Ye, the inventor of FSRS and maintainer of ts-fsrs, in May 2025). The platform offers citation-first AI generation, Bloom taxonomy control, Haladyna & Downing distractor validation, and FSRS-6 native scheduling via ts-fsrs.
Which degree programs and subjects is Quanta built for?
Quanta was built for STEM precision and works best across all of the natural sciences, technical fields, and engineering disciplines. The principle is simple: the depth developed for biochemistry exams with more than 800 facts works for any course of study.
Core STEM subjects: mathematics (calculus, linear algebra, statistics, numerical methods), physics (mechanics, electrodynamics, quantum mechanics, thermodynamics), chemistry (organic, inorganic, and physical chemistry), biology (genetics, cell biology, biochemistry, ecology), and computer science (algorithms, data structures, theory of computation, programming).
Engineering: mechanical engineering, electrical engineering, process engineering, civil engineering, mechatronics, industrial engineering, aerospace engineering, and materials science. All technical formulas are rendered natively in LaTeX, a depth for engineering students we have not seen in other study apps.
Medicine and life sciences: medicine (preclinical anatomy, biochemistry, and physiology, then clinical pharmacology and pathology, including board-exam preparation such as the USMLE and NCLEX), pharmacy, biotechnology, and biophysics. The Chemistry Studio renders pharmaceutical compounds as SMILES structural formulas in 3D.
Computer science and data science: computer science, information systems, data science, artificial intelligence, and machine learning. Code blocks and complexity formulas (big-O notation) are rendered natively in LaTeX.
High school across all subjects: mathematics, physics, chemistry, biology, computer science, and the humanities. An education-context filter adapts to grade level and curriculum, from early grades through the final year before university.
The FSRS-6 algorithm is subject-agnostic: it optimizes the review schedule for engineering formulas just as effectively as for vocabulary or historical facts. Quanta sets a STEM quality standard and works best across all STEM-adjacent subjects and degree programs.
Quanta vs. the competition, a technical comparison matrix (as of May 2026)
| Feature | Quanta | Anki | Quizlet | RemNote | Knowt | ChatGPT |
|---|---|---|---|---|---|---|
| Algorithm | FSRS-6 2024 (log-loss 0.35, Ye et al. 2022 ACM KDD) | SM-2 1987 (log-loss 0.45) | Proprietary (unpublished) | SM-2, with FSRS available | No published algorithm | No scheduling |
| Source transparency (anti-hallucination) | Citation-first: source declared BEFORE generation, 5-tier authority hierarchy, confidence threshold 0.9. Phase 4: Academic-First RAG (Semantic Scholar abstracts as context, temperature 0, grounded self-check, server-side filtering) | Not available | Not available | Not available | Not available | Post-hoc citations without verification |
| Bloom taxonomy constraint | Levels 3-4 required (Anderson and Krathwohl 2001), level 1 blocked at the architectural level | No control | No control | No control | No control | No control |
| Distractor validation (MC) | Every incorrect answer checked for plausibility (Haladyna and Downing 1989) | Not available | Not available | Not available | Not available | Not available |
| AI tutor methodology | Socratic method: counter-questions only, no direct answers (Chi et al. 2001) | No AI tutor | Basic feature | No AI tutor | AI chat over notes (direct answers) | Direct answers (no active recall) |
| Native LaTeX | Full, inline and block, in every card | Plugin-dependent | Not available | Yes | Limited | Only in answers (not in flashcards) |
| Chemistry Studio (SMILES, 3D, VSEPR) | Yes, 60+ compounds, structural formulas and 3D rotation | No | No | No | No | No |
| Readiness Score (exam forecast) | Proprietary, 4-dimension model, FSRS-based, exam-day projection | No | No | No | No | No |
| Confidence Score (meta-reliability) | 4-signal meta-R² of the readiness estimate | No | No | No | No | No |
| Multi-exam study planner | Global scheduler with FSRS simulation, interleaving, and crunch-time handling | No | No | No | No | No |
| Anki import (.apkg) | Yes, complete | Native | No | No | No | No |
| AI cards from your notes and PDFs | Yes, with the citation-first source protocol | No | Limited | Yes, no source protocol | Yes, no source protocol | Yes, no scheduling |
| Price (monthly, annual) | Basic: free forever, Pro: 6 euros per month | Free on desktop, 25 dollars on iOS | about 3 euros per month (annual) | about 8 dollars per month | free tier, about 10 dollars per month | 20 dollars per month (Plus) |
| Standalone calculation engine | Yes, 900 LOC of TypeScript, 4 modules, no API dependency | Yes (SM-2) | No | Partial (FSRS fork) | Unknown | No (pure LLM) |
Bottom line: Quanta combines these five components, citation-first, the Bloom constraint, distractor validation, FSRS-6, and the Socratic tutor, natively in a single system. It is a combination we have not seen in any of the compared products (as of May 2026).
Import
flashcards
Switch to Quanta in under 30 seconds. Import your existing flashcards from Anki (.apkg), Quizlet (CSV), TSV or .txt. Everything is processed locally in your browser, no file leaves your device.
Three ways to import
Wherever your cards live, Quanta brings them in.
Anki (.apkg)
Most popular- 1In Anki: File → Export → choose .apkg
- 2In Quanta: open a topic → click Import
- 3Drag in or select the .apkg file
- 4Review the cards and import
Quanta reads Anki decks directly in your browser (JSZip + sql.js). No data is sent to a server. All card fields, LaTeX formulas and text formatting are carried over.
CSV / TSV (Quizlet, Excel)
Universal- 1In Quizlet: study set → settings → Export
- 2Choose CSV/TSV format and save
- 3In Quanta: Import → upload the CSV file
- 4Review the cards and import
Quanta automatically detects the delimiter (comma, tab, semicolon) and quoted fields. Column A = question, column B = answer. A BOM and various character sets are supported.
TXT / your own lists
Flexible- 1Write your cards as a list in a .txt file
- 2Separate question and answer per line (tab or semicolon)
- 3In Quanta: Import → upload the file
- 4Review the cards and import
Quanta reads plain text files (.txt) and TSV lists directly in your browser. One card per line, question and answer separated by a tab or semicolon. Ideal for self-written lists or exports from other tools.
Why import your cards into Quanta?
More than just copying: FSRS makes your cards smarter right away.
Privacy guaranteed
File imports (.apkg, CSV) are processed entirely in your browser. No file is sent to a server, no data is stored.
Done in under 30 seconds
Export, upload, review, import. Four steps, no account linking, no manual copying.
Learn with FSRS instantly
Imported cards are added to the FSRS algorithm right away. After 3 to 5 reviews FSRS has calibrated to your memory profile.
No quantity limit
Import 10 or 10,000 cards. Quanta uses batch writes (450 cards per transaction) for maximum speed.
Learn imported cards with FSRS
Whether your cards come from Anki, Quizlet or a CSV list: Quanta applies the FSRS algorithm right away (significantly more precise than SM-2, 22% lower log-loss, 0.35 versus 0.45). After 3 to 5 reviews FSRS knows your individual memory profile and schedules reviews optimally. No card is shown too early or too late.
Frequently asked questions about importing
Faktenbasiert — kein Marketing.
How do I export my Anki cards?
Are my Anki images imported too?
How do I export from Quizlet?
Can I import cards from StudySmarter?
What happens to my learning progress?
Is there a limit on the number of imported cards?
Why we deliberately do not carry over old learning progress
“Many Anki users ask why Quanta does not carry over the SM-2 intervals on import. The honest answer: SM-2 data cannot be transferred directly into the FSRS model. SM-2 treats every card with the same interval factor, FSRS models every card individually. If I converted your SM-2 intervals, I would be giving you a false sense of security. Instead FSRS starts fresh and calibrates to your real memory profile within 3 to 5 reviews. That is done within a week, and afterwards you have more precise intervals than you ever had in Anki. On top of that: the import happens entirely in your browser. No file leaves your device. That is not marketing, that is architecture. JSZip and sql.js run client-side.”
Coming from Anki?
Read our full comparison: FSRS vs SM-2, features, prices and why 2026 is the right time to switch.
Import flashcards: bring Anki, Quizlet, CSV, TSV and .txt straight into FSRS-6
Quanta imports existing flashcard collections entirely client-side: Anki (.apkg including LaTeX), Quizlet (CSV export), your own CSV and TSV files and plain .txt lists. No server upload, no data loss, no third-party account needed. All imported cards use the FSRS-6 algorithm right away (Ye et al. 2022, ACM SIGKDD, log-loss 0.35, significantly more precise than SM-2, 22% lower log-loss).
Anki migration quantified: After import, FSRS-6 starts calibration anew, and within 3 to 5 reviews the forgetting curves are more precise than the entire SM-2 history. LaTeX formulas are carried over correctly and rendered natively. Only the text cards (question and answer) are imported, images, media and audio are not carried over. Import takes under 30 seconds even for large decks.
Your own lists: Self-written cards are uploaded as a TSV or .txt file. One card per line, question and answer separated by a tab or semicolon. Quanta detects the structure automatically and processes everything locally in your browser, free of charge.
Research: Active recall: 81% versus 27% retention (Karpicke & Roediger 2008, Science 319:966). Spaced repetition improves long-term retention substantially over massed practice (Cepeda et al. 2006, Psychological Bulletin 132:354).
Competition: Quanta offers a native .apkg import, CSV, TSV and .txt import as well as an AI scan from documents. Imported cards use the FSRS-6 algorithm in Quanta immediately (as of June 2026).
Pricing: Quanta Starter: file import (Anki .apkg, CSV, TSV, .txt) free and unlimited, entirely in the browser. Quanta Essential: from 6,00 €/Monat on the annual plan (8,00 €/Monat monthly). Quanta Performance: from 10,50 €/Monat on the annual plan (as of June 2026). GDPR compliant.
Note: Educational context: after import, decks can be assigned to a personal degree program and semester. 350+ degree programs. FSRS-6 calibrates imported cards to the individual user after 3 to 5 reviews. Quanta Study, AM Creative Tech UG (haftungsbeschränkt), Dresden, Germany. quanta-study.de.