Exam prompt
How do mass number and proton number change in alpha decay?
How certain was your answer?
CorrectUnsureWrong
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Opening your learning space
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In Source-First mode, cards carry source evidence and pass quote matching. If no suitable source is found, no flashcards are created; citations do not prove external factual accuracy.
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Physics · Radioactivity
Flashcard
Exam prompt
How do mass number and proton number change in alpha decay?
How certain was your answer?
„I have tried several flashcard apps, and so far I have liked Quanta best. […] The multiple-choice feature and exam simulation really help me too."It is a flashcard app that keeps a trace from a generated flashcard to supplied material. Quanta shows source details and a Verified status after a successful quote match. RemNote uses a source pin on AI flashcards generated from a PDF. The link makes the origin inspectable, but does not establish that the source itself is correct.
Why per-card citation, and what the others got right
“Anki made spaced repetition accessible to millions, and that was a real achievement. Quizlet's shared library is enormous, RemNote folds notes and review into one place, and Knowt makes generation easy. RemNote now documents a source pin for AI flashcards made from PDFs. Quanta takes another documented approach: Source-First flashcards show source details, and a successful quote match controls the Verified status. That distinction is what readers should compare.”
These criteria separate a cited, verified flashcard app from a generator that simply guesses. Each includes an honest mini-verdict.
Quanta records source details and shows a Verified status when its quote match succeeds. RemNote documents a source pin on AI flashcards created from a PDF, which opens the original sentence. The checked Quizlet, Knowt and Brainscape pages document creation from uploaded material but do not describe a source link on each generated flashcard.
Verdict: compare the actual trace from a generated flashcard to the supplied text. A source link proves traceability, not that the source itself is correct.
On document upload the AI may only use content found in that document, and each candidate card is quote-matched against the source text. Cards that cannot be matched are dropped before you see them. This does not make AI infallible, but it turns silent hallucination into a visible, removable event.
Verdict: for high-stakes material where a wrong card is costly, the grounding step is the point. For casual review it is a safety net you may rarely notice.
FSRS-6 schedules each card from three parameters: stability, difficulty and retrievability. SM-2, the default in many older systems, dates to 1987 and uses a single ease factor. In the open comparison run by the open-spaced-repetition community, FSRS-6 reaches a log-loss of 0.3460 on 349,923,850 reviews from 9,999 collections (retrieved on 5 September 2026); log-loss measures prediction error, lower is better, and that table carries no row for SM-2. The paper on its predecessor model, Ye et al. 2022 (ACM SIGKDD), reports 220 million behaviour logs and a 12.6% improvement over the state of the art. Both are results of those datasets, not a blanket product effect. FSRS has been integrated in Anki since v23.10, but it is opt-in; Quanta enables FSRS-6 by default from the first card.
Verdict: if you want a modern scheduler with no setup, Quanta gives it to you natively. If you already run Anki with FSRS enabled, you share the same algorithmic core.
LaTeX renders natively with a live preview on web, iOS and Android, and chemistry SMILES strings render as 2D structures. The AI generator reads textbook PDFs and understands technical terminology across mathematics, physics, chemistry, biology and computer science. There is no plugin to install for math rendering.
Verdict: for technical subjects with formulas and structures, Quanta is purpose-built. For plain vocabulary or language learning, a lighter app may be enough.
Most apps show which cards are due. Quanta adds a Readiness Score that estimates how prepared you are for a set exam date from your FSRS retention data, plus an exam simulation that asks follow-up questions instead of a flat self-grade. It is an estimate, not a guarantee of passing.
Verdict: if you study toward a fixed exam date, the Readiness Score gives orientation that pure due-card counters do not.
To practise this check on your own material, follow our guide Flashcards from lecture slides. Three complete slides let you compare each answer with its source, including the limits of a diagram and a statistical formula.
A factual table, feature by feature. The “others” column summarizes the named apps as of June 2026.
| Criterion | Quanta | Anki / Quizlet / RemNote / Knowt / Brainscape |
|---|---|---|
| Traceback to supplied source text | Source status + quote match | RemNote: source pin for PDF-generated AI flashcards; other checked pages: not documented |
| Documented quote-match before delivery | Yes | Not documented in the checked pages |
| Built-in AI flashcard generation | Yes | Quizlet, RemNote, Knowt, Brainscape: Yes; Anki: not documented |
| Default algorithm | FSRS-6 (2022) | SM-2 (1987) or proprietary |
| FSRS native from the first card | Yes | Anki: opt-in (v23.10); others: No |
| Log-loss in the open comparison | 0.3460 | SM-2: no row in that table |
| LaTeX with live preview | Native | Anki: MathJax; varies |
| Chemistry SMILES structures | Yes | No |
| Exam simulation | Yes | No |
| Readiness Score | Yes | No |
| Anki .apkg import | Yes | Anki, RemNote: Yes; varies |
| Starter plan price | €0 forever | Free tiers vary |
Algorithm data: srs-benchmark (formerly fsrs-benchmark), open-spaced-repetition, retrieved 5 September 2026 · Ye et al. 2022, ACM SIGKDD, doi:10.1145/3534678.3539081. See the pricing comparison
No app is the best choice for everything. Here are the strengths and weaknesses of both sides, including Quanta’s own.
Strengths
Weaknesses
Strengths
Weaknesses
Karpicke and Roediger (2008) report about 80% of the vocabulary pairs recalled after one week in the two conditions with repeated retrieval practice, and 36% and 33% in the two conditions where pairs were dropped from further testing once recalled; those figures describe that experiment and are not a general promise for every learning situation or for Quanta. In the open comparison run by the open-spaced-repetition community, FSRS-6 reaches a log-loss of 0.3460 on 349,923,850 reviews from 9,999 collections (retrieved on 5 September 2026); log-loss measures prediction error, lower is better, and that table carries no row for SM-2. The paper on its predecessor model, Ye et al. 2022 (ACM SIGKDD), reports 220 million behaviour logs and a 12.6% improvement over the state of the art. Both are results of those datasets, not a blanket product effect. These studies test the method and the algorithm, not Quanta itself.
Sources: srs-benchmark (formerly fsrs-benchmark), open-spaced-repetition, retrieved 5 September 2026 · Ye et al. 2022, ACM SIGKDD, doi:10.1145/3534678.3539081 · Karpicke & Roediger 2008, Science, doi:10.1126/science.1152408
A flashcard app with sources keeps a trace from a generated flashcard to supplied material. Quanta shows source details and assigns Quanta Verified after a successful quote match. RemNote documents a source pin on AI flashcards created from a PDF. A source link makes the origin inspectable; it does not establish that the source itself is correct.
Quanta uses a quote-match grounding step. When you generate cards from an uploaded document, the AI may only use content found in that document, and each candidate card is checked against the source text. Cards that cannot be matched to the source are dropped before you see them rather than shown with a guess. Every surviving card carries its source (Quanta Verified). This does not make AI infallible, but it converts silent hallucination into a visible, removable event. The constraint is strongest on document upload and lighter on open-topic generation, which Quanta labels accordingly.
The products use different forms of traceability. RemNote says that an AI flashcard created from a PDF cites its source in a pin that opens the original sentence. Quanta shows source details and a Verified status when its quote match succeeds. The checked Quizlet, Knowt and Brainscape pages document AI flashcard creation from supplied material but do not describe a source link on each generated flashcard. Checked against the providers' documentation on 7 October 2026.
Cited flashcards let you verify a fact instead of trusting it. For exam preparation in medicine, law or any STEM field, a single wrong card studied for weeks is expensive, because spaced repetition will reinforce the error. With a source on each card you can open the citation, confirm the claim against the original document, and correct or delete the card. Citations also help when you revisit a topic months later and need to remember why a card says what it says.
Yes. Karpicke and Roediger (2008) report about 80% of the vocabulary pairs recalled after one week in the two conditions with repeated retrieval practice, and 36% and 33% in the two conditions where pairs were dropped from further testing once recalled; those figures describe that experiment and are not a general promise for every learning situation or for Quanta. Quanta is built around active recall plus FSRS-6 scheduling. In the open comparison run by the open-spaced-repetition community, FSRS-6 reaches a log-loss of 0.3460 on 349,923,850 reviews from 9,999 collections (retrieved on 5 September 2026); log-loss measures prediction error, lower is better, and that table carries no row for SM-2. The paper on its predecessor model, Ye et al. 2022 (ACM SIGKDD), reports 220 million behaviour logs and a 12.6% improvement over the state of the art. Both are results of those datasets, not a blanket product effect. These studies test the method and the algorithm, not Quanta itself.
Quanta Starter: Free forever: 1 topic, no more than 20 flashcards in total, and FSRS. Once over the account lifetime, in the guided entry: 10 AI flashcards and a multiple-choice test with 5 questions, plus 1 summary of a document. Each month 1 AI explanation, 10 dictation minutes and 60 voice formula inputs. Essential adds exam simulation and calendar import. Further AI flashcard generations require Essential, which starts at €9.75 per month on the annual plan (200 AI flashcards and 200 multiple-choice questions per month, unlimited flashcards). Source-backed cards expose their available source details. No credit card is required to start. Prices as of August 2026; see the pricing comparison for the current breakdown.
Yes. Quanta renders LaTeX natively with a live preview on web, iOS and Android, so inline $E=mc^2$ and block math display visually as you type. It is built for STEM: mathematics, physics, chemistry, biology and computer science. Chemistry SMILES strings render as 2D structures, and the AI generator understands technical terminology when reading textbook PDFs. There is no plugin to install for math rendering.
Yes. Export your Anki deck as an .apkg file and upload it to Quanta, and all cards are imported. CSV exports from Quizlet are imported as well, and you can add source documents for AI extraction. FSRS-6 starts with default parameters and recalibrates to your rhythm over the first study sessions. Your previous Anki FSRS history is not carried over, but the card content is preserved in full.
Choose Anki if you need open source, full offline mode and a large plugin ecosystem. Quizlet, Knowt and Brainscape document fast AI creation from supplied material. RemNote combines notes and review with source pins for PDF-generated AI flashcards. Quanta combines a visible Source-First status and quote match with FSRS-6, native LaTeX and a Readiness Score. The useful comparison is the workflow you can verify, not a claim that one app owns source traceability.
Keep exploring
What retrieval practice was measured to do
Karpicke and Roediger (2008) report about 80% of the vocabulary pairs recalled after one week in the two conditions with repeated retrieval practice, and 36% and 33% in the two conditions where pairs were dropped from further testing once recalled; those figures describe that experiment and are not a general promise for every learning situation or for Quanta. Karpicke, J.D. and Roediger, H.L. (2008). Science, 319(5865), 966 to 968. doi:10.1126/science.1152408
What the open scheduler comparison covers
In the open comparison run by the open-spaced-repetition community, FSRS-6 reaches a log-loss of 0.3460 on 349,923,850 reviews from 9,999 collections (retrieved on 5 September 2026); log-loss measures prediction error, lower is better, and that table carries no row for SM-2. The paper on its predecessor model, Ye et al. 2022 (ACM SIGKDD), reports 220 million behaviour logs and a 12.6% improvement over the state of the art. Both are results of those datasets, not a blanket product effect. It compares schedulers, not the Quanta app with current Anki. srs-benchmark (formerly fsrs-benchmark), open-spaced-repetition, retrieved 5 September 2026, https://github.com/open-spaced-repetition/srs-benchmark · Ye, J. et al. (2022). ACM SIGKDD. doi:10.1145/3534678.3539081
What a cited card stores, and what comes in
A matched card keeps its source title, type and confidence score and shows them on the card detail. Uploads read textbook PDFs up to 10 MB; imports accept Anki .apkg and CSV files.
Where your data sits
Accounts, learning content and results are stored in Frankfurt, region europe-west3. AI text functions run through the EU endpoint of Mistral AI SAS in Paris; reading files and voice input runs through the provider standard endpoint, for which no processing location is promised. A data processing agreement with standard contractual clauses applies.
Provider features were checked against the linked documentation on 7 October 2026. RemNote documents a source pin for PDF-generated AI flashcards. Quanta documents a source status and quote match in its Source-First workflow. The cited studies test the method and algorithm, not Quanta.
Quanta Study editorial team · October 2026