pith:SIKKWVFN
Quantum Bayesian Networks: Compositionality and Typing via Linear Logic
Quantum Bayesian networks receive a compositional semantics and linear-logic typing that reduces to standard Bayesian networks for classical data and to tensor networks for quantum data.
arxiv:2604.26059 v2 · 2026-04-28 · cs.LO
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\pithnumber{SIKKWVFNATNIPAS265PJB666KG}
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Claims
A key feature of our compositional semantics is that when all causes are classical, it coincides with the standard factor-based semantics of Bayesian networks, while in the purely quantum case it reduces to tensor networks. We then propose a typed formalism based on linear logic proof-nets, where types ensure well-behaved composition of systems.
That the proposed linear-logic typing and compositional semantics correctly capture all causal relations and measurement probabilities in mixed classical-quantum systems without requiring extra constraints or losing expressiveness.
Quantum Bayesian networks receive a compositional semantics and linear-logic typing that reduces to standard Bayesian networks for classical data and to tensor networks for quantum data.
Receipt and verification
| First computed | 2026-05-27T01:05:55.127839Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
9214ab54ad04da87825af75e90fbde518e3efa02b8cb87fc39702b2986ef9b07
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/SIKKWVFNATNIPAS265PJB666KG \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9214ab54ad04da87825af75e90fbde518e3efa02b8cb87fc39702b2986ef9b07
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
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