One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization | Paper Citation Record

Observation f1ad9ea9-22fa-48be-a7a4-56ab17b16502 · outbound

This paper cites Dense quantum coding and quantum finite automata.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Dense quantum coding and quantum finite automata

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:27.885560Z

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Observation 4c9fcecb-ef5f-4767-92ba-6b166fc7c0fb · outbound

This paper cites Quantization error propagation: Revisiting layer-wise post-training quantization.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Quantization error propagation: Revisiting layer-wise post-training quantization

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.800914Z

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Observation 2209e2a0-526d-45d0-bdca-66eb818ee519 · outbound

This paper cites Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman

Reference 4

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Observation 5e2721de-6578-4390-a565-dff3430e8198 · outbound

This paper cites Distributed optimization and statistical learning via the alternating direction method of multipliers.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Distributed optimization and statistical learning via the alternating direction method of multipliers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:27.901020Z

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Observation 773c4ea9-85c2-4ae8-ab58-3e3dbc8f4dd5 · outbound

This paper cites Quantum random access codes and incompatibility of measurements.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Quantum random access codes and incompatibility of measurements

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:27.904221Z

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Observation 45bf5ec1-5919-4a77-8029-8b6919f8f2ae · outbound

This paper cites QuIP : 2-bit quantization of large language models with guarantees.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization QuIP : 2-bit quantization of large language models with guarantees

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.792218Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b5ddf62-2a6c-4320-865c-8a093a332e09 · outbound

This paper cites Incompatibility robustness of quantum measurements: A unified framework.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Incompatibility robustness of quantum measurements: A unified framework

Reference 8

Resolution: verified exact; doi, observed 2026-08-08T17:34:35.347482Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eb27e36c-5711-42de-8a5f-2e32814898e0 · outbound

This paper cites LLM .int8() : 8-bit matrix multiplication for transformers at scale.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization LLM .int8() : 8-bit matrix multiplication for transformers at scale

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.783360Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 91f246b6-bc6b-451f-ba52-067afe3a3a02 · outbound

This paper cites Esser, Jeffrey L.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Esser, Jeffrey L

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.774753Z

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Observation e5be8aae-c20a-4254-9718-c02bf2dac3ec · outbound

This paper cites Simple and general bounds on quantum random access codes.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Simple and general bounds on quantum random access codes

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:27.919418Z

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Observation 6a238ac1-2015-4756-95ab-946a46aeb352 · outbound

This paper cites GPTQ : Accurate post-training quantization for generative pre-trained transformers.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization GPTQ : Accurate post-training quantization for generative pre-trained transformers

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.766141Z

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Observation 1f82d6d8-9546-4685-8884-a1338e006c37 · outbound

This paper cites An invitation to quantum incompatibility.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization An invitation to quantum incompatibility

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:27.934205Z

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Observation 3d07138d-3e24-40d3-a796-5e53c1fb6a3a · outbound

This paper cites Probability inequalities for sums of bounded random variables.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Probability inequalities for sums of bounded random variables

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:27.937022Z

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Observation c090b0cd-9549-4110-82df-c61b35f5a439 · outbound

This paper cites Information-theoretic bounds on quantum advantage in machine learning.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Information-theoretic bounds on quantum advantage in machine learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:27.939767Z

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Observation 746c970c-9c8a-43fa-a5a6-df7e91eeee00 · outbound

This paper cites an unresolved cited work.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Unresolved cited work

Reference 19

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Observation cb332593-3ed6-4722-86f0-7379a118f7fc · outbound

This paper cites BiLLM : Pushing the limit of post-training quantization for LLMs.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization BiLLM : Pushing the limit of post-training quantization for LLMs

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.757567Z

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Observation 7c6c40f6-986f-4b26-8cd1-978eae463cd4 · outbound

This paper cites Binarized neural networks.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Binarized neural networks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.748723Z

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Observation e993e223-1c21-4c34-83c8-e2f2fc36d568 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:27.950843Z

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Observation b7b426f3-fc25-4dc8-acc6-cd86ecdc6a30 · outbound

This paper cites U ber das paulische \.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization U ber das paulische \

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:27.953635Z

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Observation d0fd8e25-d4e4-469e-83c2-74489e022da4 · outbound

This paper cites Extremely low bit neural network: Squeeze the last bit out with ADMM.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Extremely low bit neural network: Squeeze the last bit out with ADMM

Reference 24

Resolution: verified exact; doi, observed 2026-08-08T17:34:32.816040Z

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Observation b92a3be8-aef4-46fd-a3bc-89195880974e · outbound

This paper cites GPTAQ : Efficient finetuning-free quantization for asymmetric calibration.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization GPTAQ : Efficient finetuning-free quantization for asymmetric calibration

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.739742Z

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Observation 1f650fad-75cb-4690-9773-f8150243f70c · outbound

This paper cites AWQ : Activation-aware weight quantization for on-device LLM compression and acceleration.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization AWQ : Activation-aware weight quantization for on-device LLM compression and acceleration

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.730989Z

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Observation 2e7e562f-0bf3-4124-9880-a763f96c0b77 · outbound

This paper cites Optimal lower bounds for quantum automata and random access codes.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Optimal lower bounds for quantum automata and random access codes

Reference 31

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T17:34:38.179252Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2aed9730-c512-462b-9952-8ed348b36303 · outbound

This paper cites XNOR-Net : ImageNet classification using binary convolutional neural networks.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization XNOR-Net : ImageNet classification using binary convolutional neural networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:27.983861Z

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Observation aad62e6b-633b-44ed-ac0b-0afd4389ae3b · outbound

This paper cites QuIP \# : Even better LLM quantization with hadamard incoherence and lattice codebooks.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization QuIP \# : Even better LLM quantization with hadamard incoherence and lattice codebooks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.721955Z

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Observation 6148f896-b8b2-4cfa-81c4-8aaf246c3301 · outbound

This paper cites OneBit : Towards extremely low-bit large language models.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization OneBit : Towards extremely low-bit large language models

Reference 38

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T17:34:37.848093Z

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Observation 96079d3f-3df1-4b57-a039-59485dbab6bd · outbound

This paper cites PB-LLM : Partially binarized large language models.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization PB-LLM : Partially binarized large language models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.712670Z

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Observation bf6c56a5-fe77-4323-9ff0-afd775924057 · outbound

This paper cites Journal of the ACM , volume =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Journal of the ACM , volume =

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.703495Z

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Observation 11ca8eae-45f1-4b7e-92c2-be426ff14a12 · outbound

This paper cites Proceedings of the 40th Annual Symposium on Foundations of Computer Science (FOCS) , pages =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Proceedings of the 40th Annual Symposium on Foundations of Computer Science (FOCS) , pages =

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.694431Z

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Observation 7b753094-b23b-4dde-be21-f1069348540d · outbound

This paper cites Quantum Random Access Codes with Shared Randomness.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Quantum Random Access Codes with Shared Randomness

Reference 43

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Observation 7f72f458-698f-4aa6-ad21-942da4b41b7d · outbound

This paper cites EPL (Europhysics Letters) , volume =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization EPL (Europhysics Letters) , volume =

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.685199Z

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Observation 35ac3115-edcd-4cd6-b28b-ff476eeeb00c · outbound

This paper cites Quantum , volume =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Quantum , volume =

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.675615Z

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Observation 9833cdf5-9a11-4bf7-ba6d-f53f8803690e · outbound

This paper cites Getting almost all the bits from a quantum random access code.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Getting almost all the bits from a quantum random access code

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:28.023508Z

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Observation 438404fe-7e2d-4812-9ad8-c618119ad26d · outbound

This paper cites Journal of Physics A: Mathematical and Theoretical , volume =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Journal of Physics A: Mathematical and Theoretical , volume =

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.666366Z

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Observation 428811b2-7dc9-43d0-b828-aed784c15750 · outbound

This paper cites New Journal of Physics , volume =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization New Journal of Physics , volume =

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.657411Z

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Observation a336004e-7e3b-4181-87d9-c4440b376fa5 · outbound

This paper cites Physical Review Letters , volume =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Physical Review Letters , volume =

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.647825Z

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Observation bed948c1-0bd0-4310-b93a-83b7b75c8602 · outbound

This paper cites Science , volume =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Science , volume =

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.638520Z

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Observation 2efb48c8-9cdd-4031-93a1-ac39a083ec9d · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization LLaMA: Open and Efficient Foundation Language Models

Reference 51

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Observation aa71bbac-4e8e-4ad0-8724-f52fc9f992a2 · outbound

This paper cites The Llama 3 Herd of Models.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization The Llama 3 Herd of Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:28.040626Z

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Observation fdec7675-f795-4868-8f22-05cfd032c4dc · outbound

This paper cites arXiv preprint arXiv:2409.16694 , year =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization arXiv preprint arXiv:2409.16694 , year =

Reference 53

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Observation 08262f6d-40bb-40a6-8a56-d09118ce38c1 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:28.046057Z

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Observation ad72f98b-d325-4642-a50b-391b5a4c871c · outbound

This paper cites 2024 , url =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2024 , url =

Reference 55

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Observation c10a0efe-45fd-4f86-9207-a68b8d38e33a · outbound

This paper cites 2023 , url =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2023 , url =

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.624170Z

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Observation 6c745f84-d8e3-407c-b1f4-f3ed557258ed · outbound

This paper cites 2024 , url =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2024 , url =

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.615675Z

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Observation e1450b33-c20a-4f03-8883-f21e17dea107 · outbound

This paper cites and Li, Bo and Cameron, Pashmina and Jaggi, Martin and Alistarh, Dan and Hoefler, Torsten and Hensman, James , booktitle =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization and Li, Bo and Cameron, Pashmina and Jaggi, Martin and Alistarh, Dan and Hoefler, Torsten and Hensman, James , booktitle =

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.607229Z

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Observation 0b918e0b-993d-4fd5-8a22-f1f102c82ff6 · outbound

This paper cites Advances in Neural Information Processing Systems 38 , year =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Advances in Neural Information Processing Systems 38 , year =

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.598275Z

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Observation 01ad3239-06d5-4600-be6e-aedd17808ac7 · outbound

This paper cites 2025 , url =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2025 , url =

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.589263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b3727262-49d0-4cd5-bc5f-4994ae6d66cc · outbound

This paper cites 2026 , archiveprefix =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2026 , archiveprefix =

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:28.067158Z

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Observation 0019b4f1-d7de-4403-a8e4-ef957554240e · outbound

This paper cites 2025 , archiveprefix =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2025 , archiveprefix =

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-08-08T17:34:31.503262Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3cb346b9-747b-4fd9-848a-bb4e6b0b0b2c · outbound

This paper cites 2022 , url =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2022 , url =

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.580082Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8e6fa423-447a-4e4b-91dc-790ee6d4ff9b · outbound

This paper cites Advances in Neural Information Processing Systems 29 , pages =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Advances in Neural Information Processing Systems 29 , pages =

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.570958Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 69bdba3b-0281-456d-b311-0fee0a3d4058 · outbound

This paper cites 2016 , doi =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2016 , doi =

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.562151Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 50ef30cd-f3af-4ebe-af1b-46baba5b0803 · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 66

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Observation a13d1009-2d7c-464f-aebc-b95169820873 · outbound

This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:28.084656Z

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Observation 05cfab07-db74-4b2f-8ba8-2091ff192720 · outbound

This paper cites 2024 , url =.

One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2024 , url =

Reference 68

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2024 , doi =

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.544218Z

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2024 , archiveprefix =

Reference 70

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization PB-LLM: Partially Binarized Large Language Models

Reference 71

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Foundations and Trends in Machine Learning , volume =

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.534917Z

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Observation c4c4742a-89b9-4b14-bc77-98b38cfdc75c · outbound

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Extremely Low Bit Neural Network: Squeeze the Last Bit Out with

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.525150Z

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages =

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.515864Z

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Observation fb5c092a-4202-4066-9e43-b68be6b5a413 · outbound

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization The Eighth International Conference on Learning Representations , year =

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.506642Z

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2010 , isbn =

Reference 76

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 2018 , isbn =

Reference 77

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Reference 78

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization 1928 , doi =

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.497396Z

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Observation 1d9cc411-c752-492e-8e75-4b949edeb778 · outbound

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One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization Journal of the American Statistical Association , volume =

Reference 80

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Pith citing papers