Accepted posters TQC 2024
There are 429 accepted posters for TQC 2024. Of these, the Programme Committee highlighted 19 Outstanding Posters: you can find them by filtering on the dropdown tag menu below.
Clarifications
Accepted does not mean presented: Note that not all accepted posters will be presented at the conference due to author availability constraints. Shortly before the conference start, we will clarify which posters are set to be presented in person, based on whether the authors have registered for the conference. If you are interested in a particular poster, please contact the author directly.
Online presentation: For authors who cannot make it to the conference, it will be possible to present the poster online throughout the week on our Discord server. We will share instructions closer to the conference. In our experience, online attendance of these presentations is much lower than in-person attendance.
Withdrawing poster: If you cannot or do not wish to present your accepted poster, you don’t need to contact the organizers or PC chairs; this list will stay here to mark all submissions that were accepted. Exception: if you found a fatal mistake in the submission or would like to change the authors’ names, please let us know.
Upload media: If you would like to upload a thumbnail, more links or the poster pdf, please follow the link on the notification email sent by the PC chairs to the corresponding authors.
Poster sessions: The live poster sessions will be on Monday and Thursday (see schedule). If your poster submission number is below 290, you present on Monday; if it is above 290, you present on Thursday (290 is a talk). If you cannot make it to your allocated session, just bring the poster to the other session and find a free slot. You don’t need to ask the organizers.
Poster printing and size: The poster size should be A0 (84.1 cm × 118.9 cm) in portrait orientation. We recommend bringing your poster with you, as printing options in Okinawa are limited.
217 entries « ‹ 3 of 3
› » 201.
Céline Chevalier, Paul Hermouet, Quoc-Huy Vu
Towards Unclonable Cryptography in the Plain Model Poster
2024.
@Poster{P24_392,
title = {Towards Unclonable Cryptography in the Plain Model},
author = {Céline Chevalier and Paul Hermouet and Quoc-Huy Vu},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
202.
Sacha Lerch, Manuel Rudolph, Supanut Thanasilp, Zoe Holmes, Oriel Kiss, Michele Grossi, Sofia Vallecorsa
Trainability barriers and opportunities in quantum generative modeling Poster
2024.
@Poster{P24_330,
title = {Trainability barriers and opportunities in quantum generative modeling},
author = {Sacha Lerch and Manuel Rudolph and Supanut Thanasilp and Zoe Holmes and Oriel Kiss and Michele Grossi and Sofia Vallecorsa},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
203.
Enrico Rinaldi, Yuta Kikuchi, Matthias Rosenkranz, Ryuji Sakata
Training Quantum Boltzmann Machines to learn physical Gibbs states Poster
2024.
@Poster{P24_496,
title = {Training Quantum Boltzmann Machines to learn physical Gibbs states},
author = {Enrico Rinaldi and Yuta Kikuchi and Matthias Rosenkranz and Ryuji Sakata},
year = {2024},
date = {2024-01-01},
abstract = {Gibbs states play central roles in understanding the equilibrium properties of quantum many-body systems, and also find
applications in optimization and machine learning, where they are known as quantum Boltzmann machines (QBMs). The fact that the model Hamiltonian may contain non-commuting terms can make QBM more expressive than the classical Boltzmann machine, and in stark contrast to many other quantum machine learning models, training the QBM with an appropriate choice of objective function does not suffer from the vanishing gradient problem. In this work we numerically investigate the performance of QBMs for different target Gibbs states, model Hamiltonians, training parameters, and training strategies, used as a quantum generative model. We have developed a software package [1] that performs the QBM training with various combinations of target, model, and parameters using exact diagonalization on classical computers.
[1] E. Rinaldi, Y. Kikuchi, M. Rosenkranz, and R. Sakata,
https://github.com/cqcl/qbm benchmark dataset (2024), qbm benchmark dataset},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
Gibbs states play central roles in understanding the equilibrium properties of quantum many-body systems, and also find
applications in optimization and machine learning, where they are known as quantum Boltzmann machines (QBMs). The fact that the model Hamiltonian may contain non-commuting terms can make QBM more expressive than the classical Boltzmann machine, and in stark contrast to many other quantum machine learning models, training the QBM with an appropriate choice of objective function does not suffer from the vanishing gradient problem. In this work we numerically investigate the performance of QBMs for different target Gibbs states, model Hamiltonians, training parameters, and training strategies, used as a quantum generative model. We have developed a software package [1] that performs the QBM training with various combinations of target, model, and parameters using exact diagonalization on classical computers.
[1] E. Rinaldi, Y. Kikuchi, M. Rosenkranz, and R. Sakata,
https://github.com/cqcl/qbm benchmark dataset (2024), qbm benchmark dataset
204.
Naim Elias Comar, Luis Felipe Santos, Danilo Cius, Rafael Wagner, Barbara Amaral
Transformation contextuality witness in heat flow inversion Poster
2024.
@Poster{P24_535,
title = {Transformation contextuality witness in heat flow inversion},
author = {Naim Elias Comar and Luis Felipe Santos and Danilo Cius and Rafael Wagner and Barbara Amaral},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
205.
Adam Ehrenberg, Joseph Iosue, Abhinav Deshpande, Dominik Hangleiter, Alexey Gorshkov
Transition of Anticoncentration in Gaussian Boson Sampling Poster
2024.
@Poster{P24_410,
title = {Transition of Anticoncentration in Gaussian Boson Sampling},
author = {Adam Ehrenberg and Joseph Iosue and Abhinav Deshpande and Dominik Hangleiter and Alexey Gorshkov},
year = {2024},
date = {2024-01-01},
keywords = {Outstanding Poster, Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
206.
Patrick Emonts, Mengyao Hu, Albert Aloy, Jordi Tura Brugués
Tropological Bell Nonlocality: Topologically-induced Bell's nonlocality with tropical algebra Poster
2024.
@Poster{P24_366,
title = {Tropological Bell Nonlocality: Topologically-induced Bell's nonlocality with tropical algebra},
author = {Patrick Emonts and Mengyao Hu and Albert Aloy and Jordi Tura Brugués},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
207.
Moisés Bermejo, Felix Huber
Uncertainty relations from state polynomial optimization Poster
2024.
@Poster{P24_377,
title = {Uncertainty relations from state polynomial optimization},
author = {Moisés Bermejo and Felix Huber},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
208.
Pablo Bermejo, Paolo Braccia, Marco Cerezo, Manuel Rudolph, Zoe Holmes, Lukasz Cincio
Understanding the power and simulability of Quantum Convolutional Neural Networks Poster
2024.
@Poster{P24_558,
title = {Understanding the power and simulability of Quantum Convolutional Neural Networks},
author = {Pablo Bermejo and Paolo Braccia and Marco Cerezo and Manuel Rudolph and Zoe Holmes and Lukasz Cincio},
url = {https://tqc-conference.org/wp-content/uploads/cfdb7_uploads/1717199309-poster-TQC_2024_extended-abstract_558.pdf},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
209.
Zheng An, Jiahui Wu, Muchun Yang, D. L. Zhou, Bei Zeng
Unified Quantum State Tomography and Hamiltonian Learning Using Transformer Models: A Language-Translation-Like Approach for Quantum Systems Poster
2024.
@Poster{P24_497,
title = {Unified Quantum State Tomography and Hamiltonian Learning Using Transformer Models: A Language-Translation-Like Approach for Quantum Systems},
author = {Zheng An and Jiahui Wu and Muchun Yang and D. L. Zhou and Bei Zeng},
url = {https://link.aps.org/doi/10.1103/PhysRevApplied.21.014037},
year = {2024},
date = {2024-01-01},
abstract = {As quantum technology rapidly advances, the need for efficient scalable methods to characterize quantum systems intensifies. Quantum state tomography and Hamiltonian learning are essential for interpreting and optimizing quantum systems, yet a unified approach remains elusive. Such an integration could enhance our understanding of the complex relationship between quantum states and Hamiltonians, contributing to the development of more efficient methodologies. In this paper, we present a method that integrates quantum state tomography and Hamiltonian learning, drawing inspiration from machine translation in the field of natural language processing (NLP). We demonstrate the effectiveness of our approach across a variety of quantum systems, successfully learning the complex relationships between quantum states and Hamiltonians. Furthermore, the scalability and few-shot learning capabilities of our method could potentially minimize the resources required for characterizing and optimizing quantum systems. Our research provides valuable insights into the relationship between quantum states and Hamiltonians, paving the way for further studies on quantum systems and advancing quantum computation and related technologies.},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
As quantum technology rapidly advances, the need for efficient scalable methods to characterize quantum systems intensifies. Quantum state tomography and Hamiltonian learning are essential for interpreting and optimizing quantum systems, yet a unified approach remains elusive. Such an integration could enhance our understanding of the complex relationship between quantum states and Hamiltonians, contributing to the development of more efficient methodologies. In this paper, we present a method that integrates quantum state tomography and Hamiltonian learning, drawing inspiration from machine translation in the field of natural language processing (NLP). We demonstrate the effectiveness of our approach across a variety of quantum systems, successfully learning the complex relationships between quantum states and Hamiltonians. Furthermore, the scalability and few-shot learning capabilities of our method could potentially minimize the resources required for characterizing and optimizing quantum systems. Our research provides valuable insights into the relationship between quantum states and Hamiltonians, paving the way for further studies on quantum systems and advancing quantum computation and related technologies.
210.
Alexander Nietner
Unifying (Quantum) Statistical and Parametrized (Quantum) Algorithms Poster
2024.
@Poster{P24_355,
title = {Unifying (Quantum) Statistical and Parametrized (Quantum) Algorithms},
author = {Alexander Nietner},
url = {http://arxiv.org/abs/2310.17716},
year = {2024},
date = {2024-01-01},
abstract = {Kearns' statistical query (SQ) oracle (STOC'93) lends a unifying perspective with a rich explanatory power for most classical machine learning algorithms. This ceases to be true in quantum learning, where many settings admit neither an SQ nor a quantum statistical query (QSQ) analog. In this work, we take inspiration from Kearns' SQ oracle and Valiant's weak evaluation oracle (TOCT'14) to unify statistical and parametrized learning. We present a systematic study of the problem of learning from an evaluation oracle, which provides an estimate of function values, and extend Feldman's framework for SQ learning (COLT'17) to our setting. This leads to unconditional lower bounds and a characterization of learning linear function classes which are directly applicable to the QSQ setting and virtually any algorithm based on loss function optimization. Our first application is to extend prior results on the learnability of quantum states and their output distributions from the SQ to the (multi-copy) QSQ setting, implying exponential separations between learning stabilizer states from (multi-copy) QSQ's versus from quantum samples. Our second application is to analyze and dissect the hardness of quantum machine learning (QML). We gain a broad understanding of the hardness of various QML tasks, which goes beyond the intuition of barren plateaus and enables us to separate the implications of barren plateaus depending on the context in which they appear.},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
Kearns' statistical query (SQ) oracle (STOC'93) lends a unifying perspective with a rich explanatory power for most classical machine learning algorithms. This ceases to be true in quantum learning, where many settings admit neither an SQ nor a quantum statistical query (QSQ) analog. In this work, we take inspiration from Kearns' SQ oracle and Valiant's weak evaluation oracle (TOCT'14) to unify statistical and parametrized learning. We present a systematic study of the problem of learning from an evaluation oracle, which provides an estimate of function values, and extend Feldman's framework for SQ learning (COLT'17) to our setting. This leads to unconditional lower bounds and a characterization of learning linear function classes which are directly applicable to the QSQ setting and virtually any algorithm based on loss function optimization. Our first application is to extend prior results on the learnability of quantum states and their output distributions from the SQ to the (multi-copy) QSQ setting, implying exponential separations between learning stabilizer states from (multi-copy) QSQ's versus from quantum samples. Our second application is to analyze and dissect the hardness of quantum machine learning (QML). We gain a broad understanding of the hardness of various QML tasks, which goes beyond the intuition of barren plateaus and enables us to separate the implications of barren plateaus depending on the context in which they appear.
211.
Carlos Fernandes, Rafael Wagner, Leonardo Novo, Ernesto F. Galvão
Unitary-invariant witnesses of quantum imaginarity Poster
2024.
@Poster{P24_448,
title = {Unitary-invariant witnesses of quantum imaginarity},
author = {Carlos Fernandes and Rafael Wagner and Leonardo Novo and Ernesto F. Galvão},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
212.
Salvatore Tirone, Francesco Mele, Vittorio Giovannetti, Ludovico Lami
Universal entanglement distillation Poster
2024.
@Poster{P24_466,
title = {Universal entanglement distillation},
author = {Salvatore Tirone and Francesco Mele and Vittorio Giovannetti and Ludovico Lami},
year = {2024},
date = {2024-01-01},
abstract = {In this work we find a protocol which allows the parties to distill a certain amount of Bell pairs with a fixed asymptotic error strictly smaller than one from any unknown distillable state, using only LOCC operations. Moreover, we link two seemingly unrelated concepts: the existence of a universal distillation algorithm and the strong converse distillable entanglement. To do so we conceive the following protocol: Alice and Bob decide to use a fraction of their n copies of their shared state to obtain a classical estimate, then Alice sends her part of the state to Bob via classical teleportation, after that Bob is able to perform a measure such that the infidelity between the estimate and the actual state scales as O(1/n). After this passage Alice and Bob perform on the remaining copies the optimal LOCC to distill entanglement from the estimate, thanks to the scaling of the infidelity they can achieve the optimal rate on this batch of copies paying the price of an error which depends on the fraction of the copies used to obtain the classical estimate.},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
In this work we find a protocol which allows the parties to distill a certain amount of Bell pairs with a fixed asymptotic error strictly smaller than one from any unknown distillable state, using only LOCC operations. Moreover, we link two seemingly unrelated concepts: the existence of a universal distillation algorithm and the strong converse distillable entanglement. To do so we conceive the following protocol: Alice and Bob decide to use a fraction of their n copies of their shared state to obtain a classical estimate, then Alice sends her part of the state to Bob via classical teleportation, after that Bob is able to perform a measure such that the infidelity between the estimate and the actual state scales as O(1/n). After this passage Alice and Bob perform on the remaining copies the optimal LOCC to distill entanglement from the estimate, thanks to the scaling of the infidelity they can achieve the optimal rate on this batch of copies paying the price of an error which depends on the fraction of the copies used to obtain the classical estimate.
213.
Shouvanik Chakrabarti, Pierre Minssen, Romina Yalovetzky, Marco Pistoia
Universal Quantum Speedups for Mixed Integer Programming Poster
2024.
@Poster{P24_459,
title = {Universal Quantum Speedups for Mixed Integer Programming},
author = {Shouvanik Chakrabarti and Pierre Minssen and Romina Yalovetzky and Marco Pistoia},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
214.
Orsolya Kalman, Aurel Gabris, Igor Jex, Tamas Kiss
Universal, unambiguous preparation of Bell pairs Poster
2024.
@Poster{P24_556,
title = {Universal, unambiguous preparation of Bell pairs},
author = {Orsolya Kalman and Aurel Gabris and Igor Jex and Tamas Kiss},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
215.
Manasi Shingane, Yusuf Alnawakhtha, Andrew Childs, Carl Miller
Verification of Quantum Networks Poster
2024.
@Poster{P24_438,
title = {Verification of Quantum Networks},
author = {Manasi Shingane and Yusuf Alnawakhtha and Andrew Childs and Carl Miller},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
216.
Johannes Frank, Elham Kashefi, Dominik Leichtle, Michael Oliveira
Verification-inspired quantum benchmarking Poster
2024.
@Poster{P24_489,
title = {Verification-inspired quantum benchmarking},
author = {Johannes Frank and Elham Kashefi and Dominik Leichtle and Michael Oliveira},
year = {2024},
date = {2024-01-01},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
217.
Eric Sabo, Lane Gunderman, Benjamin Ide, Michael Vasmer, Guillaume Dauphinais
Weight Reduced Stabilizer Codes with Lower Overhead Poster
2024.
@Poster{P24_537,
title = {Weight Reduced Stabilizer Codes with Lower Overhead},
author = {Eric Sabo and Lane Gunderman and Benjamin Ide and Michael Vasmer and Guillaume Dauphinais},
url = {https://arxiv.org/pdf/2402.05228},
year = {2024},
date = {2024-01-01},
abstract = {Stabilizer codes are the most widely studied class of quantum error-correcting codes and form the basis of most proposals for a fault-tolerant quantum computer. A stabilizer code is defined by a set of parity-check operators, which are measured in order to infer information about errors that may have occurred. In typical settings, measuring these operators is itself a noisy process and the noise strength scales with the number of qubits involved in a given parity check, or its weight. Hastings proposed a method for reducing the weights of the parity checks of a stabilizer code, though it has previously only been studied in the asymptotic regime. Here, we instead focus on the regime of small-to-medium size codes suitable for quantum computing hardware. We provide both a fully explicit description of Hastings's method and propose a substantially simplified weight reduction method that is applicable to the class of quantum product codes. Our simplified method allows us to reduce the check weights of hypergraph and lifted product codes to at most six, while preserving the number of logical qubits and at least retaining (in fact often increasing) the code distance. The price we pay is an increase in the number of physical qubits by a constant factor, but we find that our method is much more efficient than Hastings's method in this regard. We benchmark the performance of our codes in a photonic quantum computing architecture based on GKP qubits and passive linear optics, finding that our weight reduction method substantially improves code performance.},
keywords = {Poster session Thursday},
pubstate = {published},
tppubtype = {Poster}
}
Stabilizer codes are the most widely studied class of quantum error-correcting codes and form the basis of most proposals for a fault-tolerant quantum computer. A stabilizer code is defined by a set of parity-check operators, which are measured in order to infer information about errors that may have occurred. In typical settings, measuring these operators is itself a noisy process and the noise strength scales with the number of qubits involved in a given parity check, or its weight. Hastings proposed a method for reducing the weights of the parity checks of a stabilizer code, though it has previously only been studied in the asymptotic regime. Here, we instead focus on the regime of small-to-medium size codes suitable for quantum computing hardware. We provide both a fully explicit description of Hastings's method and propose a substantially simplified weight reduction method that is applicable to the class of quantum product codes. Our simplified method allows us to reduce the check weights of hypergraph and lifted product codes to at most six, while preserving the number of logical qubits and at least retaining (in fact often increasing) the code distance. The price we pay is an increase in the number of physical qubits by a constant factor, but we find that our method is much more efficient than Hastings's method in this regard. We benchmark the performance of our codes in a photonic quantum computing architecture based on GKP qubits and passive linear optics, finding that our weight reduction method substantially improves code performance.
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