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Revisiting Common Randomness, No-signaling and Information Structure in Decentralized Control

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arXiv 2024年

作者： Dhingra, Apurva Kulkarni, Ankur A. Center of Machine Intelligence and Data Science CMInDS Indian Institute of Technology Bombay Mumbai400076 India Systems and Control Engineering Indian Institute of Technology Bombay Mumbai400076 India Center of excellence in Quantum Information Computing Science and Technology QuICST Indian Institute of Technology Bombay Mumbai400076 India

This work revisits the no-signaling condition for decentralized information structures. We produce examples to show that within the no-signaling polytope exist strategies that cannot be achieved by passive common randomness but instead require agents to either share their observations with a mediator or communicate directly with each other. This poses a question mark on whether the no-signaling condition truly captures the decentralized information structure in the strictest *** Codes 93C41, 93E20, 81Q93 © 2024, CC BY.

关键词： Random processes

The "Code" of Ethics: A Holistic Audit of AI Code Generators

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arXiv 2023年

作者： Ma, Wanlun Song, Yiliao M., Xue S., Wen Y., Xiang School of Science Computing and Engineering Technologies Swinburne University of Technology VIC3122 Australia College of Business and Law RMIT University VIC3001 Australia Cybersecurity and Quantum Systems Group CSIRO’s Data61.

AI-powered programming language generation (PLG) models have gained increasing attention due to their ability to generate source code of programs in a few seconds with a plain program description. Despite their remarkable performance, many concerns are raised over the potential risks of their development and deployment, such as legal issues of copyright infringement induced by training usage of licensed code, and malicious consequences due to the unregulated use of these models. In this paper, we present the first-of-its-kind study to systematically investigate the accountability of PLG models from the perspectives of both model development and deployment. In particular, we develop a holistic framework not only to audit the training data usage of PLG models, but also to identify neural code generated by PLG models as well as determine its attribution to a source model. To this end, we propose using membership inference to audit whether a code snippet used is in the PLG model’s training data. In addition, we propose a learning-based method to distinguish between human-written code and neural code. In neural code attribution, through both empirical and theoretical analysis, we show that it is impossible to reliably attribute the generation of one code snippet to one model. We then propose two feasible alternative methods: one is to attribute one neural code snippet to one of the candidate PLG models, and the other is to verify whether a set of neural code snippets can be attributed to a given PLG model. The proposed framework thoroughly examines the accountability of PLG models which are verified by extensive experiments. The implementations of our proposed framework are also encapsulated into a new artifact, named CODEFORENSIC, to foster further research. © 2023, CC BY.

关键词： Codes (symbols)

NEO-QEC: Neural Network Enhanced Online Superconducting Decoder for Surface Codes

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arXiv 2022年

作者： Ueno, Yosuke Kondo, Masaaki Tanaka, Masamitsu Suzuki, Yasunari Tabuchi, Yutaka The University of Tokyo Japan Keio University Japan Nagoya University Japan NTT Computer and Data Science Laboratories Japan RIKEN Center for Quantum Computing Japan

quantum error correction (QEC) is essential for quantum computing to mitigate the effect of errors on qubits, and surface code (SC) is one of the most promising QEC methods. Decoding SCs is the most computational expensive task in the control device of quantum computers (QCs), and many works focus on accurate decoding algorithms for SCs, including ones with neural networks (NNs). Practical QCs also require low-latency decoding because slow decoding leads to the accumulation of errors on qubits, resulting in logical failures. For QCs with superconducting qubits, a practical decoder must be very power-efficient in addition to having high accuracy and low latency. In order to reduce the hardware complexity of QC, we are supposed to decode SCs in a cryogenic environment with a limited power budget, where superconducting qubits operate. In this paper, we propose an NN-based accurate, fast, and low-power decoder capable of decoding SCs and lattice surgery (LS) operations with measurement errors on ancillary qubits. To achieve both accuracy and hardware efficiency of the SC decoder, we apply a binarized NN. We design a neural processing unit (NPU) for the decoder with SFQ-based digital circuits and evaluate it with a SPICE-level simulation. We evaluate the decoder performance by a quantum error simulator for the single logical qubit protection and the minimum operation of LS with code distances up to 13, and it achieves 2.5% and 1.0% accuracy thresholds, respectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Error correction

Physics-inspired Machine Learning for quantum Error Mitigation

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arXiv 2025年

作者： Xu, Xiao-Yue Xue, Xin Chen, Tianyu Ding, Chen Li, Tian Zhou, Haoyi Huang, He-Liang Bao, Wan-Su Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou Henan450000 China Department of Computer Science Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Department of Software Beihang University Beijing100191 China

Noise is a major obstacle in current quantum computing, and Machine Learning for quantum Error Mitigation (ML-QEM) promises to address this challenge, enhancing computational accuracy while reducing the sampling overheads of standard QEM methods. Yet, existing models lack physical interpretability and rely heavily on extensive datasets, hindering their scalability in large-scale quantum circuits. To tackle these issues, we introduce the Neural Noise Accumulation Surrogate (NNAS), a physics-inspired neural network for ML-QEM that incorporates the structural characteristics of quantum noise accumulation within multi-layer circuits, endowing the model with physical interpretability. Experimental results demonstrate that NNAS outperforms current methods across a spectrum of metrics, including error mitigation capability, quantum resource consumption, and training dataset size. Notably, for deeper circuits where QEM methods typically struggle, NNAS achieves a remarkable reduction of over half in errors. NNAS also demands substantially fewer training data, reducing dataset reliance by at least an order of magnitude, due to its ability to rapidly capture noise accumulation patterns across circuit layers. This work pioneers the integration of quantum process-derived structural characteristics into neural network architectures, broadly enhancing QEM's performance and applicability, and establishes an integrative paradigm that extends to various quantum-inspired neural network architectures. © 2025, CC BY.

关键词： quantum computers

Entanglement Detection Length of Multipartite quantum States

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Physical Review Letters 2025年第5期134卷 050201-050201页

作者： Fei Shi Lin Chen Giulio Chiribella Qi Zhao QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong LMIB(Beihang University) Ministry of Education and School of Mathematical Sciences Beihang University Beijing 100191 China Department of Computer Science Parks Road Oxford OX1 3QD United Kingdom Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Multipartite entanglement is a valuable resource for quantum technologies. However, detecting this resource can be challenging: for genuine multipartite entanglement, the detection may require global measurements that are hard to implement experimentally. Here we introduce the concept of entanglement detection length, defined as the minimum number of particles that have to be jointly measured in order to detect genuine multipartite entanglement. For symmetric states, we show that the entanglement detection length can be determined by testing separability of the marginal states. For general states, we provide an upper bound on the entanglement detection length based on semidefinite programming. We show that the entanglement detection length is generally smaller than the minimum observable length needed to uniquely determine a multipartite state, and we provide examples achieving the maximum gap between these two quantities.

关键词： quantum entanglement

Deep Learning for Channel Prediction in Non-Stationary Wireless Fading Environments

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Deep Learning for Channel Prediction in Non-Stationary Wirel...

Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

In non-stationary wireless fading channels deep learning models are effective in predicting channel states as indicated by this study. This work compared the LSTM and GRU models with autoregressive methods or Kalman filter, implying that deep learning is indeed able to capture temporal dependence between variables and was likely more adapted than linear method when dealing with many changes in dynamics. From the perspective of pre-processing data, keys like attention mechanism and transfer learning were decisive elements in helping them to improve prediction scores and make models more robust. These results showed that these enhanced procedures allow the models to generalise over a wide range of non-stationary situations, generating consistent performance in various diverse environments. This study indicates the promise of deep learning for enhancing wireless network operation, which could lead to greater self-awareness and utilization features in emerging future-generation wireless systems.

关键词： Deep learning Fading channels Adaptation models Attention mechanisms Wireless networks Transfer learning Predictive models data models Kalman filters Long short term memory

Secrecy Capacity Optimization in RF/FSO Systems: Impact of Mixed Rayleigh and Log-Normal Fading with Atmospheric Turbulence

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Secrecy Capacity Optimization in RF/FSO Systems: Impact of M...

Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

This paper investigates the secrecy capacity optimization in RF/FSO systems with mixed Rayleigh and log-normal fading, while masking eavesdroppers channel state information () as well as suffering from atmospheric turbulence. We explore the impact of several parameters including beamforming angles, modulation schemes and hybrid RF/FSO link configurations through a combination of simulations as well as analytical models. Secrecy capacity is enhanced for an improved SNR and vice versa under higher turbulence conditions and cooler influents eavesdropped scenarios. Performance of the hybrid RF/FSO systems dominates considerably over single-link solutions, especially at longer distances. All these mechanisms appreciably increase the secrecy capacity, while beamforming and advanced modulation techniques offer most of the improvement. The study also demonstrates the interdependencies of system parameters, providing guidelines for designing secure communication systems under different environmental and operational conditions.

关键词： Heating systems Analytical models Array signal processing Communication systems Atmospheric modeling System performance Modulation Rayleigh channels Optimization Signal to noise ratio

Experimental demonstration of a high-fidelity virtual two-qubit gate

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Physical Review Research 2024年第1期6卷 013235-013235页

作者： Akhil Pratap Singh Kosuke Mitarai Yasunari Suzuki Kentaro Heya Yutaka Tabuchi Keisuke Fujii Yasunobu Nakamura Department of Applied Physics Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan

We experimentally demonstrate a virtual two-qubit gate and characterize it using quantum process tomography (QPT). The virtual two-qubit gate decomposes an actual two-qubit gate into single-qubit unitary gates and projection gates in quantum circuits for expectation-value estimation. We implement projection gates via midcircuit measurements. The deterministic sampling scheme reduces the number of experimental circuit evaluations required for decomposing a virtual two-qubit gate. We also apply quantum error mitigation to suppress the effect of measurement errors and improve the average gate fidelity of a virtual controlled-Z (CZ) gate to fav=0.9938±0.0002. Our results highlight a practical approach to implement virtual two-qubit gates with high fidelities, which are useful for simulating quantum circuits using fewer qubits and implementing two-qubit gates on a distant pair of qubits.

关键词： quantum algorithms quantum algorithms & computation quantum benchmarking quantum circuits quantum computation quantum gates quantum nondemolition measurement quantum simulation

Hamiltonian characterisation of multi-time processes with classical memory

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arXiv 2024年

作者： Goswami, Kaumudibikash Roy, Abhinash Kumar Srivastava, Varun Perez, Barr Giarmatzi, Christina Gilchrist, Alexei Costa, Fabio QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong Department of Physical and Mathematical Sciences Macquarie University SydneyNSW Australia School of Computer Science University of Technology Sydney SydneyNSW Australia Stockholm University KTH Royal Institute of Technology Stockholm Sweden

A central problem in the study of open quantum systems is the characterisation of non-Markovian processes, where an environment retains memory of its interaction with the system. A key distinction is whether or not this memory can be simulated classically, as this can lead to efficient modelling and noise mitigation. Powerful tools have been developed recently within the process matrix formalism, a framework that conveniently characterises all multi-time correlations through a sequence of measurements. This leads to a detailed classification of classical and quantum-memory processes and provides operational procedures to distinguish between them. However, these results leave open the question of what type of system-environment interactions lead to classical memory. More generally, process-matrix methods lack a direct connection to joint system-environment evolution, a cornerstone of open-system modelling. In this work, we characterise Hamiltonian and circuit-based models of system-environment interactions leading to classical memory. We show that general time-dependent Hamiltonians with product eigenstates, and where the environment’s eigenstates form a time-independent, orthonormal basis, always produce a particular type of classical memory: probabilistic mixtures of unitary processes. Additionally, we show that the most general type of classical-memory processes can be generated by a quantum circuit in which system and environment interact through a specific class of controlled unitaries. Our results establish the first strong link between process-matrix methods and traditional Hamiltonian-based approaches to open quantum systems. © 2024, CC BY.

关键词： Markov processes