Quantum Science and Technology最新文献

Continuous-variable multiplexed quantum repeater networks

IF 6.7 2区物理与天体物理

Quantum Science and Technology Pub Date : 2025-04-02 DOI: 10.1088/2058-9565/adc500

Pei-Zhe Li, William J Munro, Kae Nemoto and Nicolò Lo Piparo

引用次数: 0

Heuristic-free verification-inspired quantum benchmarking

IF 6.7 2区物理与天体物理

Quantum Science and Technology Pub Date : 2025-04-02 DOI: 10.1088/2058-9565/adc298

Johannes Frank, Elham Kashefi, Dominik Leichtle and Michael de Oliveira

引用次数: 0

Ensuring superior learning outcomes and data security for authorized learner

IF 6.7 2区物理与天体物理

Quantum Science and Technology Pub Date : 2025-04-02 DOI: 10.1088/2058-9565/adc501

Jeongho Bang, Wooyeong Song, Kyujin Shin and Yong-Su Kim

{"title":"Ensuring superior learning outcomes and data security for authorized learner","authors":"Jeongho Bang, Wooyeong Song, Kyujin Shin and Yong-Su Kim","doi":"10.1088/2058-9565/adc501","DOIUrl":"https://doi.org/10.1088/2058-9565/adc501","url":null,"abstract":"The learner’s ability to generate a hypothesis that closely approximates the target function is crucial in machine learning. Achieving this requires sufficient data; however, unauthorized access by an eavesdropping learner can lead to security risks. Thus, it is important to ensure the performance of the ‘authorized’ learner by limiting the quality of the training data accessible to eavesdroppers. Unlike previous studies focusing on encryption or access controls, we provide a theorem to ensure superior learning outcomes exclusively for the authorized learner with quantum label encoding. In this context, we use the probably-approximately-correct learning framework and introduce the concept of learning probability to quantitatively assess learner performance. Our theorem allows the condition that, given a training dataset, an authorized learner is guaranteed to achieve a certain quality of learning outcome, while eavesdroppers are not. Notably, this condition can be constructed based only on the authorized-learning-only measurable quantities of the training data, i.e. its size and noise degree. We validate our theoretical proofs and predictions through convolutional neural networks image classification learning.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"16 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collective preparation of large quantum registers with high fidelity

IF 6.7 2区物理与天体物理

Quantum Science and Technology Pub Date : 2025-04-01 DOI: 10.1088/2058-9565/adc3bb

Lorenzo Buffoni and Michele Campisi

引用次数: 0

Weak value quantum metrology beyond weak interaction

IF 6.7 2区物理与天体物理

Quantum Science and Technology Pub Date : 2025-04-01 DOI: 10.1088/2058-9565/ada0d5

Seung-Yeun Yoo, Yosep Kim, U-Shin Kim, Chung-Hyun Lee and Yoon-Ho Kim

引用次数: 0

Entangling cavity-magnon polaritons by interacting with phonons

IF 6.7 2区物理与天体物理

Quantum Science and Technology Pub Date : 2025-04-01 DOI: 10.1088/2058-9565/adc47d

Xuan Zuo, Zhi-Yuan Fan, Hang Qian, Rui-Chang Shen and Jie Li

引用次数: 0

The role of higher-order terms in trapped-ion quantum computing with magnetic gradient induced coupling

IF 6.7 2区物理与天体物理

Quantum Science and Technology Pub Date : 2025-03-30 DOI: 10.1088/2058-9565/adc1fe

Sebastian Nagies, Kevin T Geier, Javed Akram, Junichi Okamoto, Dimitrios Bantounas, Christof Wunderlich, Michael Johanning and Philipp Hauke

{"title":"The role of higher-order terms in trapped-ion quantum computing with magnetic gradient induced coupling","authors":"Sebastian Nagies, Kevin T Geier, Javed Akram, Junichi Okamoto, Dimitrios Bantounas, Christof Wunderlich, Michael Johanning and Philipp Hauke","doi":"10.1088/2058-9565/adc1fe","DOIUrl":"https://doi.org/10.1088/2058-9565/adc1fe","url":null,"abstract":"Trapped-ion hardware based on the magnetic gradient induced coupling (MAGIC) scheme is emerging as a promising platform for quantum computing. Nevertheless, in this—as in any other—quantum-computing platform, many technical questions still have to be resolved before large-scale and error-tolerant applications are possible. In this work, we present a thorough discussion of the structure and effects of higher-order terms in the MAGIC setup, which can occur due to anharmonicities in the external potential of the ion crystal (e.g. through Coulomb repulsion) or through curvature of the applied magnetic field. These terms generate systematic shifts in the leading-order interactions and take the form of three-spin couplings, two-spin couplings, local fields, as well as diverse phonon–phonon conversion mechanisms. We find that most of these are negligible in realistic situations, with only two contributions that need careful attention. First, there are undesired longitudinal fields contributing shifts to the resonance frequency, whose strength increases with chain length and phonon occupation numbers; while their mean effect can easily be compensated by additional Z rotations, phonon number fluctuations need to be avoided for precise gate operations. Second, anharmonicities of the Coulomb interaction can lead to well-known two-to-one conversions of phonon excitations. Both of these error terms can be mitigated by sufficiently cooling the phonons to the ground-state. Our detailed analysis constitutes an important contribution on the way of making magnetic-gradient trapped-ion quantum technology fit for large-scale applications, and it may inspire new ways to purposefully design interaction terms.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"31 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A memristive neural decoder for cryogenic fault-tolerant quantum error correction

IF 6.7 2区物理与天体物理

Quantum Science and Technology Pub Date : 2025-03-28 DOI: 10.1088/2058-9565/adc3ba

Victor Yon, Frédéric Marcotte, Pierre-Antoine Mouny, Gebremedhin A Dagnew, Bohdan Kulchytskyy, Sophie Rochette, Yann Beilliard, Dominique Drouin and Pooya Ronagh

{"title":"A memristive neural decoder for cryogenic fault-tolerant quantum error correction","authors":"Victor Yon, Frédéric Marcotte, Pierre-Antoine Mouny, Gebremedhin A Dagnew, Bohdan Kulchytskyy, Sophie Rochette, Yann Beilliard, Dominique Drouin and Pooya Ronagh","doi":"10.1088/2058-9565/adc3ba","DOIUrl":"https://doi.org/10.1088/2058-9565/adc3ba","url":null,"abstract":"Neural decoders for quantum error correction rely on neural networks to classify syndromes extracted from error correction codes and find appropriate recovery operators to protect logical information against errors. Its ability to adapt to hardware noise and long-term drifts make neural decoders promising candidates for inclusion in a fault-tolerant quantum architecture. However, given their limited scalability, it is prudent that small-scale (local) neural decoders are treated as first stages of multi-stage decoding schemes for fault-tolerant quantum computers with millions of qubits. In this case, minimizing the decoding time to match the stabilization measurements frequency and a tight co-integration with the QPUs is highly desired. Cryogenic realizations of neural decoders can not only improve the performance of higher stage decoders, but they can minimize communication delays, and alleviate wiring bottlenecks. In this work, we design and analyze a neural decoder based on an in-memory computation (IMC) architecture, where crossbar arrays of resistive memory devices are employed to both store the synaptic weights of the neural decoder and perform analog matrix–vector multiplications. In simulations supported by experimental measurements, we investigate the impact of TiOx-based memristive devices’ non-idealities on decoding fidelity. We develop hardware-aware re-training methods to mitigate the fidelity loss, restoring the ideal decoder’s pseudo-threshold for the distance-3 surface code. This work provides a pathway to scalable, fast, and low-power cryogenic IMC hardware for integrated fault-tolerant quantum error correction.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"57 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantum simulation of boson-related Hamiltonians: techniques, effective Hamiltonian construction, and error analysis

IF 6.7 2区物理与天体物理

Quantum Science and Technology Pub Date : 2025-03-28 DOI: 10.1088/2058-9565/adbf42

Bo Peng, Yuan Su, Daniel Claudino, Karol Kowalski, Guang Hao Low and Martin Roetteler

{"title":"Quantum simulation of boson-related Hamiltonians: techniques, effective Hamiltonian construction, and error analysis","authors":"Bo Peng, Yuan Su, Daniel Claudino, Karol Kowalski, Guang Hao Low and Martin Roetteler","doi":"10.1088/2058-9565/adbf42","DOIUrl":"https://doi.org/10.1088/2058-9565/adbf42","url":null,"abstract":"Elementary quantum mechanics proposes that a closed physical system consistently evolves in a reversible manner. However, control and readout necessitate the coupling of the quantum system to the external environment, subjecting it to relaxation and decoherence. Consequently, system-environment interactions are indispensable for simulating physically significant theories. A broad spectrum of physical systems in condensed-matter and high-energy physics, vibrational spectroscopy, and circuit and cavity QED necessitates the incorporation of bosonic degrees of freedom, such as phonons, photons, and gluons, into optimized fermion algorithms for near-future quantum simulations. In particular, when a quantum system is surrounded by an external environment, its basic physics can usually be simplified to a spin or fermionic system interacting with bosonic modes. Nevertheless, troublesome factors such as the magnitude of the bosonic degrees of freedom typically complicate the direct quantum simulation of these interacting models, necessitating the consideration of a comprehensive plan. This strategy should specifically include a suitable fermion/boson-to-qubit mapping scheme to encode sufficiently large yet manageable bosonic modes, and a method for truncating and/or downfolding the Hamiltonian to the defined subspace for performing an approximate but highly accurate simulation, guided by rigorous error analysis. In this pedagogical tutorial review, we aim to provide such an exhaustive strategy, focusing on encoding and simulating certain bosonic-related model Hamiltonians, inclusive of their static properties and time evolutions. Specifically, we emphasize two aspects: (1) the discussion of recently developed quantum algorithms for these interacting models and the construction of effective Hamiltonians, and (2) a detailed analysis regarding a tightened error bound for truncating the bosonic modes for a class of fermion-boson interacting Hamiltonians.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"10 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Subspace preserving quantum convolutional neural network architectures

IF 6.7 2区物理与天体物理

Quantum Science and Technology Pub Date : 2025-03-28 DOI: 10.1088/2058-9565/adbf43

Léo Monbroussou, Jonas Landman, Letao Wang, Alex B Grilo and Elham Kashefi

{"title":"Subspace preserving quantum convolutional neural network architectures","authors":"Léo Monbroussou, Jonas Landman, Letao Wang, Alex B Grilo and Elham Kashefi","doi":"10.1088/2058-9565/adbf43","DOIUrl":"https://doi.org/10.1088/2058-9565/adbf43","url":null,"abstract":"Subspace preserving quantum circuits are a class of quantum algorithms that, relying on some symmetries in the computation, can offer theoretical guarantees for their training. Those algorithms have gained extensive interest as they can offer polynomial speed-up and can be used to mimic classical machine learning algorithms. In this work, we propose a novel convolutional neural network architecture model based on Hamming weight (HW) preserving quantum circuits. In particular, we introduce convolutional layers, and measurement based pooling layers that preserve the symmetries of the quantum states while realizing non-linearity using gates that are not subspace preserving. Our proposal offers significant polynomial running time advantages over classical deep-learning architecture. We provide an open source simulation library for HW preserving quantum circuits that can simulate our techniques more efficiently with GPU-oriented libraries. Using this code, we provide examples of architectures that highlight great performances on complex image classification tasks with a limited number of qubits, and with fewer parameters than classical deep-learning architectures.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"1 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0