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Qin H, Che L, Wei C, Xu F, Huang Y, Xin T. Experimental Direct Quantum Fidelity Learning via a Data-Driven Approach. PHYSICAL REVIEW LETTERS 2024; 132:190801. [PMID: 38804925 DOI: 10.1103/physrevlett.132.190801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 03/11/2024] [Indexed: 05/29/2024]
Abstract
Fidelity estimation is an important technique for evaluating prepared quantum states in noisy quantum devices. A recent theoretical work proposed a frugal approach called neural quantum fidelity estimation (NQFE) [X. Zhang et al., Phys. Rev. Lett. 127, 130503 (2021).PRLTAO0031-900710.1103/PhysRevLett.127.130503]. While this requires a much smaller number of measurement operators than full quantum state tomography, it uses a weight-based floating measurement strategy that predetermines the top global Pauli operators that contribute the most to the fidelity and uses discrete fidelity intervals as predictions. In this Letter, we develop a measurement-fixed NQFE based on a transformer model which requires less measurement cost and can output continuous estimates of fidelity. Here we further experimentally apply the NQFE in a realistic situation using a nuclear spin quantum processor. We prepare the ground states of local Hamiltonians and arbitrary states and investigate how to estimate their fidelity with reference states, and we compare the fidelity estimation strategy with our and the original NQFE to conventional tomography. It is shown that NQFE can estimate the fidelity with comparable accuracy to the tomography approach. In the future, NQFE will become an important tool for benchmarking quantum states ahead of the advent of well-trusted fault-tolerant quantum computers.
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Affiliation(s)
- Haiyang Qin
- Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China and Shenzhen Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Liangyu Che
- Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China and Shenzhen Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Chao Wei
- Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China and Shenzhen Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Feng Xu
- Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China and Shenzhen Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yulei Huang
- Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China and Shenzhen Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Tao Xin
- Shenzhen Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China and Shenzhen Key Laboratory of Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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2
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Suprano A, Zia D, Innocenti L, Lorenzo S, Cimini V, Giordani T, Palmisano I, Polino E, Spagnolo N, Sciarrino F, Palma GM, Ferraro A, Paternostro M. Experimental Property Reconstruction in a Photonic Quantum Extreme Learning Machine. PHYSICAL REVIEW LETTERS 2024; 132:160802. [PMID: 38701482 DOI: 10.1103/physrevlett.132.160802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/07/2024] [Indexed: 05/05/2024]
Abstract
Recent developments have led to the possibility of embedding machine learning tools into experimental platforms to address key problems, including the characterization of the properties of quantum states. Leveraging on this, we implement a quantum extreme learning machine in a photonic platform to achieve resource-efficient and accurate characterization of the polarization state of a photon. The underlying reservoir dynamics through which such input state evolves is implemented using the coined quantum walk of high-dimensional photonic orbital angular momentum and performing projective measurements over a fixed basis. We demonstrate how the reconstruction of an unknown polarization state does not need a careful characterization of the measurement apparatus and is robust to experimental imperfections, thus representing a promising route for resource-economic state characterization.
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Affiliation(s)
- Alessia Suprano
- Dipartimento di Fisica - Sapienza Università di Roma, Piazza le Aldo Moro 5, I-00185 Roma, Italy
| | - Danilo Zia
- Dipartimento di Fisica - Sapienza Università di Roma, Piazza le Aldo Moro 5, I-00185 Roma, Italy
| | - Luca Innocenti
- Università degli Studi di Palermo, Dipartimento di Fisica e Chimica - Emilio Segrè, via Archirafi 36, I-90123 Palermo, Italy
| | - Salvatore Lorenzo
- Università degli Studi di Palermo, Dipartimento di Fisica e Chimica - Emilio Segrè, via Archirafi 36, I-90123 Palermo, Italy
| | - Valeria Cimini
- Dipartimento di Fisica - Sapienza Università di Roma, Piazza le Aldo Moro 5, I-00185 Roma, Italy
| | - Taira Giordani
- Dipartimento di Fisica - Sapienza Università di Roma, Piazza le Aldo Moro 5, I-00185 Roma, Italy
| | - Ivan Palmisano
- Centre for Quantum Materials and Technologies, School of Mathematics and Physics, Queen's University Belfast, BT7 1NN, United Kingdom
| | - Emanuele Polino
- Dipartimento di Fisica - Sapienza Università di Roma, Piazza le Aldo Moro 5, I-00185 Roma, Italy
- Centre for Quantum Dynamics and Centre for Quantum Computation and Communication Technology, Griffith University, Yuggera Country, Brisbane, Queensland 4111, Australia
| | - Nicolò Spagnolo
- Dipartimento di Fisica - Sapienza Università di Roma, Piazza le Aldo Moro 5, I-00185 Roma, Italy
| | - Fabio Sciarrino
- Dipartimento di Fisica - Sapienza Università di Roma, Piazza le Aldo Moro 5, I-00185 Roma, Italy
| | - G Massimo Palma
- Università degli Studi di Palermo, Dipartimento di Fisica e Chimica - Emilio Segrè, via Archirafi 36, I-90123 Palermo, Italy
| | - Alessandro Ferraro
- Centre for Quantum Materials and Technologies, School of Mathematics and Physics, Queen's University Belfast, BT7 1NN, United Kingdom
- Quantum Technology Lab, Dipartimento di Fisica Aldo Pontremoli, Università degli Studi di Milano, I-20133 Milano, Italy
| | - Mauro Paternostro
- Università degli Studi di Palermo, Dipartimento di Fisica e Chimica - Emilio Segrè, via Archirafi 36, I-90123 Palermo, Italy
- Centre for Quantum Materials and Technologies, School of Mathematics and Physics, Queen's University Belfast, BT7 1NN, United Kingdom
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3
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Eriksson AM, Sépulcre T, Kervinen M, Hillmann T, Kudra M, Dupouy S, Lu Y, Khanahmadi M, Yang J, Castillo-Moreno C, Delsing P, Gasparinetti S. Universal control of a bosonic mode via drive-activated native cubic interactions. Nat Commun 2024; 15:2512. [PMID: 38509084 PMCID: PMC10954688 DOI: 10.1038/s41467-024-46507-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/29/2024] [Indexed: 03/22/2024] Open
Abstract
Linear bosonic modes offer a hardware-efficient alternative for quantum information processing but require access to some nonlinearity for universal control. The lack of nonlinearity in photonics has led to encoded measurement-based quantum computing, which relies on linear operations but requires access to resourceful ('nonlinear') quantum states, such as cubic phase states. In contrast, superconducting microwave circuits offer engineerable nonlinearities but suffer from static Kerr nonlinearity. Here, we demonstrate universal control of a bosonic mode composed of a superconducting nonlinear asymmetric inductive element (SNAIL) resonator, enabled by native nonlinearities in the SNAIL element. We suppress static nonlinearities by operating the SNAIL in the vicinity of its Kerr-free point and dynamically activate nonlinearities up to third order by fast flux pulses. We experimentally realize a universal set of generalized squeezing operations, as well as the cubic phase gate, and exploit them to deterministically prepare a cubic phase state in 60 ns. Our results initiate the experimental field of polynomial quantum computing, in the continuous-variables notion originally introduced by Lloyd and Braunstein.
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Affiliation(s)
- Axel M Eriksson
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden.
| | - Théo Sépulcre
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Mikael Kervinen
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Timo Hillmann
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Marina Kudra
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Simon Dupouy
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Yong Lu
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden
- Physikalisches Institut, University of Stuttgart, 70569, Stuttgart, Germany
| | - Maryam Khanahmadi
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Jiaying Yang
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Claudia Castillo-Moreno
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Per Delsing
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden
| | - Simone Gasparinetti
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96, Gothenburg, Sweden.
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4
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Wang QQ, Dong S, Li XW, Xu XY, Wang C, Han S, Yung MH, Han YJ, Li CF, Guo GC. Efficient learning of mixed-state tomography for photonic quantum walk. SCIENCE ADVANCES 2024; 10:eadl4871. [PMID: 38489356 DOI: 10.1126/sciadv.adl4871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/11/2024] [Indexed: 03/17/2024]
Abstract
Noise-enhanced applications in open quantum walk (QW) has recently seen a surge due to their ability to improve performance. However, verifying the success of open QW is challenging, as mixed-state tomography is a resource-intensive process, and implementing all required measurements is almost impossible due to various physical constraints. To address this challenge, we present a neural-network-based method for reconstructing mixed states with a high fidelity (∼97.5%) while costing only 50% of the number of measurements typically required for open discrete-time QW in one dimension. Our method uses a neural density operator that models the system and environment, followed by a generalized natural gradient descent procedure that significantly speeds up the training process. Moreover, we introduce a compact interferometric measurement device, improving the scalability of our photonic QW setup that enables experimental learning of mixed states. Our results demonstrate that highly expressive neural networks can serve as powerful alternatives to traditional state tomography.
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Affiliation(s)
- Qin-Qin Wang
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China
- CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China
| | - Shaojun Dong
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230031, China
| | - Xiao-Wei Li
- Department of Physics, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xiao-Ye Xu
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China
- CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
| | - Chao Wang
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230031, China
| | - Shuai Han
- Yangtze Delta Region Industrial Innovation Center of Quantum and Information Technology, Suzhou 215100, China
| | - Man-Hong Yung
- Institute for Quantum Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yong-Jian Han
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China
- CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230031, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
| | - Chuan-Feng Li
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China
- CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
| | - Guang-Can Guo
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei 230026, China
- CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
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5
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Iyama D, Kamiya T, Fujii S, Mukai H, Zhou Y, Nagase T, Tomonaga A, Wang R, Xue JJ, Watabe S, Kwon S, Tsai JS. Observation and manipulation of quantum interference in a superconducting Kerr parametric oscillator. Nat Commun 2024; 15:86. [PMID: 38167480 PMCID: PMC10762009 DOI: 10.1038/s41467-023-44496-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/15/2023] [Indexed: 01/05/2024] Open
Abstract
Quantum tunneling is the phenomenon that makes superconducting circuits "quantum". Recently, there has been a renewed interest in using quantum tunneling in phase space of a Kerr parametric oscillator as a resource for quantum information processing. Here, we report a direct observation of quantum interference induced by such tunneling and its dynamics in a planar superconducting circuit through Wigner tomography. We experimentally elucidate all essential properties of this quantum interference, such as mapping from Fock states to cat states, a temporal oscillation due to the pump detuning, as well as its characteristic Rabi oscillations and Ramsey fringes. Finally, we perform gate operations as manipulations of the observed quantum interference. Our findings lay the groundwork for further studies on quantum properties of superconducting Kerr parametric oscillators and their use in quantum information technologies.
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Affiliation(s)
- Daisuke Iyama
- Department of Physics, Graduate School of Science, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan
- RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama, Japan
| | - Takahiko Kamiya
- Department of Physics, Graduate School of Science, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan
- RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama, Japan
| | - Shiori Fujii
- Department of Physics, Graduate School of Science, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan
- RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama, Japan
| | - Hiroto Mukai
- RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama, Japan
- Research Institute for Science and Technology, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan
| | - Yu Zhou
- RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama, Japan
| | - Toshiaki Nagase
- Department of Physics, Graduate School of Science, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan
- RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama, Japan
| | - Akiyoshi Tomonaga
- RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama, Japan
- Research Institute for Science and Technology, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan
| | - Rui Wang
- RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama, Japan
- Research Institute for Science and Technology, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan
| | - Jiao-Jiao Xue
- RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama, Japan
- Institute of Theoretical Physics, School of Physics, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Shohei Watabe
- College of Engineering, Department of Computer Science and Engineering, Shibaura Institute of Technology, Koto-ku, Tokyo, Japan
| | - Sangil Kwon
- Research Institute for Science and Technology, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan.
| | - Jaw-Shen Tsai
- RIKEN Center for Quantum Computing (RQC), Wako-shi, Saitama, Japan
- Research Institute for Science and Technology, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan
- Graduate School of Science, Tokyo University of Science, Shinjuku-ku, Tokyo, Japan
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6
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Wang Y, Xue S, Wang Y, Liu Y, Ding J, Shi W, Wang D, Liu Y, Fu X, Huang G, Huang A, Deng M, Wu J. Quantum generative adversarial learning in photonics. OPTICS LETTERS 2023; 48:5197-5200. [PMID: 37831826 DOI: 10.1364/ol.505084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023]
Abstract
Quantum generative adversarial networks (QGANs), an intersection of quantum computing and machine learning, have attracted widespread attention due to their potential advantages over classical analogs. However, in the current era of noisy intermediate-scale quantum (NISQ) computing, it is essential to investigate whether QGANs can perform learning tasks on near-term quantum devices usually affected by noise and even defects. In this Letter, using a programmable silicon quantum photonic chip, we experimentally demonstrate the QGAN model in photonics for the first time to our knowledge and investigate the effects of noise and defects on its performance. Our results show that QGANs can generate high-quality quantum data with a fidelity higher than 90%, even under conditions where up to half of the generator's phase shifters are damaged, or all of the generator and discriminator's phase shifters are subjected to phase noise up to 0.04π. Our work sheds light on the feasibility of implementing QGANs on the NISQ-era quantum hardware.
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7
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Proppe AH, Lee KLK, Kaplan AEK, Ginterseder M, Krajewska CJ, Bawendi MG. Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations. PHYSICAL REVIEW LETTERS 2023; 131:053603. [PMID: 37595234 DOI: 10.1103/physrevlett.131.053603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 06/26/2023] [Indexed: 08/20/2023]
Abstract
Solid-state single-photon emitters (SPEs) are quantum light sources that combine atomlike optical properties with solid-state integration and fabrication capabilities. SPEs are hindered by spectral diffusion, where the emitter's surrounding environment induces random energy fluctuations. Timescales of spectral diffusion span nanoseconds to minutes and require probing single emitters to remove ensemble averaging. Photon correlation Fourier spectroscopy (PCFS) can be used to measure time-resolved single emitter line shapes, but is hindered by poor signal-to-noise ratio in the measured correlation functions at early times due to low photon counts. Here, we develop a framework to simulate PCFS correlation functions directly from diffusing spectra that match well with experimental data for single colloidal quantum dots. We use these simulated datasets to train a deep ensemble autoencoder machine learning model that outputs accurate, noiseless, and probabilistic reconstructions of the noisy correlations. Using this model, we obtain reconstructed time-resolved single dot emission line shapes at timescales as low as 10 ns, which are otherwise completely obscured by noise. This enables PCFS to extract optical coherence times on the same timescales as Hong-Ou-Mandel two-photon interference, but with the advantage of providing spectral information in addition to estimates of photon indistinguishability. Our machine learning approach is broadly applicable to different photon correlation spectroscopy techniques and SPE systems, offering an enhanced tool for probing single emitter line shapes on previously inaccessible timescales.
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Affiliation(s)
- Andrew H Proppe
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Kin Long Kelvin Lee
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Accelerated Computing Systems and Graphics, Intel Corporation, 2111 25th NE Avenue, Hillsboro, Oregon 97124, USA
| | - Alexander E K Kaplan
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Matthias Ginterseder
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Chantalle J Krajewska
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Moungi G Bawendi
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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8
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Wu YD, Zhu Y, Bai G, Wang Y, Chiribella G. Quantum Similarity Testing with Convolutional Neural Networks. PHYSICAL REVIEW LETTERS 2023; 130:210601. [PMID: 37295121 DOI: 10.1103/physrevlett.130.210601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 04/08/2023] [Accepted: 04/25/2023] [Indexed: 06/12/2023]
Abstract
The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous variable quantum systems. In this Letter, we develop a machine learning algorithm for comparing unknown continuous variable states using limited and noisy data. The algorithm works on non-Gaussian quantum states for which similarity testing could not be achieved with previous techniques. Our approach is based on a convolutional neural network that assesses the similarity of quantum states based on a lower-dimensional state representation built from measurement data. The network can be trained off-line with classically simulated data from a fiducial set of states sharing structural similarities with the states to be tested, with experimental data generated by measurements on the fiducial states, or with a combination of simulated and experimental data. We test the performance of the model on noisy cat states and states generated by arbitrary selective number-dependent phase gates. Our network can also be applied to the problem of comparing continuous variable states across different experimental platforms, with different sets of achievable measurements, and to the problem of experimentally testing whether two states are equivalent up to Gaussian unitary transformations.
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Affiliation(s)
- Ya-Dong Wu
- Department of Computer Science, QICI Quantum Information and Computation Initiative, The University of Hong Kong, Pokfulam Road, Hong Kong
| | - Yan Zhu
- Department of Computer Science, QICI Quantum Information and Computation Initiative, The University of Hong Kong, Pokfulam Road, Hong Kong
| | - Ge Bai
- Centre for Quantum Technologies, National University of Singapore, Block S15, 3 Science Drive 2, 117543, Singapore
| | - Yuexuan Wang
- Department of Computer Science, AI Technology Laboratory, The University of Hong Kong, Pokfulam Road, Hong Kong
- College of Computer Science and Technology, Zhejiang University, Zhejiang Province 310058, China
| | - Giulio Chiribella
- Department of Computer Science, QICI Quantum Information and Computation Initiative, The University of Hong Kong, Pokfulam Road, Hong Kong
- Department of Computer Science, Parks Road, Oxford OX1 3QD, United Kingdom
- Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada
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9
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Ahmed S, Quijandría F, Kockum AF. Gradient-Descent Quantum Process Tomography by Learning Kraus Operators. PHYSICAL REVIEW LETTERS 2023; 130:150402. [PMID: 37115870 DOI: 10.1103/physrevlett.130.150402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 02/17/2023] [Accepted: 03/16/2023] [Indexed: 06/19/2023]
Abstract
We perform quantum process tomography (QPT) for both discrete- and continuous-variable quantum systems by learning a process representation using Kraus operators. The Kraus form ensures that the reconstructed process is completely positive. To make the process trace preserving, we use a constrained gradient-descent (GD) approach on the so-called Stiefel manifold during optimization to obtain the Kraus operators. Our ansatz uses a few Kraus operators to avoid direct estimation of large process matrices, e.g., the Choi matrix, for low-rank quantum processes. The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes, but shines by combining the best features of these two methods. Similar to CS (but unlike PLS), GD-QPT can reconstruct a process from just a small number of random measurements, and similar to PLS (but unlike CS) it also works for larger system sizes, up to at least five qubits. We envisage that the data-driven approach of GD-QPT can become a practical tool that greatly reduces the cost and computational effort for QPT in intermediate-scale quantum systems.
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Affiliation(s)
- Shahnawaz Ahmed
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96 Gothenburg, Sweden
| | - Fernando Quijandría
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96 Gothenburg, Sweden
- Quantum Machines Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0495, Japan
| | - Anton Frisk Kockum
- Department of Microtechnology and Nanoscience, Chalmers University of Technology, 412 96 Gothenburg, Sweden
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10
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Zhu Y, Wu YD, Bai G, Wang DS, Wang Y, Chiribella G. Flexible learning of quantum states with generative query neural networks. Nat Commun 2022; 13:6222. [PMID: 36266334 PMCID: PMC9584912 DOI: 10.1038/s41467-022-33928-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 10/07/2022] [Indexed: 12/24/2022] Open
Abstract
Deep neural networks are a powerful tool for characterizing quantum states. Existing networks are typically trained with experimental data gathered from the quantum state that needs to be characterized. But is it possible to train a neural network offline, on a different set of states? Here we introduce a network that can be trained with classically simulated data from a fiducial set of states and measurements, and can later be used to characterize quantum states that share structural similarities with the fiducial states. With little guidance of quantum physics, the network builds its own data-driven representation of a quantum state, and then uses it to predict the outcome statistics of quantum measurements that have not been performed yet. The state representations produced by the network can also be used for tasks beyond the prediction of outcome statistics, including clustering of quantum states and identification of different phases of matter.
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Affiliation(s)
- Yan Zhu
- grid.194645.b0000000121742757QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong
| | - Ya-Dong Wu
- grid.194645.b0000000121742757QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong
| | - Ge Bai
- grid.194645.b0000000121742757QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong
| | - Dong-Sheng Wang
- grid.9227.e0000000119573309CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, 100190 P.R. China
| | - Yuexuan Wang
- grid.194645.b0000000121742757QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong ,grid.13402.340000 0004 1759 700XCollege of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Giulio Chiribella
- grid.194645.b0000000121742757QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong ,grid.4991.50000 0004 1936 8948Department of Computer Science, Oxford, OX1 3QD UK ,grid.420198.60000 0000 8658 0851Perimeter Institute for Theoretical Physics, Waterloo, ON N2L 2Y5 Canada
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Lohani S, Lukens J, Glasser RT, Searles TA, Kirby B. Data-Centric Machine Learning in Quantum Information Science. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac9036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a ``toy model'' demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.
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12
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Gowthami S, Harikumar R. Improved self-attention generative adversarial adaptation network-based melanoma classification. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-220015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Melanoma is one of the widespread skin cancers that has affected millions in past decades. Detection of skin cancer at preliminary stages may become a source of reducing mortality rates. Hence, it is required to develop an autonomous system of reliable type for the detection of melanoma via image processing. This paper develops an independent medical imaging technique using Self-Attention Adaptation Generative Adversarial Network (SAAGAN). The entire processing model involves the process of pre-processing, feature extraction using Scale Invariant Feature Transform (SIFT), and finally, classification using SAAGAN. The simulation is conducted on ISIC 2016/PH2 datasets, where 10-fold cross-validation is undertaken on a high-end computing platform. The simulation is performed to test the model efficacy against various images on several performance metrics that include accuracy, precision, recall, f-measure, percentage error, Matthews Correlation Coefficient, and Jaccard Index. The simulation shows that the proposed SAAGAN is more effective in detecting the test images than the existing GAN protocols.
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Affiliation(s)
- S. Gowthami
- Department of Biomedical Engineering, Bannari Amman Institute of Technology, Sathyamangalam
| | - R. Harikumar
- Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam
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13
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Barrett TD, Malyshev A, Lvovsky AI. Autoregressive neural-network wavefunctions for ab initio quantum chemistry. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00461-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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14
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CWM-CGAN Method for Renewable Energy Scenario Generation Based on Weather Label Multi-Factor Definition. Processes (Basel) 2022. [DOI: 10.3390/pr10030470] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
With the increasing installed capacity of renewable energy in the energy system, the uncertainty of renewable energy has an increasingly prominent impact on power system planning and operation. Renewable energy such as wind and solar energy is greatly affected by the external weather. How to use a reasonable method to describe the relationship between weather and renewable energy output, so as to measure the uncertainty of renewable energy more accurately, is an important problem. To solve this problem, this paper proposes a renewable energy scenario generation method based on a conditional generation countermeasure network and combination weighting method (CWM-CGAN). In this method, the combination of AHP and the entropy weight method is used to analyze the meteorological factors, the weather classification is defined as the condition label in the conditional generation countermeasure network, and the energy scenario is generated by the conditional generation confrontation network. In this paper, the proposed method is tested with actual PV data, and the results show that the proposed model can describe the uncertainty of PV more accurately.
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15
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Hsieh HY, Chen YR, Wu HC, Chen HL, Ning J, Huang YC, Wu CM, Lee RK. Extract the Degradation Information in Squeezed States with Machine Learning. PHYSICAL REVIEW LETTERS 2022; 128:073604. [PMID: 35244420 DOI: 10.1103/physrevlett.128.073604] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/18/2021] [Accepted: 01/25/2022] [Indexed: 06/14/2023]
Abstract
In order to leverage the full power of quantum noise squeezing with unavoidable decoherence, a complete understanding of the degradation in the purity of squeezed light is demanded. By implementing machine-learning architecture with a convolutional neural network, we illustrate a fast, robust, and precise quantum state tomography for continuous variables, through the experimentally measured data generated from the balanced homodyne detectors. Compared with the maximum likelihood estimation method, which suffers from time-consuming and overfitting problems, a well-trained machine fed with squeezed vacuum and squeezed thermal states can complete the task of reconstruction of the density matrix in less than one second. Moreover, the resulting fidelity remains as high as 0.99 even when the antisqueezing level is higher than 20 dB. Compared with the phase noise and loss mechanisms coupled from the environment and surrounding vacuum, experimentally, the degradation information is unveiled with machine learning for low and high noisy scenarios, i.e., with the antisqueezing levels at 12 dB and 18 dB, respectively. Our neural network enhanced quantum state tomography provides the metrics to give physical descriptions of every feature observed in the quantum state with a single scan measurement just by varying the local oscillator phase from 0 to 2π and paves a way of exploring large-scale quantum systems in real time.
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Affiliation(s)
- Hsien-Yi Hsieh
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Yi-Ru Chen
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Hsun-Chung Wu
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Hua Li Chen
- Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Jingyu Ning
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Yao-Chin Huang
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Chien-Ming Wu
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Ray-Kuang Lee
- Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan
- Department of Physics, National Tsing Hua University, Hsinchu 30013, Taiwan
- Physics Division, National Center for Theoretical Sciences, Taipei 10617, Taiwan
- Center for Quantum Technology, Hsinchu 30013, Taiwan
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16
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Nakaji K, Yamamoto N. Quantum semi-supervised generative adversarial network for enhanced data classification. Sci Rep 2021; 11:19649. [PMID: 34608219 PMCID: PMC8490428 DOI: 10.1038/s41598-021-98933-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 09/16/2021] [Indexed: 11/09/2022] Open
Abstract
In this paper, we propose the quantum semi-supervised generative adversarial network (qSGAN). The system is composed of a quantum generator and a classical discriminator/classifier (D/C). The goal is to train both the generator and the D/C, so that the latter may get a high classification accuracy for a given dataset. Hence the qSGAN needs neither any data loading nor to generate a pure quantum state, implying that qSGAN is much easier to implement than many existing quantum algorithms. Also the generator can serve as a stronger adversary than a classical one thanks to its rich expressibility, and it is expected to be robust against noise. These advantages are demonstrated in a numerical simulation.
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Affiliation(s)
- Kouhei Nakaji
- Department of Applied Physics and Physico-Informatics and Quantum Computing Center, Keio University, Hiyoshi 3-14-1, Kohoku, Yokohama, 223-8522, Japan.
| | - Naoki Yamamoto
- Department of Applied Physics and Physico-Informatics and Quantum Computing Center, Keio University, Hiyoshi 3-14-1, Kohoku, Yokohama, 223-8522, Japan
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