We used deep learning techniques to construct various models for reconstructing quantum states from a given set of coincidence measurements.Through simulations,we have demonstrated that our approach generates function...We used deep learning techniques to construct various models for reconstructing quantum states from a given set of coincidence measurements.Through simulations,we have demonstrated that our approach generates functionally equivalent reconstructed states for a wide range of pure and mixed input states.Compared with traditional methods,our system offers the advantage of faster speed.Additionally,by training our system with measurement results containing simulated noise sources,the system shows a significant improvement in average fidelity compared with typical reconstruction methods.We also found that constraining the variational manifold to physical states,i.e.,positive semi-definite density matrices,greatly enhances the quality of the reconstructed states in the presence of experimental imperfections and noise.Finally,we validated the correctness and superiority of our model by using data generated on IBM Quantum Platform,a real quantum computer.展开更多
Human experts cannot efficiently access physical information of a quantum many-body states by simply "reading"its coefficients, but have to reply on the previous knowledge such as order parameters and quantu...Human experts cannot efficiently access physical information of a quantum many-body states by simply "reading"its coefficients, but have to reply on the previous knowledge such as order parameters and quantum measurements.We demonstrate that convolutional neural network(CNN) can learn from coefficients of many-body states or reduced density matrices to estimate the physical parameters of the interacting Hamiltonians, such as coupling strengths and magnetic fields, provided the states as the ground states. We propose QubismNet that consists of two main parts: the Qubism map that visualizes the ground states(or the purified reduced density matrices) as images, and a CNN that maps the images to the target physical parameters. By assuming certain constraints on the training set for the sake of balance, QubismNet exhibits impressive powers of learning and generalization on several quantum spin models. While the training samples are restricted to the states from certain ranges of the parameters, QubismNet can accurately estimate the parameters of the states beyond such training regions. For instance, our results show that QubismNet can estimate the magnetic fields near the critical point by learning from the states away from the critical vicinity. Our work provides a data-driven way to infer the Hamiltonians that give the designed ground states, and therefore would benefit the existing and future generations of quantum technologies such as Hamiltonian-based quantum simulations and state tomography.展开更多
Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing.While various strategies using distinct quantum measurements have been proposed for overlap estimation,t...Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing.While various strategies using distinct quantum measurements have been proposed for overlap estimation,the lack of experimental benchmarks on estimation precision limits strategy selection in different situations.Here we compare the performance of four practical strategies for overlap estimation,including tomography-tomography,tomographyprojection,Schur collective measurement and optical swap test using photonic quantum systems.We encode the quantum states on the polarization and path degrees of freedom of single photons.The corresponding measurements are performed by photon detection on certain modes following single-photon mode transformation or two-photon interference.We further propose an adaptive strategy with optimized precision in full-range overlap estimation.Our results shed new light on extracting the parameter of interest from quantum systems,prompting the design of efficientquantum protocols.展开更多
We describe a mathematical structure which corresponds to the eigenstates of a density operator. For an unknown density operator, we propose an estimating procedure which uses successive ＂yes/no＂ measurements to sca...We describe a mathematical structure which corresponds to the eigenstates of a density operator. For an unknown density operator, we propose an estimating procedure which uses successive ＂yes/no＂ measurements to scan the Bloch sphere and approximately yields the eigenstates. This result is based on the quantum method of types and implies a relationship between the typical subspace and the Young frame.展开更多
The variational quantum eigensolver(VQE) is emerging as a cornerstone algorithm in the era of noisy intermediatescale quantum(NISQ) devices,which offers a practical pathway for solving complex quantum problems using h...The variational quantum eigensolver(VQE) is emerging as a cornerstone algorithm in the era of noisy intermediatescale quantum(NISQ) devices,which offers a practical pathway for solving complex quantum problems using hybrid quantum-classical frameworks.Initially proposed to estimate the ground state energies of quantum systems,VQE combines the quantum circuits with the classical optimization approaches,harnessing the strengths of both computational paradigms [1].展开更多
文摘We used deep learning techniques to construct various models for reconstructing quantum states from a given set of coincidence measurements.Through simulations,we have demonstrated that our approach generates functionally equivalent reconstructed states for a wide range of pure and mixed input states.Compared with traditional methods,our system offers the advantage of faster speed.Additionally,by training our system with measurement results containing simulated noise sources,the system shows a significant improvement in average fidelity compared with typical reconstruction methods.We also found that constraining the variational manifold to physical states,i.e.,positive semi-definite density matrices,greatly enhances the quality of the reconstructed states in the presence of experimental imperfections and noise.Finally,we validated the correctness and superiority of our model by using data generated on IBM Quantum Platform,a real quantum computer.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 12004266, 11834014 and 11975050)the Beijing Natural Science Foundation (Grant Nos. 1192005 and Z180013)+1 种基金the Foundation of Beijing Education Committees (Grant No.KM202010028013)the Academy for Multidisciplinary Studies,Capital Normal University。
文摘Human experts cannot efficiently access physical information of a quantum many-body states by simply "reading"its coefficients, but have to reply on the previous knowledge such as order parameters and quantum measurements.We demonstrate that convolutional neural network(CNN) can learn from coefficients of many-body states or reduced density matrices to estimate the physical parameters of the interacting Hamiltonians, such as coupling strengths and magnetic fields, provided the states as the ground states. We propose QubismNet that consists of two main parts: the Qubism map that visualizes the ground states(or the purified reduced density matrices) as images, and a CNN that maps the images to the target physical parameters. By assuming certain constraints on the training set for the sake of balance, QubismNet exhibits impressive powers of learning and generalization on several quantum spin models. While the training samples are restricted to the states from certain ranges of the parameters, QubismNet can accurately estimate the parameters of the states beyond such training regions. For instance, our results show that QubismNet can estimate the magnetic fields near the critical point by learning from the states away from the critical vicinity. Our work provides a data-driven way to infer the Hamiltonians that give the designed ground states, and therefore would benefit the existing and future generations of quantum technologies such as Hamiltonian-based quantum simulations and state tomography.
基金supported by National Natural Science Foundation of China(GrantsNo.U24A2017,No.12347104 and No.12461160276)the National Key Researchand Development Program of China(Grants No.2023YFC2205802)+1 种基金Natural Science Foundation of Jiangsu Province(Grants No.BK20243060 and No.BK20233001)in part by State Key Laboratory of Advanced Optical Communication Systems and Networks,China.
文摘Accurately estimating the overlap between quantum states is a fundamental task in quantum information processing.While various strategies using distinct quantum measurements have been proposed for overlap estimation,the lack of experimental benchmarks on estimation precision limits strategy selection in different situations.Here we compare the performance of four practical strategies for overlap estimation,including tomography-tomography,tomographyprojection,Schur collective measurement and optical swap test using photonic quantum systems.We encode the quantum states on the polarization and path degrees of freedom of single photons.The corresponding measurements are performed by photon detection on certain modes following single-photon mode transformation or two-photon interference.We further propose an adaptive strategy with optimized precision in full-range overlap estimation.Our results shed new light on extracting the parameter of interest from quantum systems,prompting the design of efficientquantum protocols.
基金Supported by the National Natural Science Foundation of China(61271174,61372076,61301178)
文摘We describe a mathematical structure which corresponds to the eigenstates of a density operator. For an unknown density operator, we propose an estimating procedure which uses successive ＂yes/no＂ measurements to scan the Bloch sphere and approximately yields the eigenstates. This result is based on the quantum method of types and implies a relationship between the typical subspace and the Young frame.
文摘The variational quantum eigensolver(VQE) is emerging as a cornerstone algorithm in the era of noisy intermediatescale quantum(NISQ) devices,which offers a practical pathway for solving complex quantum problems using hybrid quantum-classical frameworks.Initially proposed to estimate the ground state energies of quantum systems,VQE combines the quantum circuits with the classical optimization approaches,harnessing the strengths of both computational paradigms [1].
