Weak measurement amplification,which is considered as a very promising scheme in precision measurement,has been applied to various small physical quantities estimations.Since many physical quantities can be converted ...Weak measurement amplification,which is considered as a very promising scheme in precision measurement,has been applied to various small physical quantities estimations.Since many physical quantities can be converted into phase signals,it is interesting and important to consider measuring small longitudinal phase shifts by using weak measurement.Here,we propose and experimentally demonstrate a novel weak measurement amplification-based small longitudinal phase estimation,which is suitable for polarization interferometry.We realize one order of magnitude amplification measurement of a small phase signal directly introduced by a liquid crystal variable retarder and show that it is robust to the imperfection of interference.Besides,we analyze the effect of magnification error which is never considered in the previous works,and find the constraint on the magnification.Our results may find important applications in high-precision measurements,e.g.,gravitational wave detection.展开更多
Research of Maxwell demon and quantum entanglement is important because of its foundational significance in physics and its potential applications in quantum information. Previous studies on the Maxwell demon have pri...Research of Maxwell demon and quantum entanglement is important because of its foundational significance in physics and its potential applications in quantum information. Previous studies on the Maxwell demon have primarily focused on thermodynamics, taking into account quantum correlations. Here we consider from another perspective and ask whether quantum non-locality correlations can be simulated by performing work. The Maxwell demon-assisted Einstein–Podolsky–Rosen(EPR) steering is thus proposed, which implies a new type of loophole. The application of Landauer's erasure principle suggests that the only way to close this loophole during a steering task is by continuously monitoring the heat fluctuation of the local environment by the participant.We construct a quantum circuit model of Maxwell demon-assisted EPR steering, which can be demonstrated by current programmable quantum processors, such as superconducting quantum computers. Based on this quantum circuit model, we obtain a quantitative formula describing the relationship between energy dissipation due to the work of the demon and quantum non-locality correlation. The result is of great physical interest because it provides a new way to explore and understand the relationship between quantum non-locality, information, and thermodynamics.展开更多
The historical significance of the Stern–Gerlach(SG)experiment lies in its provision of the initial evidence for space quantization.Over time,its sequential form has evolved into an elegant paradigm that effectively ...The historical significance of the Stern–Gerlach(SG)experiment lies in its provision of the initial evidence for space quantization.Over time,its sequential form has evolved into an elegant paradigm that effectively illustrates the fundamental principles of quantum theory.To date,the practical implementation of the sequential SG experiment has not been fully achieved.In this study,we demonstrate the capability of programmable quantum processors to simulate the sequential SG experiment.The specific parametric shallow quantum circuits,which are suitable for the limitations of current noisy quantum hardware,are given to replicate the functionality of SG devices with the ability to perform measurements in different directions.Surprisingly,it has been demonstrated that Wigner’s SG interferometer can be readily implemented in our sequential quantum circuit.With the utilization of the identical circuits,it is also feasible to implement Wheeler’s delayed-choice experiment.We propose the utilization of cross-shaped programmable quantum processors to showcase sequential experiments,and the simulation results demonstrate a strong alignment with theoretical predictions.With the rapid advancement of cloud-based quantum computing,such as BAQIS Quafu,it is our belief that the proposed solution is well-suited for deployment on the cloud,allowing for public accessibility.Our findings not only expand the potential applications of quantum computers,but also contribute to a deeper comprehension of the fundamental principles underlying quantum theory.展开更多
Wavefunction is a fundamental concept of quantum theory.Recent studies have shown surprisingly that wavefunction can be directly reconstructed via the measurement of weak value.The weak value based direct wavefunction...Wavefunction is a fundamental concept of quantum theory.Recent studies have shown surprisingly that wavefunction can be directly reconstructed via the measurement of weak value.The weak value based direct wavefunction reconstruction not only gives the operational meaning of wavefunction,but also provides the possibility of realizing holographic imaging with a totally new quantum approach.Here,we review the basic background knowledge of weak value based direct wavefunction reconstruction combined with recent experimental demonstrations.The main purpose of this work focuses on the idea of holographic imaging via direct wavefunction reconstruction.Since research on this topic is still in its early stage,we hope that this work can attract interest in the field of traditional holographic imaging.In addition,the wavefunction holographic imaging may find important applications in quantum information science.展开更多
We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and c...We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.展开更多
With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate...With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 92065113, 11904357, 62075208, and 12174367)the Innovation Programme for Quantum Science and Technology (Grant No. 2021ZD0301604)+1 种基金the National Key Research and Development Program of China (Grant No. 2021YFE0113100)supported by Beijing Academy of Quantum Information Sciences
文摘Weak measurement amplification,which is considered as a very promising scheme in precision measurement,has been applied to various small physical quantities estimations.Since many physical quantities can be converted into phase signals,it is interesting and important to consider measuring small longitudinal phase shifts by using weak measurement.Here,we propose and experimentally demonstrate a novel weak measurement amplification-based small longitudinal phase estimation,which is suitable for polarization interferometry.We realize one order of magnitude amplification measurement of a small phase signal directly introduced by a liquid crystal variable retarder and show that it is robust to the imperfection of interference.Besides,we analyze the effect of magnification error which is never considered in the previous works,and find the constraint on the magnification.Our results may find important applications in high-precision measurements,e.g.,gravitational wave detection.
基金the support from the Natural Science Foundation of China (Grant No. 92365206)the support from the Fundamental Research Funds for the Central Universitiessupported by the National Natural Science Foundation of China (Grant No. 92065113)。
文摘Research of Maxwell demon and quantum entanglement is important because of its foundational significance in physics and its potential applications in quantum information. Previous studies on the Maxwell demon have primarily focused on thermodynamics, taking into account quantum correlations. Here we consider from another perspective and ask whether quantum non-locality correlations can be simulated by performing work. The Maxwell demon-assisted Einstein–Podolsky–Rosen(EPR) steering is thus proposed, which implies a new type of loophole. The application of Landauer's erasure principle suggests that the only way to close this loophole during a steering task is by continuously monitoring the heat fluctuation of the local environment by the participant.We construct a quantum circuit model of Maxwell demon-assisted EPR steering, which can be demonstrated by current programmable quantum processors, such as superconducting quantum computers. Based on this quantum circuit model, we obtain a quantitative formula describing the relationship between energy dissipation due to the work of the demon and quantum non-locality correlation. The result is of great physical interest because it provides a new way to explore and understand the relationship between quantum non-locality, information, and thermodynamics.
基金supported by Beijing Academy of Quantum Information Sciencessupported by the State Key Laboratory of Low Dimensional Quantum Physics+2 种基金the Start-up Fund provided by Tsinghua Universitythe financial support provided by the National Natural Science Foundation of China(Grant No.92065113)the Anhui Initiative in Quantum Information Technologies。
文摘The historical significance of the Stern–Gerlach(SG)experiment lies in its provision of the initial evidence for space quantization.Over time,its sequential form has evolved into an elegant paradigm that effectively illustrates the fundamental principles of quantum theory.To date,the practical implementation of the sequential SG experiment has not been fully achieved.In this study,we demonstrate the capability of programmable quantum processors to simulate the sequential SG experiment.The specific parametric shallow quantum circuits,which are suitable for the limitations of current noisy quantum hardware,are given to replicate the functionality of SG devices with the ability to perform measurements in different directions.Surprisingly,it has been demonstrated that Wigner’s SG interferometer can be readily implemented in our sequential quantum circuit.With the utilization of the identical circuits,it is also feasible to implement Wheeler’s delayed-choice experiment.We propose the utilization of cross-shaped programmable quantum processors to showcase sequential experiments,and the simulation results demonstrate a strong alignment with theoretical predictions.With the rapid advancement of cloud-based quantum computing,such as BAQIS Quafu,it is our belief that the proposed solution is well-suited for deployment on the cloud,allowing for public accessibility.Our findings not only expand the potential applications of quantum computers,but also contribute to a deeper comprehension of the fundamental principles underlying quantum theory.
基金supported by the Beijing Academy of Quantum Information Sciencessupported by the National Natural Science Foundation of China(Grant Nos.11674306 and 92065113)the University Synergy Innovation Program of Anhui Province(Grant No.GXXT-2022-039)。
文摘Wavefunction is a fundamental concept of quantum theory.Recent studies have shown surprisingly that wavefunction can be directly reconstructed via the measurement of weak value.The weak value based direct wavefunction reconstruction not only gives the operational meaning of wavefunction,but also provides the possibility of realizing holographic imaging with a totally new quantum approach.Here,we review the basic background knowledge of weak value based direct wavefunction reconstruction combined with recent experimental demonstrations.The main purpose of this work focuses on the idea of holographic imaging via direct wavefunction reconstruction.Since research on this topic is still in its early stage,we hope that this work can attract interest in the field of traditional holographic imaging.In addition,the wavefunction holographic imaging may find important applications in quantum information science.
基金supported by the National Natural Science Foundation of China(Grant No.92365206)the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)+1 种基金supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.
基金supported by the Beijing Academy of Quantum Information Sciencessupported by the National Natural Science Foundation of China(Grant No.92365206)+2 种基金the support of the China Postdoctoral Science Foundation(Certificate Number:2023M740272)supported by the National Natural Science Foundation of China(Grant No.12247168)China Postdoctoral Science Foundation(Certificate Number:2022TQ0036)。
文摘With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.
