We propose Hamiltonian quantum generative adversarial networks (HQuGANs) to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is...
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We propose Hamiltonian quantum generative adversarial networks (HQuGANs) to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the success of classical generative adversarial networks in learning high-dimensional distributions. The quantum optimal control approach not only makes the algorithm naturally adaptable to the experimental constraints of near-term hardware, but also offers a more natural characterization of overparameterization compared to the circuit model. We numerically demonstrate the capabilities of the proposed framework to learn various highly entangled many-body quantum states, using simple two-body Hamiltonians and under experimentally relevant constraints such as low-bandwidth controls. We analyze the computational cost of implementing HQuGANs on quantumcomputers and show how the framework can be extended to learn quantum dynamics. Furthermore, we introduce a cost function that circumvents the problem of mode collapse that prevents convergence of HQuGANs and demonstrate how to accelerate the convergence of them when generating a pure state.
Nonlinear optics plays an important role in many areas of science and *** advance of nonlinear optics is empowered by the discovery and utilization of materials with growing optical *** we demonstrate an indium galliu...
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Nonlinear optics plays an important role in many areas of science and *** advance of nonlinear optics is empowered by the discovery and utilization of materials with growing optical *** we demonstrate an indium gallium phosphide(InGaP)integrated photonics platform for broadband,ultra-efficient second-order nonlinear *** InGaP nanophotonic waveguide enables second-harmonic generation with a normalized efficiency of 128,000%/W/cm^(2)at 1.55μm pump wavelength,nearly two orders of magnitude higher than the state of the art in the telecommunication C ***,we realize an ultra-bright,broadband time-energy entangled photon source with a pair generation rate of 97 GHz/mW and a bandwidth of 115 nm centered at the telecommunication C *** InGaP entangled photon source shows high coincidence-to-accidental counts ratio CAR>10^(4)and two-photon interference visibility>98%.The InGaP second-order nonlinear photonics platform will have wide-ranging implications for non-classical light generation,optical signal processing,and quantum networking.
We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supe...
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We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.
We construct a fault-tolerant quantum error-correcting protocol based on a qubit encoded in a large spin qudit using a spin-cat code, analogous to the continuous-variable cat encoding. With this, we can correct the do...
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We construct a fault-tolerant quantum error-correcting protocol based on a qubit encoded in a large spin qudit using a spin-cat code, analogous to the continuous-variable cat encoding. With this, we can correct the dominant error sources, namely processes that can be expressed as error operators that are linear or quadratic in the components of angular momentum. Such codes tailored to dominant error sources can exhibit superior thresholds and lower resource overheads when compared to those designed for unstructured noise models. A key component is the cnot gate that preserves the rank of spherical tensor operators. Categorizing the dominant errors as phase and amplitude errors, we demonstrate how phase errors, analogous to phase-flip errors for qubits, can be effectively corrected. Furthermore, we propose a measurement-free error-correction scheme to address amplitude errors without relying on syndrome measurements. Through an in-depth analysis of logical cnot gate errors, we establish that the fault-tolerant threshold for error correction in the spin-cat encoding surpasses that of standard qubit-based encodings. We consider a specific implementation based on neutral-atom quantum computing, with qudits encoded in the nuclear spin of 87Sr, and show how to generate the universal gate set, including the rank-preserving cnot gate, using quantumcontrol and the Rydberg blockade. These findings pave the way for encoding a qubit in a large spin with the potential to achieve fault tolerance, high threshold, and reduced resource overhead in quantuminformation processing.
Graph states play an important role in quantuminformation theory through their connection to measurement-based computing and error correction. Prior work revealed elegant connections between the graph structure of th...
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Graph states play an important role in quantuminformation theory through their connection to measurement-based computing and error correction. Prior work revealed elegant connections between the graph structure of these states and their multipartite entanglement. We continue this line of investigation by identifying additional entanglement properties for certain types of graph states. From the perspective of tensor theory, we tighten both upper and lower bounds on the tensor rank of odd ring states (|R2n+1〉) to read 2n+1≤rank(|R2n+1〉)≤3×2n−1. Next we show that several multipartite extensions of bipartite entanglement measures are dichotomous for graph states based on the connectivity of the corresponding graph. Finally, we give a simple graph rule for computing the n-tangle τn.
We measure visibility in fiber Sagnac interferometers up to 200 km using CW light. Results show visibility is backscattering-limited after 60 km. We obtain 90% visibility in 200 km fiber using a burst-patterning techn...
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We measure visibility in fiber Sagnac interferometers up to 200 km using CW light. Results show visibility is backscattering-limited after 60 km. We obtain 90% visibility in 200 km fiber using a burst-patterning techn...
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quantum computation and optimization have recently garnered considerable attention, with a noticeable focus on their floating-point and arithmetic designs. In classical computing, numerical optimization problems are c...
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This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monoton...
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This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value *** method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing *** numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.
Electro-optic phase modulators are commonly used for polarization and phase encoding in quantum key distribution. Here, a novel state preparation flaw which arises during high speed electro-optic phase modulation is i...
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