Integrated quantum photonic circuits are becoming increasingly complex. Accurate calibration of device parameters and detailed characterization of the prepared quantum states are critically important for future progress. Here we report on an effective experimental calibration method based on Bayesian updating and Markov chain Monte Carlo integration. We use this calibration technique to characterize a two qubit chip and extract the reflectivities of its directional couplers. An average quantum state tomography fidelity of 93.79 ± 1.05% against the four Bell states is achieved. Furthermore, comparing the measured density matrices against a model using the non-ideal device parameters derived from the calibration we achieve an average fidelity of 97.57 ± 0.96%. This pinpoints non-ideality of chip parameters as a major factor in the decrease of Bell state fidelity. We also perform quantum state tomography for Bell states while continuously varying photon distinguishability and find excellent agreement with theory.
Integrated quantum photonic circuits are becoming increasingly complex. Accurate calibration of device parameters and detailed characterization of the prepared quantum states are critically important for future progress. Here we report on an effective experimental calibration method based on Bayesian updating and Markov chain Monte Carlo integration. We use this calibration technique to characterize a two qubit chip and extract the reflectivities of its directional couplers. An average quantum state tomography fidelity of 93.79 ± 1.05% against the four Bell states is achieved. Furthermore, comparing the measured density matrices against a model using the non-ideal device parameters derived from the calibration we achieve an average fidelity of 97.57 ± 0.96%. This pinpoints non-ideality of chip parameters as a major factor in the decrease of Bell state fidelity. We also perform quantum state tomography for Bell states while continuously varying photon distinguishability and find excellent agreement with theory.
Abstract Quantum information systems are on a path to vastly exceed the complexity of any classical device. The number of entangled qubits in quantum devices is rapidly increasing, and the information required to fully describe these systems scales exponentially with qubit number. This scaling is the key benefit of quantum systems, however it also presents a severe challenge. To characterize such systems typically requires an exponentially long sequence of different measurements, becoming highly resource demanding for large numbers of qubits. Here we propose and demonstrate a novel and scalable method for characterizing quantum systems based on expanding a multi-photon state to larger dimensionality. We establish that the complexity of this new measurement technique only scales linearly with the number of qubits, while providing a tomographically complete set of data without a need for reconfigurability. We experimentally demonstrate an integrated photonic chip capable of measuring two- and three-photon quantum states with statistical reconstruction fidelity of 99.71%.
Summary form only given. Full characterization of quantum states is increasingly important as ever more complex quantum systems are realized. Quantum state tomography of these systems is a difficult task because individual measurements only give partial information about the underlying density matrix of the system. Thus, conventionally the measurement apparatus must be reconfigured to take multiple measurements in order to fully determine the density matrix. It is accepted that for an W-qubit system an exponentially increasing number of measurements are needed [1]. As a consequence, the complexity of typical quantum state tomography grows exponentially with increasing photon number, which makes characterizing large quantum systems highly challenging. We formulate a concept of on-chip quantum tomography where the complexity scales linearly with the number of photons. This is achieved by using a single specially designed non-reconfigurable linear optical circuit as in Fig. 1(a). Crucially, the number of waveguides in this circuit scales linearly with the number of photons in the state to be measured. Furthermore, our approach is simple and robust, and we demonstrate it experimentally with a specially designed photonic chip.
Reconstructing the state of a quantum system, called quantum state tomography, is an essential task in quantum technologies.Standard methods are inefficient, as they scale exponentially with the number of qubits.Better scaling methods, such as SIC-POVM tomography, are not yet widespread due to their measurement complexity.As such, there is an urgent need to find efficient and compact devices for performing tomography of photonic states.Here we present simulations of photonic devices implementing SIC-POVM tomography in photonic chips.We obtain fidelities F = 0.8452 (for qubits) and F = 0.7609 (for qutrits).Our results show that we can design efficient and compact SIC-POVM tomography modules for integrated-photonics quantum chips.Since integrated photonics is a major quantum technology platform, we expect our results to be instrumental in the future development of compact, costeffective quantum devices.Applications include quantum communications, quantum computing, quantum sensing and quantum imaging.
The design, fabrication, and detailed calibration of essential building blocks towards fully integrated linear-optics quantum computation are discussed. Photonic devices are made from silicon nitride rib waveguides, where measurements on ring resonators show small propagation losses. Directional couplers are designed to be insensitive to fabrication variations. Their offset and coupling lengths are measured, as well as the phase difference between the transmitted and reflected light. With careful calibrations, the insertion loss of the directional couplers is found to be small. Finally, an integrated controlled-NOT circuit is characterized by measuring the transmission through different combinations of inputs and outputs. The gate fidelity for the CNOT operation with this circuit is estimated to be 99.81% after post selection. This high fidelity is due to our robust design, good fabrication reproducibility, and extensive characterizations.
With the development of research on integrated photonic quantum information processing, the integration level of the integrated quantum photonic circuits has been increasing continuously, which makes the calibration of the phase shifters on the chip increasingly difficult. For the calibration of multiple cascaded phase shifters that is not easy to be decoupled, the resources consumed by conventional brute force methods increase exponentially with the number of phase shifters, making it impossible to calibrate a relatively large number of cascaded phase shifters. In this work, we experimentally validate an efficient method for calibrating cascaded phase shifters that achieves an exponential increase in calibration efficiency compared to the conventional method, thus solving the calibration problem for multiple cascaded phase shifters. Specifically, we experimentally calibrate an integrated quantum photonic circuit with nine cascaded phase shifters and achieve a high-precision calibration with an average fidelity of 99.26%.
Bayesian inference is a widely used technique for real-time characterization of quantum systems. It excels in experimental characterization in the low data regime, and when the measurements have degrees of freedom. A decisive factor for its performance is the numerical representation of the Bayesian probability distributions. In this work, we explore advanced statistical methods for this purpose, and numerically analyze their performance against the state-of-the-art in quantum parameter learning. In particular, we consider sequential importance resampling, tempered likelihood estimation, Markov Chain Monte Carlo, random walk Metropolis (RWM), Hamiltonian Monte Carlo (HMC) and variants (stochastic gradients with and without friction, energy conserving subsampling), block pseudo-marginal Metropolis-Hastings with subsampling, hybrid HMC-RWM approaches, and Gaussian rejection filtering. We demonstrate advantages of these approaches over existing ones, namely robustness under multi-modality and high dimensionality. We apply these algorithms to the calibration of superconducting qubits from IBMQ, surpassing the standard quantum limit and achieving better results than Qiskit's default tools. In Hahn echo and Ramsey experiments, we reduce the uncertainty by factors of 10 and 3 respectively, without increasing the number of measurements; conversely, we match the performance of Qiskit's methods while using up to to 99.5% less experimental data. We additionally investigate the roles of adaptivity, dataset ordering and heuristics in quantum characterization. Our findings have applications in challenging quantum characterization tasks, namely learning the dynamics of open quantum systems.