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Register a new userQuantum--Classical Interface Based on Single Flux Quantum Digital Logic
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We describe an approach to the integrated control and measurement of a large-scale superconducting multiqubit circuit using a proximal coprocessor based on the Single Flux Quantum (SFQ) digital logic family. Coherent control is realized by irradiating the qubits directly with classical bitstreams derived from optimal control theory. Qubit measurement is performed by a Josephson photon counter, which provides access to the classical result of projective quantum measurement at the millikelvin stage. We analyze the power budget and physical footprint of the SFQ coprocessor and discuss challenges and opportunities associated with this approach.
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We describe an experimental effort designing and deploying error-robust single-qubit operations using a cloud-based quantum computer and analog-layer programming access. We design numerically-optimized pulses that implement target operations and exhibit robustness to various error processes including dephasing noise, instabilities in control amplitudes, and crosstalk. Pulse optimization is performed using a flexible optimization package incorporating a device model and physically-relevant constraints (e.g. bandwidth limits on the transmission lines of the dilution refrigerator housing IBM Quantum hardware). We present techniques for conversion and calibration of physical Hamiltonian definitions to pulse waveforms programmed via Qiskit Pulse and compare performance against hardware default DRAG pulses on a five-qubit device. Experimental measurements reveal default DRAG pulses exhibit coherent errors an order of magnitude larger than tabulated randomized-benchmarking measurements; solutions designed to be robust against these errors outperform hardware-default pulses for all qubits across multiple metrics. Experimental measurements demonstrate performance enhancements up to: $sim10times$ single-qubit gate coherent-error reduction; $sim5times$ average coherent-error reduction across a five qubit system; $sim10times$ increase in calibration window to one week of valid pulse calibration; $sim12times$ reduction gate-error variability across qubits and over time; and up to $sim9times$ reduction in single-qubit gate error (including crosstalk) in the presence of fully parallelized operations. Randomized benchmarking reveals error rates for Clifford gates constructed from optimized pulses consistent with tabulated $T_{1}$ limits, and demonstrates a narrowing of the distribution of outcomes over randomizations associated with suppression of coherent-errors.
We investigate dynamical properties of a quantum generalization of classical reversible Boolean networks. The state of each node is encoded as a single qubit, and classical Boolean logic operations are supplemented by controlled bit-flip and Hadamard operations. We consider synchronous updating schemes in which each qubit is updated at each step based on stored values of the qubits from the previous step. We investigate the periodic or quasiperiodic behavior of quantum networks, and we analyze the propagation of single site perturbations through the quantum networks with input degree one. A non-classical mechanism for perturbation propagation leads to substantially different evolution of the Hamming distance between the original and perturbed states.
Oscillating magnetic fields and field gradients can be used to implement single-qubit rotations and entangling multi-qubit quantum gates for trapped-ion quantum information processing (QIP). With fields generated by currents in microfabricated surface-electrode traps, it should be possible to achieve gate speeds that are comparable to those of optically induced gates for realistic distances between the ion crystal and the electrode surface. Magnetic-field-mediated gates have the potential to significantly reduce the overhead in laser beam control and motional state initialization compared to current QIP experiments with trapped ions and will eliminate spontaneous scattering, a fundamental source of decoherence in laser-mediated gates.
One key step in performing quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding. Traditional principle component analysis (PCA) and neural networks have been used to perform this task; however, the classical and quantum layers are usually trained separately. A framework that allows for a better integration of the two key components is thus highly desirable. Here we introduce a hybrid model combining the quantum-inspired tensor networks (TN) and the variational quantum circuits (VQC) to perform supervised learning tasks, which allows for an end-to-end training. We show that a matrix product state based TN with low bond dimensions performs better than PCA as a feature extractor to compress data for the input of VQCs in the binary classification of MNIST dataset. The architecture is highly adaptable and can easily incorporate extra quantum resource when available.
Increasing control of single photons enables new applications of photonic quantum-enhanced technology and further experimental exploration of fundamental quantum phenomena. Here, we demonstrate quantum logic using narrow linewidth photons that are produced under nearly perfect quantum control from a single ^87Rb atom strongly coupled to a high-finesse cavity. We use a controlled- NOT gate integrated into a photonic chip to entangle these photons, and we observe non-classical correlations between events separated by periods exceeding the travel time across the chip by three orders of magnitude. This enables quantum technology that will use the properties of both narrowband single photon sources and integrated quantum photonics, such as networked quantum computing, narrow linewidth quantum enhanced sensing and atomic memories.
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