Patrick Coles

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). In this work, we prove the first out-of-distribution generalization guarantees in QML, where we require a trained model to perform well even on testing data drawn from a distribution different from the training data distribution. Namely, we establish out-of-distribution generalization for the task of learning an unknown unitary using a quantum neural network and for a broad class of training and testing distributions. In particular, we show that one can learn the action of a unitary on entangled states using only product state training data. Since product states can be prepared using only single-qubit gates, this advances the near-term prospects of QML for learning quantum dynamics, and further opens up new methods for both the classical and quantum compilation of quantum circuits. Based on these insights, we propose a QML-based algorithm for simulating quantum dynamics on near-term quantum hardware and rigorously prove its resource-efficiency in terms of qubit and training data requirements. We also demonstrate the viability of this algorithm through numerical experiments, both in classical simulations and on quantum hardware. Finally, we embed this algorithm in a broader framework for using QML methods for quantum dynamical simulation on NISQ devices.

Abstract Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data $\{X_i^{noisy},X_i^{exact}\}$ via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where $X_i^{noisy}$ and $X_i^{exact}$ are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.

Quantum key distribution (QKD) protocols with threshold detectors are driving high-performance QKD demonstrations. The corresponding security proofs usually assume that all physical detectors have the same detection efficiency. However, the efficiencies of the detectors used in practice might show a mismatch depending on the manufacturing and setup of these detectors. A mismatch can also be induced as the different spatial-temporal modes of an incoming signal might couple differently to a detector. Here we develop a method that allows to provide security proofs without the usual assumption. Our method can take the detection-efficiency mismatch into account without having to restrict the attack strategy of the adversary. Especially, we do not rely on any photon-number cut-off of incoming signals such that our security proof is complete. Though we consider polarization encoding in the demonstration of our method, the method applies to a variety of coding mechanisms, including time-bin encoding, and also allows for general manipulations of the spatial-temporal modes by the adversary. We thus can close the long-standing question how to provide a valid, complete security proof of a QKD setup with characterized efficiency mismatch. Our method also shows that in the absence of efficiency mismatch, the key rate increases if the loss due to detection inefficiency is assumed to be outside of the adversary's control, as compared to the view where for a security proof this loss is attributed to the action of the adversary.

In this work, we present an open-source software platform that calculates key rate for general QKD protocols, building upon the numerical framework proposed by our group that can perform automated security proof of QKD protocols. The software platform is fully modularized with mutually independent modules for descriptions of protocols/channels, solvers for bounding key rate, and parameter optimization algorithms. It currently supports BB84 and measurement-device-independent QKD (including decoy states), as well as discrete-modulated continuous variable QKD. It also supports finite-size analysis for non-decoy-state protocols. We hope that the open-sourcing can attract theorists to test new protocols and/or contribute to new solvers, as well as appeal to experimentalists who wish to analyze their data or optimize parameters for new experiments.

A numerical approach for the calculation of QKD key rates allows a uniform framework to be applied to general QKD protocols. Based on our group's previous work, we would like to build a universal software platform that is fully modularized and user-friendly, where one can easily swap in and out different QKD protocol descriptions, channel simulation models or experimental data, backend numerical solvers, and parameter optimization algorithms. Our goal is to build an open-source platform that can be both useful for theorists testing new protocols as well as experimentalists looking for optimal parameters or analyzing their experimental data.