Quantum Physics (quant-ph)

Abstract: Entanglement, a fundamental feature of quantum mechanics, has long been recognized as a valuable resource in enabling secure communications and surpassing classical limits. However, previous research has primarily concentrated on static entangled states generated at a single point in time, overlooking the crucial role of the quantum dynamics responsible for creating such states. Here, we propose a framework for investigating entanglement across multiple time points, termed temporal entanglement, and demonstrate that the performance of a quantum network in transmitting information is inherently dependent on its temporal entanglement. Through case studies, we showcase the capabilities of our framework in enhancing conventional quantum teleportation and achieving exponential performance growth in the protocol of quantum repeaters. Additionally, our framework effectively doubles the communication distance in certain noise models. Our results address the longstanding question surrounding temporal entanglement within non-Markovian processes and its impact on quantum communication, thereby pushing the frontiers of quantum information science.

Abstract: Anomaly detection in Endpoint Detection and Response is a critical task in cybersecurity programs of large companies. With a rapidly growing amount of data and the omnipresence of zero-day attacks, manual and rule-based detection techniques are no longer eligible in practice. While classical machine learning approaches to this problem exist, they frequently show unsatisfactory performance in differentiating malicious from benign anomalies. A promising approach to attain superior generalization than currently employed machine learning techniques are quantum generative models. Allowing for the largest representation of data on available quantum hardware, we investigate Quantum Annealing based Quantum Boltzmann Machines (QBMs) for the given problem. We contribute the first fully unsupervised approach for the problem of anomaly detection using QBMs and evaluate its performance on a suitable synthetic dataset. Our results indicate that QBMs can outperform their classical analog (i.e., Restricted Boltzmann Machines) in terms of result quality and training steps. When employing Quantum Annealers from D-Wave Systems, we conclude that either more accurate classical simulators or substantially more QPU time is needed to conduct the necessary hyperparameter optimization allowing to replicate our simulation results on quantum hardware.

Abstract: Many promising applications of quantum computing with a provable speedup center around the HHL algorithm. Due to restrictions on the hardware and its significant demand on qubits and gates in known implementations, its execution is prohibitive on near-term quantum computers. Aiming to facilitate such NISQ-implementations, we propose a novel circuit approximation technique that enhances the arithmetic subroutines in the HHL, which resemble a particularly resource-demanding component in small-scale settings. For this, we provide a description of the algorithmic implementation of space-efficient rotations of polynomial functions that do not demand explicit arithmetic calculations inside the quantum circuit. We show how these types of circuits can be reduced in depth by providing a simple and powerful approximation technique. Moreover, we provide an algorithm that converts lookup-tables for arbitrary function rotations into a structure that allows an application of the approximation technique. This allows implementing approximate rotation circuits for many polynomial and non-polynomial functions. Experimental results obtained for realistic early-application dimensions show significant improvements compared to the state-of-the-art, yielding small circuits while achieving good approximations.

Abstract: We demonstrate a property of the quantum 5-qubit stabilizer code that enables the interaction between qubits of different logical layers, and conduct a full density-matrix simulation of an interaction between a logical and a physical qubit. We use the logical qubit as an ancilla and find under which circumstances it gives an advantage over the bare physical ancilla approach, changing the circuit depth and noise level with decoherence processes at play. We use it as a post selection oracle for quantum phase estimation to detect errors propagating from the sensor qubit. Finally, we use our simulation to give noise thresholds both for computation and for sensing a signal using quantum phase estimation that are well within the capabilities of today's hardware.

Abstract: A central problem of development in chemical and pharmaceutical industries is modelling a cheap to evaluate surrogate function, that approximates a given black box function sufficiently well. As state-of-the-art methods from classical machine learning struggle to solve this problem accurately for the typically scarce and noisy datasets in practical applications, investigating novel approaches is of great interest to chemical companies worldwide. We demonstrate that quantum neural networks (QNNs) offer a particularly promising approach to this issue and experimentally support recent theoretical findings indicating their potential to outperform classical equivalents in training on small datasets and noisy data. Our contribution displays the first application centered exploration of using QNNs as surrogate models on higher dimensional, realistic data. In extensive experiments, our QNN significantly outperforms a minimalist classical artificial neural network on noisy and scarce data, displaying a possible advantage of quantum surrogate models empirically. Finally, we demonstrate the performance of current NISQ hardware experimentally and estimate the gate fidelities necessary to replicate our simulation results.

Abstract: Variational Quantum Algorithms are one of the most promising candidates to yield the first industrially relevant quantum advantage. Being capable of arbitrary function approximation, they are often referred to as Quantum Neural Networks (QNNs) when being used in analog settings as classical Artificial Neural Networks (ANNs). Similar to the early stages of classical machine learning, known schemes for efficient architectures of these networks are scarce. Exploring beyond existing design patterns, we propose a reducing-width circuit Ansatz design, which aims at mitigating the common problem of vanishing gradients caused by barren plateaus in the parameter training of QNNs. Our design of gradually width-reduced ansatz-layers is inspired by the similar reduction of layer-width in classical ANNs such as the encoder component in autoencoders. We evaluate our approach in a VQE ansatz to the maximum cut problem and identify its potential for increasingly deep circuits in terms of training time and result quality.

Abstract: We propose a reservoir design, composed of fixed dissipation operators acting each on few local subsystems, to stabilize an approximate GHZ state on n qubits. The main idea is to work out how a previously proposed sequence of two stabilization steps can be applied instead in appropriate (probabilistic) superposition. We examine alternatives to synchronize the superposition using local couplings only, thanks to a chain of "clock" ancillas or to additional levels on the data subsystems. The practical value of these alternatives depends on experimental constraints. They all feature a design tradeoff between approximate stabilization fidelity and protection against perturbations. These proposals illustrate how simple autonomous automata can be implemented in quantum reservoir engineering to replace sequential state preparation procedures. Encoding automaton actions via additional data levels only, appears particularly efficient in this context. Our analysis method, reducing the Lindblad master equation to a Markov chain on virtual output signals, may be of independent interest.

Abstract: The quantum computing paradigm in photonics currently relies on the multi-port interference in linear optical devices, which is intrinsically based on probabilistic measurements outcome and thus non-deterministic. Devising a fully deterministic, universal, and practically achievable quantum computing platform based on integrated photonic circuits is still an open challenge. Here we propose to exploit weakly nonlinear photonic devices to implement deterministic entangling quantum gates, following the definition of dual rail photonic qubits. It is shown that a universal set of single- and two-qubit gates can be designed by a suitable concatenation of few optical interferometric elements, with optimal fidelities arbitrarily close to 100% theoretically demonstrated through a bound constrained optimization algorithm. The actual realization would require the concatenation of a few tens of elementary operations, as well as on-chip optical nonlinearities that are compatible with some of the existing quantum photonic platforms, as it is finally discussed.

Abstract: Quantum discord has been utilised in order to find quantum advantage in an extension of the Clauser, Horne, Shimony, and Holt (CHSH) game. By writing the game explicitly as a Bayesian game, the resulting game is modified such the payoff's are different, and crucially restrictions are imposed on the measurements that Alice and Bob can perform. By imposing these restrictions, it is found that there exists quantum advantage beyond entanglement for a given quantum state. This is shown by decomposing the expected payoff into a classical and quantum term. Optimising over the expected payoff, results in the classical limit being surpassed. This gives an operational framework in order to witness and determine quantum discord.

Abstract: High-performance quantum light sources based on semiconductor quantum dots coupled to microcavities are showing their promise in long-distance solid-state quantum networks.

Abstract: Predicting the performance of traveling-wave parametric amplifiers (TWPAs) based on nonlinear elements like superconducting Josephson junctions (JJs) is vital for qubit read-out in quantum computers. The purpose of this article is twofold: (a) to demonstrate how nonlinear inductors based on combinations of JJs can be modeled in commercial circuit simulators, and (b) to show how the harmonic balance (HB) is used in the reliable prediction of the amplifier performance e.g., gain and pump harmonic power conversion. Experimental characterization of two types of TWPA architectures is compared with simulations to showcase the reliability of the HB method. We disseminate the modeling know-how and techniques to new designers of parametric amplifiers.

Abstract: Universal blind quantum computing allows users with minimal quantum resources to delegate a quantum computation to a remote quantum server, while keeping intrinsically hidden input, algorithm, and outcome. State-of-art experimental demonstrations of such a protocol have only involved one client. However, an increasing number of multi-party algorithms, e.g. federated machine learning, require the collaboration of multiple clients to carry out a given joint computation. In this work, we propose and experimentally demonstrate a lightweight multi-client blind quantum computation protocol based on a novel linear quantum network configuration (Qline). Our protocol originality resides in three main strengths: scalability, since we eliminate the need for each client to have its own trusted source or measurement device, low-loss, by optimizing the orchestration of classical communication between each client and server through fast classical electronic control, and compatibility with distributed architectures while remaining intact even against correlated attacks of server nodes and malicious clients.

Abstract: The ground-state of an artificial atom ultrastrongly coupled to quantized modes is entangled thus it contains an arbitrary number of virtual photons. The problem of their detection has been raised since the very birth of the field but despite the theoretical efforts still awaits experimental demonstration. Recently experimental problems have been addressed in detail showing that they can be overcome by combining an unconventional design of the artificial atom with advanced coherent control. In this work we study a simple scheme of control-integrated continuous measurement which makes remarkably favourable the tradeoff between measurement efficiency and backaction showing that the unambiguous detection of virtual photons can be achieved within state-of-the art quantum technologies.

Abstract: Quantum steering has attracted increasing research attention because of its fundamental importance, as well as its applications in quantum information science. Regardless of the well-established characterization of the steerability of assemblages, it remains unclear how to detect the degree of steerability even for an arbitrary qubit-pair state due to the cumbersome optimization over all possible incompatible measurements. Here we leverage the power of the deep learning models to infer the hierarchy of steering measurement setting. A computational protocol consisting of iterative tests is constructed to overcome the optimization, meanwhile, generating the necessary training data. According to the responses of the well-trained models to the different physics-driven features encoding the states to be recognized, we can conclude that the most compact characterization of the Alice-to-Bob steerability is Alice's regularly aligned steering ellipsoid; whereas Bob's ellipsoid is irrelevant. Additionally, our approach is versatile in revealing further insights into the hierarchical structure of quantum steering and detecting the hidden steerability.

Abstract: We present a smorgasbord of results on the stabiliser ZX-calculus for odd prime-dimensional qudits (i.e. qupits). We derive a simplified rule set that closely resembles the original rules of qubit ZX-calculus. Using these rules, we demonstrate analogues of the spider-removing local complementation and pivoting rules. This allows for efficient reduction of diagrams to the affine with phases normal form. We also demonstrate a reduction to a unique form, providing an alternative and simpler proof of completeness. Furthermore, we introduce a different reduction to the graph state with local Cliffords normal form, which leads to a novel layered decomposition for qupit Clifford unitaries. Additionally, we propose a new approach to handle scalars formally, closely reflecting their practical usage. Finally, we have implemented many of these findings in DiZX, a new open-source Python library for qudit ZX-diagrammatic reasoning.

Abstract: We have proposed and experimentally verified a tunable inter-qubit coupling scheme for large-scale integration of superconducting qubits. The key feature of the scheme is the insertion of connecting pads between qubit and tunable coupling element. In such a way, the distance between two qubits can be increased considerably to a few millimeters, leaving enough space for arranging control lines, readout resonators and other necessary structures. The increased inter-qubit distance provides more wiring space for flip-chip process and reduces crosstalk between qubits and from control lines to qubits. We use the term Tunable Coupler with Capacitively Connecting Pad (TCCP) to name the tunable coupling part that consists of a transmon coupler and capacitively connecting pads. With the different placement of connecting pads, different TCCP architectures can be realized. We have designed and fabricated a few multi-qubit devices in which TCCP is used for coupling. The measured results show that the performance of the qubits coupled by the TCCP, such as $T_1$ and $T_2$, was similar to that of the traditional transmon qubits without TCCP. Meanwhile, our TCCP also exhibited a wide tunable range of the effective coupling strength and a low residual ZZ interaction between the qubits by properly tuning the parameters on the design. Finally, we successfully implemented an adiabatic CZ gate with TCCP. Furthermore, by introducing TCCP, we also discuss the realization of the flip-chip process and tunable coupling qubits between different chips.

Abstract: Inspired by the method that can deterministically generated the massive entanglement through phase transitions, we study the ground state properties of a spin-1 condensate mixture, under the premise that the heteronuclear spin-exchange collision is taken into account. We developed a effective model to analyze the binary-coupled two-level system and studied the ground state phase transitions. Three representative quantum states with the same number distribution are studied and distinguished through the number fluctuations. We demonstrate that there will be the GreenbergerHorne-Zeilinger (GHZ) state in the mixture if the the extra magnetic field is specifically selected or adiabatically adjusted. One advantage of preparing entangled states in mixtures is that we only need to adjust the external magnetic field, instead of considering the microwaves-magnetic cooperation. Finally we estimate the feasibility of experimentally generating the heteronuclear many-body entanglement in the alkali-metal atomic mixture.

Abstract: Quantum computers have the potential to outperform classical computers, but are currently limited in their capabilities. One such limitation is the restricted connectivity between qubits, as captured by the hardware's coupling graph. This limitation poses a challenge for running algorithms that require a coupling graph different from what the hardware can provide. To overcome this challenge and fully utilize the hardware, efficient qubit routing strategies are necessary. In this paper, we introduce line-graph qubit routing, a general method for routing qubits when the algorithm's coupling graph is a line graph and the hardware coupling graph is a heavy graph. Line-graph qubit routing is fast, deterministic, and effective; it requires a classical computational cost that scales at most quadratically with the number of gates in the original circuit, while producing a circuit with a SWAP overhead of at most two times the number of two-qubit gates in the original circuit. We implement line-graph qubit routing and demonstrate its effectiveness in mapping quantum circuits on kagome, checkerboard, and shuriken lattices to hardware with heavy-hex, heavy-square, and heavy-square-octagon coupling graphs, respectively. Benchmarking shows the ability of line-graph qubit routing to outperform established general-purpose methods, both in the required classical wall-clock time and in the quality of the solution that is found. Line-graph qubit routing has direct applications in the quantum simulation of lattice-based models and aids the exploration of the capabilities of near-term quantum hardware.

Abstract: Noise detrimentally affects quantum computations so that they not only become less accurate but also easier to simulate classically as systems scale up. We construct a classical simulation algorithm, LOWESA (low weight efficient simulation algorithm), for estimating expectation values of noisy parameterised quantum circuits. It combines previous results on spectral analysis of parameterised circuits with Pauli back-propagation and recent ideas for simulations of noisy random circuits. We show, under some conditions on the circuits and mild assumptions on the noise, that LOWESA gives an efficient, polynomial algorithm in the number of qubits (and depth), with approximation error that vanishes exponentially in the physical error rate and a controllable cut-off parameter. We also discuss the practical limitations of the method for circuit classes with correlated parameters and its scaling with decreasing error rates.