Quantum Architectures Research Articles

Quantum embedding theories to simulate condensed systems on quantum computers.

Quantum computers hold promise to improve the efficiency of quantum simulations of materials and to enable the investigation of systems and properties that are more complex than tractable at present on classical architectures. Here, we discuss computational frameworks to carry out electronic structure calculations of solids on noisy intermediate-scale quantum computers using embedding theories, and we give examples for a specific class of materials, that is, solid materials hosting spin defects. These are promising systems to build future quantum technologies, such as quantum computers, quantum sensors and quantum communication devices. Although quantum simulations on quantum architectures are in their infancy, promising results for realistic systems appear to be within reach.

A Path to Solving Robotic Differential Equations Using Quantum Computing

Abstract Quantum computing and quantum information science is a burgeoning engineering field at the cusp of solving challenging robotic applications. This paper introduces a hybrid (gate-based) quantum computing and classical computing architecture to solve the motion propagation problem for a robotic system. This paper presents the quantum-classical architecture for linear differential equations defined by two types of linear operators: unitary and non-unitary system matrices, thereby solving any linear ordinary differential equations. The ability to encode information using bits or qubits—is essential in any computation problem. The results in this paper also introduce two novel approaches to encoding any arbitrary state vector or any arbitrary linear operator using qubits. Unlike other algorithms that solve ordinary differential equations (ODEs) using purely quantum or classical architectures, the ODE solver presented in this paper leverages the best of quantum and classical computing paradigms.

Performance of superconducting quantum computing chips under different architecture designs

Existing and near-term quantum computers can only perform two-qubit gates between physically connected qubits. Research has been done on compilers to rewrite quantum programs to match hardware constraints. However, the quantum processor architecture, in particular the qubit connectivity and topology, still lacks enough discussion, while it potentially has a huge impact on the performance of the quantum algorithms. We perform a quantitative and comprehensive study on the quantum processor performance under different qubit connectivity and topology. We select ten representative design models with different connectivities and topologies from quantum architecture design space and benchmark their performance by running a set of standard quantum algorithms. It is shown that a high-performance architecture almost always comes with a design with a large connectivity, while the topology shows a weak influence on the performance in our experiment. Different quantum algorithms show different dependence on quantum chip connectivity and topologies. This work provides quantum computing researchers with a systematic approach to evaluating their processor design.

NetQASM—a low-level instruction set architecture for hybrid quantum–classical programs in a quantum internet

We introduce NetQASM, a low-level instruction set architecture for quantum internet applications. NetQASM is a universal, platform-independent and extendable instruction set with support for local quantum gates, powerful classical logic and quantum networking operations for remote entanglement generation. Furthermore, NetQASM allows for close integration of classical logic and communication at the application layer with quantum operations at the physical layer. This enables quantum network applications to be programmed in high-level platform-independent software, which is not possible using any other QASM variants. We implement NetQASM in a series of tools to write, parse, encode and run NetQASM code, which are available online. Our tools include a higher-level software development kit (SDK) in Python, which allows an easy way of programming applications for a quantum internet. Our SDK can be used at home by making use of our existing quantum simulators, NetSquid and SimulaQron, and will also provide a public interface to hardware released on a future iteration of Quantum Network Explorer.

Quantum Architecture Search with Meta‐Learning

AbstractVariational quantum algorithms (VQAs) have been successfully applied to quantum approximate optimization algorithms, variational quantum compiling and quantum machine learning models. The performances of VQAs largely depend on the architecture of parameterized quantum circuits (PQCs). Quantum architecture search (QAS) aims to automate the design of PQCs in different VQAs with classical optimization algorithms. However, current QAS algorithms do not use prior experiences and search the quantum architecture from scratch for each new task, which is inefficient and time consuming. In this paper, a meta quantum architecture search (MetaQAS) algorithm is proposed, which learns good initialization heuristics of the architecture (i.e., meta‐architecture), along with the meta‐parameters of quantum gates from a number of training tasks such that they can adapt to new tasks with fewer gradient updates, which leads to fast learning on new tasks. The proposed MetaQAS can be used with arbitrary gradient‐based QAS algorithms. Simulation results on variational quantum compiling (VQC) and quantum approximate optimization algorithm (QAOA) show that the architectures optimized by MetaQAS converge faster than a state‐of‐the‐art gradient‐based QAS algorithm, namely DQAS. MetaQAS also achieves a better solution than DQAS after fine‐tuning of gate parameters.

Quantum circuit architecture search for variational quantum algorithms

Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS cannot only alleviate the influence of quantum noise and barren plateaus but also outperforms VQAs with pre-selected ansatze.

Heuristic Reordering Strategy for Quantum Circuit Mapping on LNN Architectures.

Because of the connection constraints of quantum devices, the quantum gate cannot operate directly on nonadjacent qubits. Quantum circuit mapping transforms a logical quantum circuit to a circuit that satisfies the connection constraints by adding SWAP gates for nonadjacent qubits. Global and local heuristic reordering strategies are proposed in this paper for quantum circuit mapping over linear nearest neighbor (LNN) architectures, which are one-dimensional topology structures, to reduce the number of SWAP gates added. Experiment results show that the average improvements of the two methods are 13.19% and 15.46%, respectively. In this paper, we consider the quantum circuit mapping problem for linear nearest neighbor (LNN) architectures. We propose a global heuristic qubit reordering optimization algorithm and a local heuristic qubit reordering optimization algorithm. Compared with the other algorithm results, the average improvements of the two methods for quantum cost are 13.19% and 15.46%, respectively. The two methods apply to the realization of quantum circuit neighboring over one-dimensional quantum architectures and can be extended to algorithms that work for other quantum architectures of different topologies.

On the remote entanglement of MW qubits using hybrid Rydberg systems

Distributed quantum computing is a promising architecture for the realization of scalable quantum computers. The cornerstone of such a quantum architecture is the ability to establish quantum correlations between remote computation modules. Remote entanglement and quantum logic gates have been demonstrated in a few physical systems enabled by coupling matter qubits to photons. However, building such a quantum architecture with superconducting qubits seems very challenging due to the lack of efficient matter-light quantum interface. Following our previous work on hybrid microwave-optical quantum gate, we propose a protocol to perform a remote quantum logic gate between distant superconducting quantum modules. MW qubits in distant modules are connected with photonic ancillary qubits and a hybrid Rydberg-cavity system is employed as the quantum mediator for microwave-optical photon interaction. We perform thorough analysis and find high-fidelity remote quantum logic gate is achievable by integrating state of the art quantum systems.

Superconducting circuit architecture for digital-analog quantum computing

We propose a superconducting circuit architecture suitable for digital-analog quantum computing (DAQC) based on an enhanced NISQ family of nearest-neighbor interactions. DAQC makes a smart use of digital steps (single qubit rotations) and analog blocks (parametrized multiqubit operations) to outperform digital quantum computing algorithms. Our design comprises a chain of superconducting charge qubits coupled by superconducting quantum interference devices (SQUIDs). Using magnetic flux control, we can activate/deactivate exchange interactions, double excitation/de-excitations, and others. As a paradigmatic example, we present an efficient simulation of an ell times h fermion lattice (with 2<ell leq h), using only 2(2ell +1)^{2}+24 analog blocks. The proposed architecture design is feasible in current experimental setups for quantum computing with superconducting circuits, opening the door to useful quantum advantage with fewer resources.

Quantum computations for disambiguation and question answering

Automatic text processing is now a mature discipline in computer science, and so attempts at advancements using quantum computation have emerged as the new frontier, often under the term of quantum natural language processing. The main challenges consist in finding the most adequate ways of encoding words and their interactions on a quantum computer, considering hardware constraints, as well as building algorithms that take advantage of quantum architectures, so as to show improvement on the performance of natural language tasks. In this paper, we introduce a new framework that starts from a grammar that can be interpreted by means of tensor contraction, to build word representations as quantum states that serve as input to a quantum algorithm. We start by introducing an operator measurement to contract the representations of words, resulting in the representation of larger fragments of text. We then go on to develop pipelines for the tasks of sentence meaning disambiguation and question answering that take advantage of quantum features. For the first task, we show that our contraction scheme deals with syntactically ambiguous phrases storing the various different meanings in quantum superposition, a solution not available on a classical setting. For the second task, we obtain a question representation that contains all possible answers in equal quantum superposition, and we implement Grover’s quantum search algorithm to find the correct answer, agnostic to the specific question, an implementation with the potential of delivering a result with quadratic speedup.

Quantum convolutional neural networks for high energy physics data analysis

This paper presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed quantum architecture demonstrates an advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters. In addition to the faster convergence, the QCNN achieves a greater test accuracy compared to CNNs. Based on our results from numerical simulations, it is a promising direction to apply QCNN and other quantum machine learning models to high energy physics and other scientific fields.

On the Quantum Performance Evaluation of Two Distributed Quantum Architectures

Distributed quantum applications impose requirements on the quality of the quantum states that they consume. When analyzing architecture implementations of quantum hardware, characterizing this quality forms an important factor in understanding their performance. Fundamental characteristics of quantum hardware lead to inherent tradeoffs between the quality of states and traditional performance metrics such as throughput. Furthermore, any real-world implementation of quantum hardware exhibits time-dependent noise that degrades the quality of quantum states over time. Here, we study the performance of two possible architectures for interfacing a quantum processor with a quantum network. The first corresponds to the current experimental state of the art in which the same device functions both as a processor and a network device. The second corresponds to a future architecture that separates these two functions over two distinct devices. We model these architectures as continuous-time Markov chains and compare their quality of executing quantum operations and producing entangled quantum states as functions of their memory lifetimes, as well as the time that it takes to perform various operations within each architecture. As an illustrative example, we apply our analysis to architectures based on Nitrogen- Vacancy centers in diamond, where we find that for presentday device parameters one architecture is more suited to computation-heavy applications, and the other for networkheavy ones. We validate our analysis with the quantum network simulator NetSquid. Besides the detailed study of these architectures, a novel contribution of our work are several formulas that connect an understanding of waiting time distributions to the decay of quantum quality over time for the most common noise models employed in quantum technologies. This provides a valuable new tool for performance evaluation experts, and its applications extend beyond the two architectures studied in this work.

Design of an LSTM Cell on a Quantum Hardware

The present brief proposes a methodology to implement a Long Short-Term Memory cell in the quantum framework, where inference is computed by replicating the internal structure of the cell using quantum circuits. A suitable encoding is proposed and the design of each quantum operation is detailed. A complexity analysis of the circuit is hence conducted and finally, the quantum architecture is experimentally validated both in an IBM Q simulator and with a numerical simulation on a classical device. The proposed approach leads the way for a completely quantum implementation of a Long Short-Term Memory network.

Electrical and Morphological Characterizations of 3-D Interconnections for Quantum Computation

Large-scale integration of quantum bits (qubits) requires 3-D architectures functional at sub-Kelvin temperatures. Electrical signals are transferred through 3-D interconnects to control and read out the information stored in the qubits. Careful design of the interconnects in terms of used materials and dimensions is, thus, essential to optimize the whole system performance. To help the design of future quantum architectures, we benchmark two interconnect technologies based on SnAg microbumps and Cu/SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> hybrid bonding. We report electrical and morphological characterizations performed on 20- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> pitch daisy chains (DCs) fabricated with die-to-wafer processes developed at CEA-LETI on 300-mm Si wafers. DCs with more than 20 000 SnAg microbump-based interconnects and more than 1000 direct Cu-bonded ones have been electrically characterized in a He <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> cryostat between 300 K and 500 mK. These measurements allow extracting unitary link resistances, establishing a preliminary process design kit at these low temperatures. The lack of defects and cracks on cross section electron micrographs performed after the low-temperature electrical measurements confirmed the mechanical robustness of these interconnect technologies to thermal variations between the room and sub-Kelvin temperatures.

Technical perspective: Applying design-space exploration to quantum architectures

Technical perspective: Applying design-space exploration to quantum architectures

Quantum annealing learning search implementations

This paper presents the details and testing of two implementations (in C++ and Python) of the hybrid quantum-classical algorithm Quantum Annealing Learning Search (QALS) on a D-Wave quantum annealer. QALS was proposed in 2019 as a novel technique to solve general QUBO problems that cannot be directly represented into the hardware architecture of a D-Wave machine. Repeated calls to the quantum machine within a classical iterative structure and a related convergence proof originate a learning mechanism to find an encoding of a given problem into the quantum architecture. The present work considers the Number Partitioning Problem (NPP) and the Travelling Salesman Problem (TSP) for the testing of QALS. The results turn out to be quite unexpected, with QALS not being able to perform as well as the other considered methods, especially in NPP, where classical methods outperform quantum annealing in general. Nevertheless, looking at the TSP tests, QALS has fulfilled its primary goal, i.e., processing QUBO problems not directly mappable to the QPU topology.

Hybrid Rydberg quantum gate for quantum network

The high fidelity storage, distribution and processing of quantum information prefers qubits with different physical properties. Thus, hybrid quantum gates interfacing different types of qubits are essential for the realization of complex quantum network structures. A Rydberg-atom based physical quantum CZ gate is proposed to hybridly process the polarisation-encoded single-photon optical qubit and the "Schroedinger cat" microwave qubit. The degradation of the fidelity under the influence of various noise channels, such as microwave cavity loss, sponetanous emission of atom states, and non-adiabaticity effect, etc, has been analyised through detailed theoretical analysis by deriving input-output relation of qubit fields. The feasibility and the challenges of the protocol within current technology are also discussed by analysing the possible experimental parameter settings.

Laser Writing of Color Centers

AbstractColor centers, optically active defects within solids, are vital for leading quantum information technologies such as quantum computing and quantum sensing. An essential prerequisite for realizing scalable quantum architectures is the ability to create quantum emitters deterministically. In the last decades, significant efforts have been devoted to selectively generating color centers. One of the most used methods is high‐energy ion implantation. However, this method usually causes extended lattice damage along the entire trajectory because of ions bombardment. Moreover, the position depth of color centers is also limited by the ions penetrate length in crystals. The direct laser‐writing (DLW) technique has recently emerged as a powerful tool to create color centers in solid‐state materials. It can define color centers at arbitrary depths inside the substrate and operate at the ambient environment, therefore, providing a feasible 3D fabrication method for integrated quantum photonics. Here, recent advancements of laser writing of color centers in solid‐state materials are reviewed, from bulk crystals, such as diamond and silicon carbide, to nanostructures, involving single‐walled carbon nanotubes, 2D layered materials, and quantum dots.

Markovian Quantum Neuroevolution for Machine Learning

Neuroevolution, a field that draws inspiration from the evolution of brains in nature, harnesses evolutionary algorithms to construct artificial neural networks. It bears a number of intriguing capabilities that are typically inaccessible to gradient-based approaches, including optimizing neural-network architectures, hyperparameters, and even learning the training rules. In this paper, we introduce a quantum neuroevolution algorithm that autonomously finds near-optimal quantum neural networks for different machine-learning tasks. In particular, we establish a one-to-one mapping between quantum circuits and directed graphs, and reduce the problem of finding the appropriate gate sequences to a task of searching suitable paths in the corresponding graph as a Markovian process. We benchmark the effectiveness of the introduced algorithm through concrete examples including classifications of real-life images and symmetry-protected topological states. Our results showcase the vast potential of neuroevolution algorithms in quantum architecture search, which would boost the exploration towards quantum-learning advantage with noisy intermediate-scale quantum devices.

Symmetry-resolved entanglement detection using partial transpose moments

We propose an ordered set of experimentally accessible conditions for detecting entanglement in mixed states. The k-th condition involves comparing moments of the partially transposed density operator up to order k. Remarkably, the union of all moment inequalities reproduces the Peres-Horodecki criterion for detecting entanglement. Our empirical studies highlight that the first four conditions already detect mixed state entanglement reliably in a variety of quantum architectures. Exploiting symmetries can help to further improve their detection capabilities. We also show how to estimate moment inequalities based on local random measurements of single state copies (classical shadows) and derive statistically sound confidence intervals as a function of the number of performed measurements. Our analysis includes the experimentally relevant situation of drifting sources, i.e. non-identical, but independent, state copies.