Adaptive Resource Orchestration for Distributed Quantum Computing Systems

This paper reviews recent work related to the interplay between quantum information and computation on the one hand and classical and quantum chaos on the other. First, we present several models of quantum chaos that can be simulated efficiently on a quantum computer. Then a discussion of information extraction shows that such models can give rise to complete algorithms including measurements that can achieve an increase in speed compared with classical computation. It is also shown that models of classical chaos can be simulated efficiently on a quantum computer, and again information can be extracted efficiently from the final wave function. The total gain can be exponential or polynomial, depending on the model chosen and the observable measured. The simulation of such systems is also economical in the number of qubits, allowing implementation on present-day quantum computers, some of these algorithms having been already experimentally implemented. The second topic considered concerns the analysis of errors on quantum computers. It is shown that quantum chaos algorithms can be used to explore the effect of errors on quantum algorithms, such as random unitary errors or dissipative errors. Furthermore, the tools of quantum chaos allows a direct analysis of the effects of static errors on quantum computers. Finally, we consider the different resources used by quantum information, and show that quantum chaos has some precise consequences on entanglement generation, which becomes close to maximal. For another resource, interference, a proposal is presented for quantifying it, enabling a discussion on entanglement and interference generation in quantum algorithms.

In the search for scalable, fault-tolerant quantum computing, distributed quantum computers are promising candidates. These systems can be realized in large-scale quantum networks or condensed onto a single chip with closely situated nodes. We present a framework for numerical simulations of a memory channel using the distributed toric surface code, where each data qubit of the code is part of a separate node, and the error-detection performance depends on the quality of four-qubit Greenberger–Horne–Zeilinger (GHZ) states generated between the nodes. We quantitatively investigate the effect of memory decoherence and evaluate the advantage of GHZ creation protocols tailored to the level of decoherence. We do this by applying our framework for the particular case of color centers in diamond, employing models developed from experimental characterization of nitrogen-vacancy centers. For diamond color centers, coherence times during entanglement generation are orders of magnitude lower than coherence times of idling qubits. These coherence times represent a limiting factor for applications, but previous surface code simulations did not treat them as such. Introducing limiting coherence times as a prominent noise factor makes it imperative to integrate realistic operation times into simulations and incorporate strategies for operation scheduling. Our model predicts error probability thresholds for gate and measurement reduced by at least a factor of three compared to prior work with more idealized noise models. We also find a threshold of 4×102 in the ratio between the entanglement generation and the decoherence rates, setting a benchmark for experimental progress.

With the advent of interconnected quantum computers, i.e., distributed quantum computing (DQC), multiple quantum computers can now collaborate via quantum networks to perform massively complex computational tasks. However, DQC faces problems sharing quantum information because it cannot be cloned or duplicated between quantum computers. Thanks to advanced quantum mechanics, quantum computers can teleport quantum information across quantum networks. However, challenges to utilizing efficiently quantum resources, e.g., quantum computers and quantum channels, arise in DQC due to their capabilities and properties, such as uncertain qubit fidelity and quantum channel noise. In this paper, we propose a resource allocation scheme for DQC based on stochastic programming to minimize the total deployment cost for quantum resources. Essentially, the two-stage stochastic programming model is formulated to handle the uncertainty of quantum computing demands, computing power, and fidelity in quantum networks. The performance evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers while minimizing the overall cost of provisioning under uncertainty.

Practical distributed quantum computing requires the development of efficient compilers, able to make quantum circuits compatible with some given hardware constraints. This problem is known to be tough, even for local computing. Here, we address it on distributed architectures. As generally assumed in this scenario, telegates represent the fundamental remote (inter-processor) operations. Each telegate consists of several tasks: (i) entanglement generation and distribution, (ii) local operations, and (iii) classical communications. Entanglement generations and distribution is an expensive resource, as it is time-consuming. To mitigate its impact, we model an optimization problem that combines running-time minimization with the usage of distributed entangled states. Specifically, we formulated the distributed compilation problem as a dynamic network flow. To enhance the solution space, we extend the formulation, by introducing a predicate that manipulates the circuit given in input and parallelizes telegate tasks. To evaluate our framework, we split the problem into three sub-problems, and solve it by means of an approximation routine. Experiments demonstrate that the run-time is resistant to the problem size scaling. Moreover, we apply the proposed algorithm to compile circuits under different topologies, showing that topologies with a higher ratio between edges and nodes give rise to shallower circuits.

Quantum networks promise to be the future architecture for secure communication and distributed quantum computation. This thesis describes experiments on nitrogenvacancy (NV) centres that lead towards a versatile multi-node quantum network consisting ofmulti-qubit nodes. The NV centre in diamond is a spinful optically-active crystal defect. NVs are a prime network-node candidate due to demonstrated coherence times beyond 100ms and longitudinal relaxation times exceeding 1s and their spin-selective optical interface which facilitates the generation of spin-photon entanglement. Entangling links between nodes are therefore readily created by overlapping the emission of two NVs on a beam splitter. Besides NVs, we further address individual 13C nuclear spins in the vicinity and use these spins as a quantum resource. Our goal is to propel these nuclear spins to constitute robust quantummemories which store and manipulate quantum information in an NV-based quantum network. The experiments described in this thesis are thematically separated into three groups. First, we explore the NV-nuclear interplay. We demonstrate nuclear-spin control by observing the Zeno effect on up to two logical qubits within the state space of three nuclear spins (Chapter 3). We further realize that the always-on magnetic hyperfine interaction between NV and nuclear spins will limit the nuclear spin coherence when entangling distant NV centres (Chapter 4). A systematic experimental study probes our theoretical prediction and we additionally demonstrate improved robustness for logical states within decoherence-protected state spaces (Chapter 5) and finally for individual nuclear spins (Chapter 6). Second,we use remoteNV-NV entangled states to demonstrate experimental milestones in quantumnetworks. The realization of a high-fidelity entangled link over a distance of 1.3km permits the loophole-free violation of Bell’s inequality (Chapter 7). We further increase the entangling rate by three orders of magnitude such that it exceeds the decoherence rate of an entangled state on our network. This allows us to convert our probabilistic entanglement generation into a deterministic process which delivers entangled states at prespecifiedmoments in time (Chapter 8). Third, we finally combine the concepts of nuclear-spin quantum memories and remote entanglement generation to demonstrate entanglement distillation in a network setting (Chapter 9). We subsequently generate two raw entangled input states between two remote NV centres. The first state is stored on nuclear spins to liberate both NVs for the second round of state generation. Finally, a higher-fidelity entangled state is distilled via local operations. This constitutes the first quantum-network demonstration that relies on the control of multiple fully-coherent quantum systems per network node.

Distributed quantum computing (DQC) is a promising way to achieve large-scale quantum computing. However, mapping large-sized quantum circuits in DQC is a challenging job; for example, it is difficult to find an ideal cutting and mapping solution when many qubits, complicated qubit operations, and diverse QPUs are involved. In this study, we propose LarQucut, a new quantum circuit cutting and mapping approach for large-sized circuits in DQC. LarQucut has several new designs. (1) LarQucut can have cutting solutions that use fewer cuts, and it does not cut a circuit into independent sub-circuits, therefore reducing the overall cutting and computing overheads. (2) LarQucut finds isomorphic sub-circuits and reuses their execution results. So, LarQucut can reduce the number of sub-circuits that need to be executed to reconstruct the large circuit's output, reducing the time spent on sampling the sub-circuits. (3) We design an adaptive quantum circuit mapping approach, which identifies qubit interaction patterns and accordingly enables the best-fit mapping policy in DQC. The experimental results show that, for large circuits with hundreds to thousands of qubits in DQC, LarQucut can provide a better cutting and mapping solution with lower overall overheads and achieves results closer to the ground truth.

The exploration of distributed quantum computing (DQC) represents a significant frontier in quantum information science, aiming to harness the unique properties of quantum mechanics to solve complex computational problems effectively. This paper discusses the diverse architectures and models that facilitate the distribution of quantum computing tasks across multiple quantum nodes, thereby addressing the limitations inherent in single quantum devices. DQC systems exploit the principles of superposition and entanglement to enhance computational capabilities beyond what classical computing systems can achieve. We examine various DQC architectures, including both quantum-classical hybrid systems and fully quantum distributed systems, highlighting their respective benefits and challenges. Key issues such as coherence, communication overhead, and error correction are discussed in detail, and we analyze specific use cases where DQC demonstrates superior performance relative to classical computing approaches, including optimization problems in logistics and finance, as well as quantum simulations for material science. Furthermore, we identify future research directions aimed at overcoming existing barriers to the practical implementation of DQC systems. By assessing the current landscape and future possibilities of DQC, this paper underscores the transformative potential of distributed quantum computing in various fields, paving the way for realizing more complex quantum algorithms and applications.

: The first objective of this effort, searching for new quantum algorithms, created six new quantum hidden subgroup algorithms. The second objective, improving the theoretical understanding of existing quantum algorithms, produced three new systematic procedures. Also, application of combinatorial group theory led to substantial progress in the understanding and analysis of nonabelian quantum hidden subgroup algorithms. Additionally, methods and techniques of quantum topology have been used to obtain new results in quantum computing including discovery of a relationship between quantum entanglement and topological linking. The last objective, analyzing issues associated with algorithm implementation proposed distributed quantum computing (DQC) as a fast track to scalable quantum computing with technology available within the next five years. A universal set of DQC primitives has been created and used to transform the quantum Fourier transform and the Shor algorithm into DQC. The additional computational overhead needed for DQC algorithms is insignificant and DQC is found to simplify the decoherence problem.

Recent advances in quantum technologies are rapidly pushing the boundaries of computational capabilities, with the promise of significant speedups across a variety of applications. However, building large-scale monolithic quantum processors remains an immense engineering challenge, motivating the pursuit of alternative paradigms to realize practical quantum advantage. One such paradigm is Distributed Quantum Computing (DQC), which interconnects multiple quantum nodes via quantum networks to collectively perform complex computations. While Circuit-Based Quantum Computing (CBQC) has traditionally been the dominant model, Measurement-Based Quantum Computing (MBQC), with its one-way computation framework and pre-shared entangled cluster states, introduces distinctive benefits for distributed architectures. Importantly, within DQC, MBQC offers enhanced security over CBQC by naturally supporting protocols such as Blind Quantum Computation (BQC) and secure delegated processing. This study offers an application-oriented perspective on the role of MBQC in DQC, highlighting concrete industrial use cases alongside key implementation challenges. By bridging theoretical potential with practical deployment considerations, it aims to accelerate progress toward secure and scalable distributed quantum systems.

In noisy intermediate-scale quantum computing, the limited scalability of a single quantum processing unit (QPU) can be extended through distributed quantum computing (DQC), in which one can implement global operations over two QPUs by entanglement-assisted local operations and classical communication. To facilitate this type of DQC in experiments, we need an entanglement-efficient protocol. To this end, we extend the protocol in [Eisert et. al., PRA, 62:052317(2000)] implementing each nonlocal controlled-unitary gate locally with one maximally entangled pair to a packing protocol, which can pack multiple nonlocal controlled-unitary gates locally using one maximally entangled pair. In particular, two types of packing processes are introduced as the building blocks, namely the distributing processes and embedding processes. Each distributing process distributes corresponding gates locally with one entangled pair. The efficiency of entanglement is then enhanced by embedding processes, which merge two non-sequential distributing processes and hence save the entanglement cost. We show that the structure of distributability and embeddability of a quantum circuit can be fully represented by the corresponding packing graphs and conflict graphs. Based on these graphs, we derive heuristic algorithms for finding an entanglement-efficient packing of distributing processes for a given quantum circuit to be implemented by two parties. These algorithms can determine the required number of local auxiliary qubits in the DQC. We apply these algorithms for bipartite DQC of unitary coupled-cluster circuits and find a significant reduction of entanglement cost through embeddings. This method can determine a constructive upper bound on the entanglement cost for the DQC of quantum circuits.

In the current noisy intermediate-scale quantum (NISQ) era, the number of qubits and the depth of quantum circuits in a quantum computer are limited because of complex operation among increasing number of qubits, low-fidelity quantum gates under noise, and short coherence time of physical qubits. However, with distributed quantum computation (DQC) in which multiple small-scale quantum computers cooperate, large-scale quantum circuits can be implemented. In DQC, it is a key step to decompose large-scale quantum circuits into several small-scale subcircuits equivalently. In this paper, we propose a quantum circuit cutting scheme for the circuits consisting of only single-qubit gates and two-qubit gates. In the scheme, the number of non-local gates and the rounds of subcircuits operation are minimized by using the multi-objective simulated annealing (MOSA) algorithm to cluster the gates and to choose the cutting positions whilst using non-local gates. A reconstruction process is also proposed to calculate the probability distribution of output states of the original circuit. As an example, the 7-qubit circuit of Shor algorithm factoring 15 is used to verify the algorithm. Five cutting schemes are recommended, which can be selected according to practical requirements. Compared with the results of the mixing integer programming (MIP) algorithm, the number of execution rounds is efficiently reduced by slightly increasing the number of nonlocal gates.

With the advantages of high-speed parallel processing, quantum computers can efficiently solve large-scale complex optimization problems in future networks. However, due to the uncertain qubit fidelity and quantum channel noise, distributed quantum computing, which relies on quantum networks connected through entanglement, faces many challenges in exchanging information across quantum computers. In this article, we propose an adaptive distributed quantum computing approach, called DQC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> O, to manage quantum computers and quantum networks for solving optimization tasks in future networks. Firstly, we describe the fundamentals of quantum computing and its distributed concept in quantum networks. Secondly, to address the uncertainty of future demands of collaborative optimization tasks and instability over quantum networks, we propose a quantum resource allocation scheme based on stochastic programming for minimizing quantum resource consumption. Finally, based on the proposed approach, we discuss the potential military applications of collaborative optimization in future networks, such as smart grid management, IoT cooperation, and semantic communications. Promising research directions that can lead to the design and implementation of future distributed quantum computing frameworks are also highlighted.

With the advantages of high-speed parallel processing, quantum computers can efficiently solve large-scale complex optimization problems in future networks. However, due to the uncertain qubit fidelity and quantum channel noise, distributed quantum computing which relies on quantum networks connected through entanglement faces a lot of challenges for exchanging information across quantum computers. In this paper, we propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks. Firstly, we describe the fundamentals of quantum computing and its distributed concept in quantum networks. Secondly, to address the uncertainty of future demands of collaborative optimization tasks and instability over quantum networks, we propose a quantum resource allocation scheme based on stochastic programming for minimizing quantum resource consumption. Finally, based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning. Promising research directions that can lead to the design and implementation of future distributed quantum computing frameworks are also highlighted.

A viable approach for building large-scale quantum computers is to interlink small-scale quantum computers with a quantum network to create a larger distributed quantum computer. When designing quantum algorithms for such a distributed quantum computer, one can make use of the added parallelization and distribution abilities inherent in the system. An added difficulty to then overcome for distributed quantum computing is that a complex control system to orchestrate the various components is required. In this work, we aim to address these issues. We explicitly define what it means for a quantum algorithm to be distributed and then present various quantum algorithms that fit the definition. We discuss potential benefits and propose a high-level scheme for controlling the system. With this, we present our software framework called Interlin-q, a simulation platform that aims to simplify designing and verifying parallel and distributed quantum algorithms. We demonstrate Interlin-q by implementing some of the discussed algorithms using Interlin-q and layout future steps for developing Interlin-q into a control system for distributed quantum computers.

Distributed quantum computation requires quantum operations to act on logical qubits over a distance. We will develop a formal model for the telegate-based distributive quantum computation. We show that a controlled-controlled-NOT (Toffoli) gate as an elementary gate of the universal quantum computation may be remotely implemented by exploring a high-level quantum system. These remote Toffoli gates cost at most two Einstein-Podolsky-Rosen (EPR) pairs, whereas four or six EPR pairs are required from the teleportation-based quantum computation or the remote CNOT gate, respectively. Thus, the previous Toffoli gate-based circuit synthesis may be used as an elementary subroutine of this distributed quantum computation.