arXiv subjects

Niels M. P. Neumann

Publications and source records attributed to Niels M. P. Neumann.

18 recordsLinked to original sources

arXiv2026

Evaluating Quantum Wire Cutting for QAOA: Performance Benchmarks in Ideal and Noisy Environments

Current quantum computers suffer from a limited number of qubits and high error rates, limiting practical applicability. Different techniques exist to mitigate these effects and run larger algorithms. In this work, we analyze one of these techniques called quantum circuit cutting. With circuit cutting, a quantum circuit is decomposed into smaller sub-circuits, each of which can be run on smaller quantum hardware. We compare the performance of quantum circuit cutting with different cutting strategies, and then apply circuit cutting to a QAOA algorithm. Using simulations, we first show that Randomized Clifford measurements outperform both Pauli and random unitary measurements. Second, we show that circuit cutting has trouble providing correct answers in noisy settings, especially as the number of circuits increases.

quant-ph↗

arXiv2025

A Triple-Hybrid Quantum Support Vector Machine Using Classical, Quantum Gate-based and Quantum Annealing-based Computing

Quantum machine learning is one of the fields where quantum computers are expected to bring advantages over classical methods. However, the limited size of current computers restricts the exploitation of the full potential of quantum machine learning methods. Additionally, different computing paradigms, both quantum and classical, each have their own strengths and weaknesses. Obtaining optimal results with algorithms thus requires algorithms to be tweaked to the underlying computational paradigm, and the tasks to be optimally distributed over the available computational resources. In this work, we explore the potential gains from combining different computing paradigms to solve the complex task of data classification for three different datasets. We use a gate-based quantum model to implement a quantum kernel and implement a complex feature map. Next, we formulate a quadratic unconstrained optimisation problem to be solved on quantum annealing hardware. We then evaluate the losses on classical hardware and reconfigure the model parameters accordingly. We tested this so-called triple-hybrid quantum support vector machine on various data sets, and find that it achieves higher precision than other support vector machines (both quantum and classical) on complex quantum data, whereas it achieves varying performance on simple classical data using limited training. For the complex data sets, the triple-hybrid version converges faster, requiring fewer circuit evaluations.

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arXiv2025

Probability distribution reconstruction using circuit cutting applied to a variational classifier

Significant efforts are being spent on building a quantum computer. At the same time, developments in quantum software are rapidly progressing. Insufficient quantum resources often are the problem when running quantum algorithms. New techniques can aid in using smaller quantum computers to run larger quantum algorithms. One of these techniques is circuit cutting. With this method, a circuit is broken into multiple pieces, each of which is run on quantum hardware independently and then recombined to obtain the overall answer. These circuit cutting techniques require additional circuit evaluations, which can form a bottleneck for algorithms requiring many interactions. This work explores the potential of circuit cutting techniques precisely in this regime of many interactions. We consider two different models, a standard method based on expectation values, and a novel method based on probability distribution reconstruction. Next, we compare two different training methods we call train-then-cut and cut-then-train and show that in practice, cut-then-train still produces good results. This observation brings closer the practical applicability of circuit cutting techniques, as a train-then-cut strategy is often infeasible. We conclude by implementing a cut and uncut circuit and find that circuit cutting helps achieve higher fidelity results.

quant-ph↗

arXiv2025

Adaptive Quantum Computers: decoding and state preparation

Interacting with a standard computer can enhance the capabilities of current quantum computers already today, particularly by offloading certain computations to the standard computer. Quantum computers that interact with standard computers to perform computations are called adaptive quantum computers. This work formalizes a model that describes these adaptive quantum computers. As quantum computers are still under development, this work focuses on computations that terminate after a fixed number of steps, as that makes their implementation likely easier in practice. First, we show that adaptive quantum computers are more powerful than standard computers with respect to the practical problem of retrieving information from corrupted digital data. Standard computers struggle to retrieve such information within a fixed number of computation steps. The proof uses a structure-versus-randomness approach that splits the problem in a structured and a random-like component. The potential of adaptive quantum computations follows from a specific example where information is retrieved from corrupted data. Additionally, adaptive quantum computers can even improve standard computations for this problem that are not constrained by a fixed number of computation steps. Next, we show how adaptive quantum computations can improve non-adaptive quantum computations when preparing specific quantum states. We present efficient adaptive quantum algorithms to prepare the uniform superposition state, the GHZ state, the W-state and the Dicke state. These states are often used in other quantum algorithms, so having efficient routines for preparing them also enhances the efficiency of other algorithms. This work concludes by comparing these adaptive quantum computations with non-adaptive ones, analyzing their performance both theoretically and through quantum hardware implementations.

quant-ph↗

arXiv2025

Theoretical and experimental analysis of adaptive quantum computers

Fault-tolerant quantum computations require alternating quantum and classical computations, where the classical computations prove vital in detecting and correcting errors in the quantum computation. Recently, interest in using these classical computations has been growing again, not to correct errors, but to perform computations. Various works have looked into these so-called adaptive quantum algorithms. Few works however have looked in the advantages of adaptive quantum algorithms in realistic scenarios. This work provides the first step in this direction. We introduce a worst-case noise model and use it to derive success probabilities for preparing a GHZ state and preparing a $W$-state using either an adaptive quantum algorithm, or using a standard non-adaptive quantum algorithm. Next, we implemented these protocols on quantum hardware and we compare the outcomes to our derived theoretical results. We find that despite their potential, adaptive quantum algorithms currently do not outperform full quantum algorithms.

quant-ph↗

arXiv2025

Solving Markov Chains with Analog Quantum Computing: The Fine Print

With a growing interest in quantum computing, the number of proposed quantum algorithms grows as well. The practical applicability of these algorithms differs: Some can be applied out-of-the-box, while others require black box oracles, which can not always be easily implemented. One of the first works to explicitly discuss these practical applicability aspects is by Aaronson discussing the \textit{fine print} of the HHL quantum algorithm that solves linear systems of equations. We extend this line of research by providing a similar fine print for the first analog quantum algorithm that computes the stationary distribution of Markov chains. We conclude that more focus should be put on this practical applicability of quantum algorithms, either through a separate line of research, or through more attention when introducing the algorithm.

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arXiv2025

Non-Local Phase Estimation with a Rydberg-Superconducting Qubit Hybrid

Distributed quantum computing (DQC) is crucial for high-volume quantum processing in the NISQ era. Many different technologies are utilized to implement a quantum computer, each with a different advantages and disadvantages. Various research is performed on how to implement DQC within a certain technology, but research on DQC between different technologies is rather limited. In this work, we contribute to this latter research line, by implementing the Quantum Phase Estimation algorithm on a superconducting-resonator-atom hybrid system. This system combines a Rydberg atom qubit, as well as a superconducting flux qubit system to perform the algorithm. In addition, Hamiltonian dynamics are studied to analyze noise sources, after which quantum optimal control (GRAPE) is used to optimize gate construction. The results show tradeoffs between GRAPE step size, iterations and noise level.

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arXiv2024

State preparation by shallow circuits using feed forward

In order to achieve fault-tolerant quantum computation, we need to repeat the following sequence of four steps: First, perform 1 or 2 qubit quantum gates (in parallel if possible). Second, do a syndrome measurement on a subset of the qubits. Third, perform a fast classical computation to establish which errors have occurred (if any). Fourth, depending on the errors, we apply a correction step. Then the procedure repeats with the next sequence of gates. In order for these four steps to succeed, we need the error rate of the gates to be below a certain threshold. Unfortunately, the error rates of current quantum hardware are still too high. On the other hand, current quantum hardware platforms are designed with these four steps in mind. In this work we make use of this four-step scheme not to carry out fault-tolerant computations, but to enhance short, constant-depth, quantum circuits that perform 1 qubit gates and nearest-neighbor 2 qubit gates. To explore how this can be useful, we study a computational model which we call Local Alternating Quantum Classical Computations (LAQCC). In this model, qubits are placed in a grid allowing nearest neighbor interactions; the quantum circuits are of constant depth with intermediate measurements; a classical controller can perform log-depth computations on these intermediate measurement outcomes to control future quantum operations. This model fits naturally between quantum algorithms in the NISQ era and full fledged fault-tolerant quantum computation. We show that LAQCC circuits can create long-ranged interactions, which constant-depth quantum circuits cannot achieve, and use it to construct a range of useful multi-qubit gates. With these gates, we create three new state preparation protocols for a uniform superposition over an arbitrary number of states, W-states, Dicke states and may-body scar states.

quant-ph↗

arXiv2024

Distributed Quantum Computing: Applications and Challenges

Quantum computing is presently undergoing rapid development to achieve a significant speedup promised in certain applications. Nonetheless, scaling quantum computers remains a formidable engineering challenge, prompting exploration of alternative methods to achieve the promised quantum advantage. An example is given by the concept of distributed quantum computing, which aims to scale quantum computers through the linking of different individual quantum computers. Additionally, distributed quantum computing opens the way to new applications on the longer term. This study seeks to give an overview of this technology on an application-level, considering both use cases and implementation considerations. In this way, this work aims to push forward the field of distributed quantum computing, aiming for real-world distributed quantum systems in the near future.

quant-ph↗

arXiv2024

Extending the Q-score to an Application-level Quantum Metric Framework

Evaluating the performance of quantum devices is an important step towards scaling quantum devices and eventually using them in practice. The great number of available quantum metrics and the different hardware technologies used to develop quantum computers complicate this evaluation. In addition, different computational paradigms implement quantum operations in different ways. A prominent quantum metric is given by the Q-score metric of Atos. This metric was originally introduced as a standalone way to benchmark devices using the Max-Cut problem. In this work, we show that the Q-score defines a framework of quantum metrics, which allows benchmarking using different problems, user settings and solvers. To showcase the applicability of the framework, we showcase a second Q-score in this framework, called the Q-score Max-Clique. This yields, to our knowledge, the first application-level metric capable of natively comparing three different paradigms of quantum computing. This metric is evaluated on these computational quantum paradigms -- quantum annealing, gate-based quantum computing, and photonic quantum computing -- and the results are compared to those obtained by classical solvers.

quant-ph↗

arXiv2024

QuAS: Quantum Application Score for benchmarking the utility of quantum computers

Benchmarking quantum computers helps to quantify them and bringing the technology to the market. Various application-level metrics exist to benchmark a quantum device at an application level. This paper presents a revised holistic scoring method called the Quantum Application Score (QuAS) incorporating strong points of previous metrics, such as QPack and the Q-score. We discuss how to integrate both and thereby obtain an application-level metric that better quantifies the practical utility of quantum computers. We evaluate the new metric on different hardware platforms such as D-Wave and IBM as well as quantum simulators of Quantum Inspire and Rigetti.

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arXiv2024

Quantum algorithms for N-1 security in power grids

In recent years, the supply and demand of electricity has significantly increased. As a result, the interconnecting grid infrastructure has required (and will continue to require) further expansion, while allowing for rapid resolution of unforeseen failures. Energy grid operators strive for networks that satisfy different levels of security requirements. In the case of N-1 security for medium voltage networks, the goal is to ensure the continued provision of electricity in the event of a single-link failure. However, the process of determining if networks are N-1 secure is known to scale polynomially in the network size. This poses restrictions if we increase our demand of the network. In that case, more computationally hard cases will occur in practice and the computation time also increases significantly. In this work, we explore the potential of quantum computers to provide a more scalable solution. In particular, we consider gate-based quantum computing, quantum annealing, and photonic quantum computing.

quant-ph↗

arXiv2024

Noise robustness of a multiparty quantum summation protocol

Connecting quantum computers to a quantum network opens a wide array of new applications, such as securely performing computations on distributed data sets. Near-term quantum networks are noisy, however, and hence correctness and security of protocols are not guaranteed. To study the impact of noise, we consider a multiparty summation protocol with imperfect shared entangled states. We study analytically the impact of both depolarising and dephasing noise on this protocol and the noise patterns arising in the probability distributions. We conclude by eliminating the need for a trusted third party in the protocol using Shamir's secret sharing.

quant-ph↗

arXiv2023

Noisy decoding by shallow circuits with parities: classical and quantum

We consider the problem of decoding corrupted error correcting codes with NC$^0[\oplus]$ circuits in the classical and quantum settings. We show that any such classical circuit can correctly recover only a vanishingly small fraction of messages, if the codewords are sent over a noisy channel with positive error rate. Previously this was known only for linear codes with large dual distance, whereas our result applies to any code. By contrast, we give a simple quantum circuit that correctly decodes the Hadamard code with probability $Ω(\varepsilon^2)$ even if a $(1/2 - \varepsilon)$-fraction of a codeword is adversarially corrupted. Our classical hardness result is based on an equidistribution phenomenon for multivariate polynomials over a finite field under biased input-distributions. This is proved using a structure-versus-randomness strategy based on a new notion of rank for high-dimensional polynomial maps that may be of independent interest. Our quantum circuit is inspired by a non-local version of the Bernstein-Vazirani problem, a technique to generate ``poor man's cat states'' by Watts et al., and a constant-depth quantum circuit for the OR function by Takahashi and Tani.

cs.CC↗

arXiv2023

Evaluating Three Levels of Quantum Metrics on Quantum-Inspire Hardware

With the rise of quantum computing, many quantum devices have been developed and many more devices are being developed as we speak. This begs the question of which device excels at which tasks and how to compare these different quantum devices with one another. The answer is given by quantum metrics, of which many exist today already. Different metrics focus on different aspects of (quantum) devices and choosing the right metric to benchmark one device against another is a difficult choice. In this paper we aim to give an overview of this zoo of metrics by grouping established metrics in three levels: component level, system level and application level. With this characterisation we also mention what the merits and uses are for each of the different levels. In addition, we evaluate these metrics on the Starmon-5 device of Quantum-Inspire through the cloud access, giving the most complete benchmark of a quantum device from an user experience to date.

quant-ph↗

arXiv2022

Distributed Quantum Machine Learning

Quantum computers can solve specific complex tasks for which no reasonable-time classical algorithm is known. Quantum computers do however also offer inherent security of data, as measurements destroy quantum states. Using shared entangled states, multiple parties can collaborate and securely compute quantum algorithms. In this paper we propose an approach for distributed quantum machine learning, which allows multiple parties to securely perform computations, without having to reveal their data. We will consider a distributed adder and a distributed distance-based classifier.

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arXiv2020

Quantum Communication for Military Applications

Communication is vital in everyday life and critical for current and future military operations. However, conventional communication as we know it also has its limitations. Quantum communication allows some of these challenges to be overcome and, thereby, new application areas open up, also in the military domain. In this work quantum communication for military purposes is considered. Different applications are presented and the state-of-the-art of the technology is given. Also, quantum communication use cases specific for military applications are described.

quant-ph↗

arXiv2020

Classification using a two-qubit quantum chip

Quantum computing has great potential for advancing machine learning algorithms beyond classical reach. Even though full-fledged universal quantum computers do not exist yet, its expected benefits for machine learning can already be shown using simulators and already available quantum hardware. In this work, we focus on distance-based classification using actual early stage quantum hardware. We extend earlier work and present a distance-based classification algorithm using only two qubits. We show that the results are similar to the theoretically expected results.

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