arXiv subjects

Lloyd C. L. Hollenberg

Publications and source records attributed to Lloyd C. L. Hollenberg.

At least 19 recordsLinked to original sources

arXiv2026

Scalable Quantum State Preparation for Encoding Genomic Data with Matrix Product States

As quantum computing hardware advances, the need for algorithms that facilitate the loading of classical data into the quantum states of these devices has become increasingly important. This study presents a method for producing scalable quantum circuits to encode genomic data using the Matrix Product State (MPS) formalism. The method is illustrated by encoding the genome of the bacteriophage $ΦX174$ into a 15-qubit state, and analysing the trade-offs between MPS bond dimension, reconstruction error, and the resulting circuit complexity. This study proposes methods for optimising encoding circuits with standard benchmark datasets for the emerging field of quantum bioinformatics. The results for circuit generation and simulation on HPC and on current quantum hardware demonstrate the viability and utility of the encoding.

quant-ph↗

arXiv2026

Kernel Alignment for Quantum Support Vector Machines Using Genetic Algorithms

The data encoding circuits used in quantum support vector machine (QSVM) kernels play a crucial role in their classification accuracy. However, manually designing these circuits poses significant challenges in terms of time and performance. To address this, we leverage the GASP (Genetic Algorithm for State Preparation) framework for gate sequence selection in QSVM kernel circuits. We explore supervised and unsupervised kernel loss functions' impact on encoding circuit optimisation and evaluate them on diverse datasets for binary and multiple-class scenarios. Benchmarking against classical and quantum kernels reveals GA-generated circuits matching or surpassing standard techniques. We analyse the relationship between test accuracy and quantum kernel entropy, with results indicating a positive correlation. Our automated framework reduces trial and error, and enables improved QSVM based machine learning performance for finance, healthcare, and materials science applications.

quant-ph↗

arXiv2026

Practical learning of multi-time statistics in open quantum systems

Randomised measurements can efficiently characterise many-body quantum states by learning the expectation values of observables with low Pauli weights. In this paper, we generalise the theoretical tools of classical shadow tomography to the temporal domain to explore multi-time phenomena. This enables us to efficiently learn the features of multi-time processes such as correlated error rates, multi-time non-Markovianity, and temporal entanglement. We test the efficacy of these tools on a noisy quantum processor to characterise its noise features. Implementing these tools requires mid-circuit instruments, typically slow or unavailable in current quantum hardware. We devise a protocol to achieve fast and reliable instruments such that these multi-time distributions can be learned to a high accuracy. This enables a compact matrix product operator representation of large processes allowing us to showcase a reconstructed 20-step process (whose naive dimensionality is that of a 42-qubit state). Our techniques are pertinent to generic quantum stochastic dynamical processes, with a scope ranging across condensed matter physics, quantum biology, and in-depth diagnostics of noisy intermediate-scale quantum devices.

quant-ph↗

arXiv2025

Genetically Engineered Quantum Circuits for Financial Market Indicators

Quantum computing holds immense potential for transforming financial analysis and decision-making. Realising this potential necessitates the efficient encoding and processing of financial data on quantum computers. In this study, we propose using the GASP (Genetic Algorithm for State Preparation) framework to optimise the encoding of stock price data into quantum states and show it can enhance both the fidelity and efficiency of the encoding process. We demonstrate the efficacy of our approach by encoding stock price data onto both a simulated and real quantum computer to calculate the Singular Value Decomposition (SVD) entropy. Our results show improvements in fidelity and the potential for more precise financial analysis. This research provides insights into the applicability of GASP for the efficient encoding of real-world data, specifically stock price data, which is crucial for quantum advantage on noisy intermediate-scale quantum (NISQ) era quantum computers.

quant-ph↗

arXiv2025

Moments-based quantum computation of the electric dipole moment of molecular systems

With rapid progress being made in the development of platforms for quantum computation, there has been considerable interest in whether present-day and near-term devices can be used to solve problems of relevance. A commonly cited application area is the domain of quantum chemistry. While most experimental demonstrations of quantum chemical calculations on quantum devices have focused on the ground-state electronic energy of the system, other properties of the ground-state, such as the electric dipole moment, are also of interest. Here we employ the quantum computed moments (QCM) method, based on the Lanczos cluster expansion, to estimate the dipole moment of the water molecule on an IBM Quantum superconducting quantum device. The noise-mitigated results agree with full configuration interaction (FCI) calculations to within 0.03 $\pm$ 0.007 debye (2% $\pm$ 0.5%), compared to direct expectation value determination (i.e. VQE) with errors on the order of 0.07 debye (5%), even when the VQE calculation is performed without noise. This demonstrates that moments-based energy estimation techniques can be adapted to noise-robust evaluation of non-energetic ground-state properties of chemical systems.

quant-ph↗

arXiv2025

Teleporting two-qubit entanglement across 19 qubits on a superconducting quantum computer

Quantum teleportation is not merely a fascinating corollary of quantum entanglement, it also finds utility in quantum processing and circuit compilation. In this paper, we measure and track the entanglement and fidelity of two-qubit states prepared on a 127-qubit IBM Quantum device, as one of the qubits is teleported across 19 qubits. We design, evaluate and compare two distinct approaches to teleportation: post-selected measurement categorisation and dynamic circuit corrections based on mid-circuit measurements, and compare with direct state transportation using SWAP gates. By optimally choosing the teleportation path which exhibits the highest total negativity entanglement measure across nearest-neighbour pairs, we show the entanglement of a two-qubit graph state is sustained after at least 19 hops in teleportation using the post-selection approach and 17 hops using the dynamic circuit approach. We observe a higher level of teleported entanglement in paths determined from two-qubit negativities compared to those obtained from gate errors, demonstrating an advantage in using the negativity map over the gate error map for compiling quantum circuits.

quant-ph↗

arXiv2025

Implementation of a quantum sequence alignment algorithm for quantum bioinformatics

This paper presents the implementation of a quantum sequence alignment (QSA) algorithm on biological data in environments simulating noisy intermediate-scale quantum (NISQ) computers. The approach to quantum bioinformatics adapts the original QSA algorithm proposed in 2000 to current capabilities and limitations of NISQ-era quantum computers and uses a genetic algorithm for state preparation (GASP) to create encoding circuits to load both database and target sequences into the quantum data registers. The implementation is tested in a simulated quantum computer environment to validate the approach and refine the GASP data-loading circuit designs. The results demonstrate the practicalities of deploying the QSA algorithm and exemplify the potential of GASP for data encoding in the realm of quantum circuit design, particularly for complex algorithms in quantum bioinformatics and other data-rich problems.

quant-ph↗

arXiv2025

Fully convolutional 3D neural network decoders for surface codes with syndrome circuit noise

Artificial Neural Networks (ANNs) are a promising approach to the decoding problem of Quantum Error Correction (QEC), but have observed consistent difficulty when generalising performance to larger QEC codes. Recent scalability-focused approaches have split the decoding workload by using local ANNs to perform initial syndrome processing and leaving final processing to a global residual decoder. We investigated ANN surface code decoding under a scheme exploiting the spatiotemporal structure of syndrome data. In particular, we present a vectorised method for surface code data simulation and benchmark decoding performance when such data defines a multi-label classification problem and generative modelling problem for rotated surface codes with circuit noise after each gate and idle timestep. Performance was found to generalise to rotated surface codes of sizes up to $d=97$, with depolarisation parameter thresholds of up to $0.7\%$ achieved, competitive with h Minimum Weight Perfect Matching (MWPM). Improved latencies, compared with MWPM alone, were found starting at code distances of $d=33$ and $d=89$ under noise models above and below threshold respectively. These results suggest promising prospects for ANN-based frameworks for surface code decoding with performance sufficient to support the demands expected from fault-tolerant resource estimates.

quant-ph↗

arXiv2025

What can unitary sequences tell us about multi-time physics?

Multi-time quantum processes are endowed with the same richness as multipartite states, including temporal entanglement and exotic causal structures. However, experimentally probing these rich phenomena leans heavily on fast and clean mid-circuit measurements, which are rarely available. We show here how surprisingly accessible these phenomena are in nascent quantum processors even when faced with substantially limited control. We work within the limitation where only unitary control is allowed, followed by a terminating measurement. Within this setting, we first develop a witness for genuine multi-time entanglement, and then methods to bound (from top and bottom) multi-time entanglement, non-Markovianity, purity, entropy, and other correlative measures. Our tools are designed to be implemented on quantum information processors, which we proceed to demonstrate. Finally, we discuss the limitations of these methods by testing them across random multi-time processes. Conceptually, this broadens our understanding of the extent to which temporal correlations may be determined with only deterministic control. Our techniques are pertinent to generic quantum stochastic dynamical processes, with a scope ranging across condensed matter physics, quantum biology, and in-depth diagnostics of NISQ-era quantum devices.

quant-ph↗

arXiv2025

Quantum autoencoders using mixed reference states

One of the fundamental tasks in quantum information theory is quantum data compression, which can be realized via quantum autoencoders that first compress quantum states to low-dimensional ones and then recover to the original ones with a reference state. When taking a pure reference state, there exists an upper bound for the encoding fidelity, which limits the compression of states with high entropy. To overcome the entropy inconsistency, we allow the reference state to be a mixed state and propose a cost function that combines the encoding fidelity and the quantum mutual information. We consider the reference states to be a mixture of maximally mixed states and pure states and propose three strategies for setting the ratio of mixedness. Numerical simulations of different states and experimental implementations on IBM quantum devices illustrate the effectiveness of our approach.

quant-ph↗

arXiv2025

Machine learning assisted tracking of magnetic objects using quantum diamond magnetometry

Remote magnetic sensing can be used to monitor the position of objects in real-time, enabling ground transport monitoring, underground infrastructure mapping and hazardous detection. However, magnetic signals are typically weak and complex, requiring sophisticated physical models to analyze them and a detailed knowledge of the system under study, factors that are frequently unavailable. In this work, we provide a solution to these limitations by demonstrating a Machine Learning (ML) method that can be trained exclusively on experimental data, without the need of any physical model, to predict the position of a magnetic target in real-time. The target can be any object with a magnetic signal above the floor noise, and in this case we use a quantum diamond magnetometer to track variations of few hundreds of nanoteslas produced by an elevator moving along a single axis. The one-dimensional movement is a simple yet challenging scenario, resembling realistic environments such as high buildings, tunnels or train circuits, and is the first step towards building broader applications. Our ML algorithm can be trained in approximately 40 min, achieving over 80% accuracy in predicting the target's position at a rate of 10 Hz, for a positional error tolerance of 30 cm, which is a precise distance compared to the 4-meter spacing between parking levels. Our results open up the possibility to apply this ML method more generally for real-time monitoring of magnetic objects, which will broaden the scope of magnetic detection applications.

physics.app-ph↗

arXiv2025

Entanglement teleportation along a regenerating hamster-wheel graph state

We scheme an efficient and reusable approach to quantum teleportation that allows cyclic teleportation of a two-qubit graph state around a quantum hamster wheel -- a ring of qubits entangled as a one-dimensional line prepared on the 20-qubit Quantinuum H1-1 ion-trap quantum processor. The qubits on the ring are periodically measured and reused to achieve a teleportation depth that exceeds the total number of available qubits in the quantum processor. Using the outcomes measured during teleportation, we calculate and apply byproduct operators through dynamic circuits to correct local transformations induced on the teleported state. We evaluate the quality of teleportation by tracing the preserved entanglement and fidelity of the teleported two-qubit graph state from its density matrix. In the real-machine experiments, we demonstrate that 58% of the teleported state's entanglement is sustained with a measured two-qubit negativity of $0.291\pm0.018$ after three complete revolutions around the hamster wheel, or equivalently, after hopping across 56 qubits. On the machine-specific noisy emulator, we found that the teleported state after 100 hops still sustained 45% of its entanglement. By performing teleportation along a regenerating graph state, our work is a step forward in demonstrating the feasibility of measurement-based quantum computation.

quant-ph↗

arXiv2024

Let the Quantum Creep In: Designing Quantum Neural Network Models by Gradually Swapping Out Classical Components

Artificial Intelligence (AI), with its multiplier effect and wide applications in multiple areas, could potentially be an important application of quantum computing. Since modern AI systems are often built on neural networks, the design of quantum neural networks becomes a key challenge in integrating quantum computing into AI. To provide a more fine-grained characterisation of the impact of quantum components on the performance of neural networks, we propose a framework where classical neural network layers are gradually replaced by quantum layers that have the same type of input and output while keeping the flow of information between layers unchanged, different from most current research in quantum neural network, which favours an end-to-end quantum model. We start with a simple three-layer classical neural network without any normalisation layers or activation functions, and gradually change the classical layers to the corresponding quantum versions. We conduct numerical experiments on image classification datasets such as the MNIST, FashionMNIST and CIFAR-10 datasets to demonstrate the change of performance brought by the systematic introduction of quantum components. Through this framework, our research sheds new light on the design of future quantum neural network models where it could be more favourable to search for methods and frameworks that harness the advantages from both the classical and quantum worlds.

quant-ph↗

arXiv2024

Quantum Hamiltonian Embedding of Images for Data Reuploading Classifiers

When applying quantum computing to machine learning tasks, one of the first considerations is the design of the quantum machine learning model itself. Conventionally, the design of quantum machine learning algorithms relies on the ``quantisation" of classical learning algorithms, such as using quantum linear algebra to implement important subroutines of classical algorithms, if not the entire algorithm, seeking to achieve quantum advantage through possible run-time accelerations brought by quantum computing. However, recent research has started questioning whether quantum advantage via speedup is the right goal for quantum machine learning [1]. Research also has been undertaken to exploit properties that are unique to quantum systems, such as quantum contextuality, to better design quantum machine learning models [2]. In this paper, we take an alternative approach by incorporating the heuristics and empirical evidences from the design of classical deep learning algorithms to the design of quantum neural networks. We first construct a model based on the data reuploading circuit [3] with the quantum Hamiltonian data embedding unitary [4]. Through numerical experiments on images datasets, including the famous MNIST and FashionMNIST datasets, we demonstrate that our model outperforms the quantum convolutional neural network (QCNN)[5] by a large margin (up to over 40% on MNIST test set). Based on the model design process and numerical results, we then laid out six principles for designing quantum machine learning models, especially quantum neural networks.

quant-ph↗

arXiv2024

Quantum computer error structure probed by quantum error correction syndrome measurements

With quantum devices rapidly approaching qualities and scales needed for fault tolerance, the validity of simplified error models underpinning the study of quantum error correction needs to be experimentally evaluated. In this work, we have assessed the performance of IBM superconducting quantum computer devices implementing heavy-hexagon code syndrome measurements with increasing circuit sizes up to 23 qubits, against the error assumptions underpinning code threshold calculations. Circuit operator change rate statistics in the presence of depolarizing and biased noise were modelled using analytic functions of error model parameters. Data from 16 repeated syndrome measurement cycles was found to be inconsistent with a uniform depolarizing noise model, favouring instead biased and inhomogeneous noise models. Spatial-temporal correlations investigated via $Z$ stabilizer measurements revealed significant temporal correlation in detection events. These results highlight the non-trivial structure which may be present in the noise of quantum error correction circuits, revealed by operator measurement statistics, and support the development of noise-tailored codes and decoders to adapt.

quant-ph↗

arXiv2023

Arbitrary Ground State Observables from Quantum Computed Moments

The determination of ground state properties of quantum systems is a fundamental problem in physics and chemistry, and is considered a key application of quantum computers. A common approach is to prepare a trial ground state on the quantum computer and measure observables such as energy, but this is often limited by hardware constraints that prevent an accurate description of the target ground state. The quantum computed moments (QCM) method has proven to be remarkably useful in estimating the ground state energy of a system by computing Hamiltonian moments with respect to a suboptimal or noisy trial state. In this paper, we extend the QCM method to estimate arbitrary ground state observables of quantum systems. We present preliminary results of using QCM to determine the ground state magnetisation and spin-spin correlations of the Heisenberg model in its various forms. Our findings validate the well-established advantage of QCM over existing methods in handling suboptimal trial states and noise, extend its applicability to the estimation of more general ground state properties, and demonstrate its practical potential for solving a wide range of problems on near-term quantum hardware.

quant-ph↗

arXiv2023

Precision ground-state energy calculation for the water molecule on a superconducting quantum processor

The accurate computation of properties of large molecular systems is classically infeasible and is one of the applications in which it is hoped that quantum computers will demonstrate an advantage over classical devices. However, due to the limitations of present-day quantum hardware, variational-hybrid algorithms introduced to tackle these problems struggle to meet the accuracy and precision requirements of chemical applications. Here, we apply the Quantum Computed Moments (QCM) approach combined with a variety of noise-mitigation techniques to an 8 qubit/spin-orbital representation of the water molecule (H$_2$O). A noise-stable improvement on the variational result for a 4-excitation trial-state (circuit depth 25, 22 CNOTs) was obtained, with the ground-state energy computed to be within $1.4\pm1.2$ mHa of exact diagonalisation in the 14 spin-orbital basis. Thus, the QCM approach, despite an increased number of measurements and noisy quantum hardware (CNOT error rates c.1% corresponding to expected error rates on the trial-state circuit of order 20%), is able to determine the ground-state energy of a non-trivial molecular system at the required accuracy (c.0.1%). To the best of our knowledge, these results are the largest calculations performed on a physical quantum computer to date in terms of encoding individual spin-orbitals producing chemically relevant accuracy, and a promising indicator of how such hybrid approaches might scale to problems of interest in the low-error/fault-tolerant regimes as quantum computers develop.

quant-ph↗

arXiv2023

Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning

Quantum machine learning (QML) is emerging as an application of quantum computing with the potential to deliver quantum advantage, but its realisation for practical applications remains impeded by challenges. Amongst those, a key barrier is the computationally expensive task of encoding classical data into a quantum state, which could erase any prospective speed-ups over classical algorithms. In this work, we implement methods for the efficient preparation of quantum states representing encoded image data using variational, genetic and matrix product state based algorithms. Our results show that these methods can approximately prepare states to a level suitable for QML using circuits two orders of magnitude shallower than a standard state preparation implementation, obtaining drastic savings in circuit depth and gate count without unduly sacrificing classification accuracy. Additionally, the QML models trained and evaluated on approximately encoded data display an increased robustness to adversarially generated input data perturbations. This partial alleviation of adversarial vulnerability, possible due to the "drowning out" of adversarial perturbations while retaining the meaningful large-scale features of the data, constitutes a considerable benefit for approximate state preparation in addition to lessening the requirements of the quantum hardware. Our results, based on simulations and experiments on IBM quantum devices, highlight a promising pathway for the future implementation of accurate and robust QML models on complex datasets relevant for practical applications, bringing the possibility of NISQ-era QML advantage closer to reality.

quant-ph↗