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George Siopsis

Publications and source records attributed to George Siopsis.

At least 37 records · Page 2Linked to original sources
arXiv2024

Quantum Distance Approximation for Persistence Diagrams

Topological Data Analysis methods can be useful for classification and clustering tasks in many different fields as they can provide two dimensional persistence diagrams that summarize important information about the shape of potentially complex and high dimensional data sets. The space of persistence diagrams can be endowed with various metrics such as the Wasserstein distance which admit a statistical structure and allow to use these summaries for machine learning algorithms. However, computing the distance between two persistence diagrams involves finding an optimal way to match the points of the two diagrams and may not always be an easy task for classical computers. In this work we explore the potential of quantum computers to estimate the distance between persistence diagrams, in particular we propose variational quantum algorithms for the Wasserstein distance as well as the $d^{c}_{p}$ distance. Our implementation is a weighted version of the Quantum Approximate Optimization Algorithm that relies on control clauses to encode the constraints of the optimization problem.

quant-ph↗
arXiv2024

Continuous variable quantum computation of the $O(3)$ model in 1+1 dimensions

We formulate the $O(3)$ non-linear sigma model in 1+1 dimensions as a limit of a three-component scalar field theory restricted to the unit sphere in the large squeezing limit. This allows us to describe the model in terms of the continuous variable (CV) approach to quantum computing. We construct the ground state and excited states using the coupled-cluster Ansatz and find excellent agreement with the exact diagonalization results for a small number of lattice sites. We then present the simulation protocol for the time evolution of the model using CV gates and obtain numerical results using a photonic quantum simulator. We expect that the methods developed in this work will be useful for exploring interesting dynamics for a wide class of sigma models and gauge theories, as well as for simulating scattering events on quantum hardware in the coming decades.

quant-ph↗
arXiv2024

Continuous-variable Quantum Boltzmann Machine

We propose a continuous-variable quantum Boltzmann machine (CVQBM) using a powerful energy-based neural network. It can be realized experimentally on a continuous-variable (CV) photonic quantum computer. We used a CV quantum imaginary time evolution (QITE) algorithm to prepare the essential thermal state and then designed the CVQBM to proficiently generate continuous probability distributions. We applied our method to both classical and quantum data. Using real-world classical data, such as synthetic aperture radar (SAR) images, we generated probability distributions. For quantum data, we used the output of CV quantum circuits. We obtained high fidelity and low Kuller-Leibler (KL) divergence showing that our CVQBM learns distributions from given data well and generates data sampling from that distribution efficiently. We also discussed the experimental feasibility of our proposed CVQBM. Our method can be applied to a wide range of real-world problems by choosing an appropriate target distribution (corresponding to, e.g., SAR images, medical images, and risk management in finance). Moreover, our CVQBM is versatile and could be programmed to perform tasks beyond generation, such as anomaly detection.

quant-ph↗
arXiv2024

Solving Power Grid Optimization Problems with Rydberg Atoms

The rapid development of neutral atom quantum hardware provides a unique opportunity to design hardware-centered algorithms for solving real-world problems aimed at establishing quantum utility. In this work, we study the performance of two such algorithms on solving MaxCut problem for various weighted graphs. The first method uses a state-of-the-art machine learning tool to optimize the pulse shape and embedding of the graph using an adiabatic Ansatz to find the ground state. We tested the performance of this method on finding maximum power section task of the IEEE 9-bus power system and obtaining MaxCut of randomly generated problems of size up to 12 on the Aquila quantum processor. To the best of our knowledge, this work presents the first MaxCut results on Quera's Aquila quantum hardware. Our experiments run on Aquila demonstrate that even though the probability of obtaining the solution is reduced, one can still solve the MaxCut problem on cloud-accessed neutral atom quantum hardware. The second method uses local detuning, which is an emergent update on the Aquila hardware, to obtain a near exact realization of the standard QAOA Ansatz with similar performance. Finally, we study the fidelity throughout the time evolution realized in the adiabatic method as a benchmark for the IEEE 9-bus power grid graph state.

quant-ph↗
arXiv2023

Combinatorial optimization with quantum imaginary time evolution

We use Quantum Imaginary Time Evolution (QITE) to solve polynomial unconstrained binary optimization (PUBO) problems. We show that a linear Ansatz yields good results for a wide range of PUBO problems, often outperforming standard classical methods, such as the Goemans-Williamson (GW) algorithm. We obtain numerical results for the Low Autocorrelation Binary Sequences (LABS) and weighted MaxCut combinatorial optimization problems, thus extending an earlier demonstration of successful application of QITE on MaxCut for unweighted graphs. We find the performance of QITE on the LABS problem with a separable Ansatz comparable with p=10 QAOA, and do not see a significant advantage with an entangling Ansatz. On weighted MaxCut, QITE with a separable Ansatz often outperforms the GW algorithm on graphs up to 150 vertices.

quant-ph↗
arXiv2023

Toward coherent quantum computation of scattering amplitudes with a measurement-based photonic quantum processor

In recent years, applications of quantum simulation have been developed to study properties of strongly interacting theories. This has been driven by two factors: on the one hand, needs from theorists to have access to physical observables that are prohibitively difficult to study using classical computing; on the other hand, quantum hardware becoming increasingly reliable and scalable to larger systems. In this work, we discuss the feasibility of using quantum optical simulation for studying scattering observables that are presently inaccessible via lattice QCD and are at the core of the experimental program at Jefferson Lab, the future Electron-Ion Collider, and other accelerator facilities. We show that recent progress in measurement-based photonic quantum computing can be leveraged to provide deterministic generation of required exotic gates and implementation in a single photonic quantum processor.

quant-ph↗
arXiv2023

Approximate Boltzmann Distributions in Quantum Approximate Optimization

Approaches to compute or estimate the output probability distributions from the quantum approximate optimization algorithm (QAOA) are needed to assess the likelihood it will obtain a quantum computational advantage. We analyze output from QAOA circuits solving 7,200 random MaxCut instances, with $n=14-23$ qubits and depth parameter $p \leq 12$, and find that the average basis state probabilities follow approximate Boltzmann distributions: The average probabilities scale exponentially with their energy (cut value), with a peak at the optimal solution. We describe the rate of exponential scaling or "effective temperature" in terms of a series with a leading order term $T \sim C_\mathrm{min}/n\sqrt{p}$, with $C_\mathrm{min}$ the optimal solution energy. Using this scaling we generate approximate output distributions with up to 38 qubits and find these give accurate accounts of important performance metrics in cases we can simulate exactly.

quant-ph↗
arXiv2023

Experimental decoy-state asymmetric measurement-device-independent quantum key distribution over a turbulent high-loss channel

Real-world BB84 Quantum Key Distribution (QKD) systems utilize imperfect devices that introduce vulnerabilities to their security, known as side-channel attacks. Measurement-Device-Independent (MDI) QKD authorizes an untrusted third party to make measurements and removes all side-channel attacks. The typical implementations of MDI-QKD employ near symmetric channels which are difficult to realize physically in many practical scenarios such as when asymmetric channel losses are present, normally a consequence of the communication environment. Maritime and satellite-based communications are two such instances in which the channels are characterized by continuously changing losses in different channels. In this work, we perform asymmetric MDI-QKD in a laboratory environment with simulated turbulence using an Acousto-Optic Modulator (AOM) to interrogate the performance of free-space quantum communication. Under turbulent conditions, scattering and beam wandering cause intensity fluctuations which decrease the detected signal-to-noise ratio. Using the 7-intensity optimization method proposed by Wang et al., coupled with Prefixed-Threshold Real-time Selection (P-RTS), we demonstrate enhancement in the secure key rate under turbulent conditions for finite-size decoy-state MDI QKD. Furthermore, we show that P-RTS can yield considerably higher secure key rates for a wide range of atmospheric channel parameters.

quant-ph↗
arXiv2023

Quantum Key Distribution for Critical Infrastructures: Towards Cyber Physical Security for Hydropower and Dams

Hydropower facilities are often remotely monitored or controlled from a centralized remote-control room. Additionally, major component manufacturers monitor the performance of installed components. While these communications enable efficiencies and increased reliability, they also expand the cyber-attack surface. Communications may use the internet to remote control a facility's control systems, or it may involve sending control commands over a network from a control room to a machine. The content could be encrypted and decrypted using a public key to protect the communicated information. These cryptographic encoding and decoding schemes have been shown to be vulnerable, a situation which is being exacerbated as more advances are made in computer technologies such as quantum computing. In contrast, quantum key distribution (QKD) is not based upon a computational problem, and offers an alternative to conventional public-key cryptography. Although the underlying mechanism of QKD ensures that any attempt by an adversary to observe the quantum part of the protocol will result in a detectable signature as an increased error rate, potentially even preventing key generation, it serves as a warning for further investigation. When the error rate is low enough and enough photons have been detected, a shared private key can be generated known only to the sender and receiver. We describe how this novel technology and its several modalities could benefit the critical infrastructures of dams or hydropower facilities. The presented discussions may be viewed as a precursor to a quantum cybersecurity roadmap for the identification of relevant threats and mitigation.

quant-ph↗
arXiv2023

Toward quantum computations of the $O(3)$ model using qumodes

We express the discrete 1+1-dimensional $O(3)$ non-linear sigma model (NL$σ$M) in a form well-suited for the continuous variable approach to quantum computing. Within the Schwinger boson formulation, we need two qumodes (quantum-mechanical oscillators) at each lattice site. We envision that it might be possible to reach the scaling regime of this model and observe asymptotic freedom on near-term photonic quantum devices in the coming decade.

hep-lat↗
arXiv2023

Experimentally Realizable Continuous-variable Quantum Neural Networks

Continuous-variable (CV) quantum computing has shown great potential for building neural network models. These neural networks can have different levels of quantum-classical hybridization depending on the complexity of the problem. Previous work on CV neural network protocols required the implementation of non-Gaussian operators in the network. These operators were used to introduce non-linearity, an essential feature of neural networks. However, these protocols are hard to execute experimentally. We built a CV hybrid quantum-classical neural network protocol that can be realized experimentally with current photonic quantum hardware. Our protocol uses Gaussian gates only with the addition of ancillary qumodes. We implemented non-linearity through repeat-until-success measurements on ancillary qumodes. To test our neural network, we studied canonical machine learning and quantum computer problems in a supervised learning setting -- state preparation, curve fitting, and classification problems. We achieved high fidelity in state preparation of single-photon (99.9%), cat (99.8%), and Gottesman-Kitaev-Preskill (93.9%) states, a well-fitted curve in the presence of noise at a cost of less than 1%, and more than 95% accuracy in classification problems. These results bode well for real-world applications of CV quantum neural networks.

quant-ph↗
arXiv2023

Graph decomposition techniques for solving combinatorial optimization problems with variational quantum algorithms

The quantum approximate optimization algorithm (QAOA) has the potential to approximately solve complex combinatorial optimization problems in polynomial time. However, current noisy quantum devices cannot solve large problems due to hardware constraints. In this work, we develop an algorithm that decomposes the QAOA input problem graph into a smaller problem and solves MaxCut using QAOA on the reduced graph. The algorithm requires a subroutine that can be classical or quantum--in this work, we implement the algorithm twice on each graph. One implementation uses the classical solver Gurobi in the subroutine and the other uses QAOA. We solve these reduced problems with QAOA. On average, the reduced problems require only approximately 1/10 of the number of vertices than the original MaxCut instances. Furthermore, the average approximation ratio of the original MaxCut problems is 0.75, while the approximation ratios of the decomposed graphs are on average of 0.96 for both Gurobi and QAOA. With this decomposition, we are able to measure optimal solutions for ten 100-vertex graphs by running single-layer QAOA circuits on the Quantinuum trapped-ion quantum computer H1-1, sampling each circuit only 500 times. This approach is best suited for sparse, particularly $k$-regular graphs, as $k$-regular graphs on $n$ vertices can be decomposed into a graph with at most $\frac{nk}{k+1}$ vertices in polynomial time. Further reductions can be obtained with a potential trade-off in computational time. While this paper applies the decomposition method to the MaxCut problem, it can be applied to more general classes of combinatorial optimization problems.

quant-ph↗
arXiv2023

Experimental free-space quantum key distribution over a turbulent high-loss channel

Free-space quantum cryptography plays an integral role in realizing a global-scale quantum internet system. Compared to fiber-based communication networks, free-space networks experience significantly less decoherence and photon loss due to the absence of birefringent effects in the atmosphere. However, the atmospheric turbulence contributes to deviation in transmittance distribution, which introduces noise and channel loss. Several methods have been proposed to overcome the low signal-to-noise ratio. Active research is currently focused on establishing secure and practical quantum communication in a high-loss channel, and enhancing the secure key rate by implementing bit rejection strategies when the channel transmittance drops below a certain threshold. By simulating the atmospheric turbulence using an acousto-optical-modulator (AOM) and implementing the prefixed-threshold real-time selection (P-RTS) method, our group performed finite-size decoy-state Bennett-Brassard 1984 (BB84) quantum key distribution (QKD) protocol for 19 dB channel loss. With better optical calibration and efficient superconducting nano-wire single photon detector (SNSPD), we have extended our previous work to 40 dB channel loss characterizing the transmittance distribution of our system under upper moderate turbulence conditions.

quant-ph↗
arXiv2023

Quantum Computation of Phase Transition in Interacting Scalar Quantum Field Theory

It has been demonstrated that the critical point of the phase transition in scalar quantum field theory with a quartic interaction in one space dimension can be approximated via a Gaussian Effective Potential (GEP). We discuss how this critical point can be estimated using quantum hardware. We perform quantum computations with various lattice sizes and obtain evidence of a transition from a symmetric to a symmetry-broken phase. We use both discrete- and continuous-variable quantum computation. We implement the ten-site case on IBM quantum hardware using the Variational Quantum Eigensolver (VQE) algorithm to minimize the GEP and identify lattice level-crossings. These are extrapolated via simulations to find the continuum critical point.

quant-ph↗
arXiv2023

Solving MaxCut with Quantum Imaginary Time Evolution

We introduce a method to solve the MaxCut problem efficiently based on quantum imaginary time evolution (QITE). We employ a linear Ansatz for unitary updates and an initial state involving no entanglement, as well as an imaginary-time-dependent Hamiltonian interpolating between a given graph and a subgraph with two edges excised. We apply the method to thousands of randomly selected graphs with up to fifty vertices. We show that our algorithm exhibits a 93% and above performance converging to the maximum solution of the MaxCut problem for all considered graphs. Our results compare favorably with the performance of classical algorithms, such as the greedy and Goemans-Williamson algorithms. We also discuss the overlap of the final state of the QITE algorithm with the ground state as a performance metric, which is a quantum feature not shared by other classical algorithms. This metric can be improved by introducing higher-order Ansaetze and entangled initial states.

quant-ph↗
arXiv2023

Non-Abelian Anyons with Rydberg Atoms

We study the emergence of topological matter in two-dimensional systems of neutral Rydberg atoms in Ruby lattices. While Abelian anyons have been predicted in such systems, non-Abelian anyons, which would form a substrate for fault-tolerant quantum computing, have not been generated. To generate anyons with non-Abelian braiding statistics, we consider systems with mixed-boundary punctures. We obtain the topologically distinct ground states of the system numerically using the iDMRG technique. We discuss how these topological states can be created using ancilla atoms of a different type. We show that a system with 2N+2 punctures and an equal number of ancilla atoms leads to N logical qubits whose Hilbert space is determined by a set of stabilizing conditions on the ancilla atoms. Quantum gates can be implemented using a set of gates acting on the ancilla atoms that commute with the stabilizers and realize the braiding group of non-Abelian Ising anyons.

quant-ph↗
arXiv2022

Quantum Persistent Homology for Time Series

Persistent homology, a powerful mathematical tool for data analysis, summarizes the shape of data through tracking topological features across changes in different scales. Classical algorithms for persistent homology are often constrained by running times and memory requirements that grow exponentially on the number of data points. To surpass this problem, two quantum algorithms of persistent homology have been developed based on two different approaches. However, both of these quantum algorithms consider a data set in the form of a point cloud, which can be restrictive considering that many data sets come in the form of time series. In this paper, we alleviate this issue by establishing a quantum Takens's delay embedding algorithm, which turns a time series into a point cloud by considering a pertinent embedding into a higher dimensional space. Having this quantum transformation of time series to point clouds, then one may use a quantum persistent homology algorithm to extract the topological features from the point cloud associated with the original times series.

quant-ph↗
arXiv2022

Dynamic attenuation scheme in measurement-device-independent quantum key distribution over turbulent channels

Measurement-device-independent quantum key distribution (MDI QKD) offers great security in practice because it removes all detector side channels. However, conducting MDI QKD over free-space channels is challenging. One of the largest culprits is the mismatched transmittance of the two independent turbulent channels causing a reduced Hong-Ou-Mandel visibility and thus a lower secret key rate. Here we introduce a dynamic attenuation scheme, where the transmittance of each of the two channels is monitored in real time by transmitting bright light pulses from each users to the measurement device. Based on the measured channel transmittance, a suitable amount of attenuation is introduced to the low-loss channel at the measurement device. Our simulation results show a significant improvement of QKD performance, especially when using short raw keys.

quant-ph↗