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2024 International Conference on quantum Computing and Engineering

作者： Periyasamy, Maniraman Plinge, Axel Mutschler, Christopher Scherer, Daniel D. Mauerer, Wolfgang Fraunhofer Inst Integrated Circuits IIS Fraunhofer IIS Nurnberg Germany Tech Univ Appl Sci Regensburg Regensburg Germany Siemens AG Corp Res Munich Germany

ISBN: (纸本)9798331541378

The study of variational quantum algorithms (VQCs) has received significant attention from the quantum computing community in recent years. These hybrid algorithms, utilizing both classical and quantum components, are well-suited for noisy intermediate-scale quantum devices. Though estimating exact gradients using the parameter-shift rule to optimize the VQCs is realizable in NISQ devices, they do not scale well for larger problem sizes. The computational complexity, in terms of the number of circuit evaluations required for gradient estimation by the parameter-shift rule, scales linearly with the number of parameters in VQCs. On the other hand, techniques that approximate the gradients of the VQCs, such as the simultaneous perturbation stochastic approximation (SPSA), do not scale with the number of parameters but struggle with instability and often attain suboptimal solutions. In this work, we introduce a novel gradient estimation approach called Guided-SPSA, which meaningfully combines the parameter-shift rule and SPSA-based gradient approximation. The Guided-SPSA results in a 15% to 25% reduction in the number of circuit evaluations required during training for a similar or better optimality of the solution found compared to the parameter-shift rule. The Guided-SPSA outperforms standard SPSA in all scenarios and outperforms the parameter-shift rule in scenarios such as suboptimal initialization of the parameters. We demonstrate numerically the performance of Guided-SPSA on different paradigms of quantum machine learning, such as regression, classification, and reinforcement learning.

关键词： gradient estimation SPSA parameter-shift rule variational quantum algorithms quantum regression quantum classification quantum reinforcement learning

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Recommending Solution Paths for Solving Optimization Problems with quantum Computing

Recommending Solution Paths for Solving Optimization Problem...

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2nd IEEE International Conference on quantum Software (IEEE QSW)

作者： Poggel, Benedikt Quetschlich, Nils Burgholzer, Lukas Wille, Robert Lorenz, Jeanette Miriam Fraunhofer Inst Cognit Syst IKS Munich Germany Tech Univ Munich Chair Design Automat Munich Germany Software Competence Ctr Hagenberg GmbH SCCH Hagenberg Austria Johannes Kepler Univ Linz Inst Integrated Circuits Linz Austria

ISBN: (纸本)9798350304794

Solving real-world optimization problems with quantum computing requires choosing between a large number of options concerning formulation, encoding, algorithm and hardware. Finding good solution paths is challenging for end users and researchers alike. We propose a framework designed to identify and recommend the best-suited solution paths in an automated way. This introduces a novel abstraction layer that is required to make quantum-computing-assisted solution techniques accessible to end users without requiring a deeper knowledge of quantum technologies. State-of-the-art hybrid algorithms, encoding and decomposition techniques can be integrated in a modular manner and evaluated using problem-specific performance metrics. Equally, tools for the graphical analysis of variational quantum algorithms are developed. Classical, fault tolerant quantum and quantum-inspired methods can be included as well to ensure a fair comparison resulting in useful solution paths. We demonstrate and validate our approach on a selected set of options and illustrate its application on the capacitated vehicle routing problem (CVRP). We also identify crucial requirements and the major design challenges for the proposed abstraction layer within a quantum-assisted solution workflow for optimization problems.

关键词： quantum computing applied optimization hybrid quantum-classical algorithms variational quantum algorithms design automation abstraction layer

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Invited Paper: Introduction to Hybrid quantum-Classical Programming Using C plus plus quantum Extension 42

Invited Paper: Introduction to Hybrid Quantum-Classical Prog...

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42nd IEEE/ACM International Conference on Computer-Aided Design (ICCAD)

作者： Wu, Xin-Chuan Premaratne, Shavindra Rasch, Kevin Intel Corp Intel Labs Santa Clara CA 95051 USA Intel Corp Intel Labs Hillsboro OR USA

ISBN: (纸本)9798350322255

The rapid development of quantum computing technology has led to an increasing need for efficient and accessible programming tools that can bridge the gap between classical and quantum computing paradigms. In this paper, we introduce the Intel((R)) quantum SDK, a powerful software development kit designed to facilitate hybrid quantum-classical programming using the C++ quantum Extension. The SDK provides a comprehensive set of tools and libraries that enable developers to harness the power of quantum computing while leveraging their existing knowledge of classical programming techniques. By incorporating quantum algorithms and operations within a familiar C++ framework, the Intel quantum SDK empowers programmers to create innovative hybrid applications that can tackle complex problems and drive advancements in various fields, including cryptography, optimization, and materials science. This paper will guide readers through the essential concepts, features, and capabilities of the Intel quantum SDK. This seamless integration of quantum and classical programming paradigms paves the way for the development of innovative and efficient solutions to complex computational challenges, ultimately driving the advancement of quantum computing technology and its real-world applications.

关键词： quantum Computing quantum Compiler variational quantum algorithms

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An Empirical Analysis of Realistic Noise in quantum Neural Networks for Medical Classifications of Tabular, Signal and Imaging Data 5

An Empirical Analysis of Realistic Noise in Quantum Neural N...

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2024 International Conference on quantum Computing and Engineering

作者： Moser, Philipp Maletzky, Alexander Giretzlehner, Michael RISC Software GmbH Res Unit Med Informat Hagenberg Austria

ISBN: (纸本)9798331541378

quantum machine learning has gained significant momentum in recent years, particularly with the use of parameterized quantum circuits that exhibit a certain level of resilience to noise in current NISQ devices. However, the real-world applicability of QML remains an active field of research as transitioning from idealized simulations to real quantum hardware introduces challenges related to data size and noise. Our study empirically analyzes the performance of popular quantum neural networks in ideal, noise-free conditions, but also in the presence of realistic noise, which was modeled to mimic the noisy behavior of current IBM quantum devices. We carefully selected three medical classification tasks that cover common data modalities with varying numbers of features-from tabular cell data to signal sampling points and image pixels. Our findings statistically show that while complex parameterized circuits perform well in noise-free simulations, their performance significantly deteriorates with increasing qubit numbers under noisy conditions. Our study suggests that parameterized quantum circuits of moderate complexity are a favorable choice for future experiments on noisy quantum devices.

关键词： quantum computing machine learning variational quantum algorithms quantum neural networks medical classification noise

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Optimization of Sensor-Placement on Vehicles using quantum-Classical Hybrid Methods 3

Optimization of Sensor-Placement on Vehicles using Quantum-C...

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3rd IEEE International Conference on quantum Computing and Engineering (QCE)

作者： Pramanik, Sayantan Vaidya, Vishnu Malviya, Gajendra Sinha, Sudhir Salsingikar, Shripad Chandra, M. Girish Sridhar, C. V. Mathais, Godfrey Navelkar, Vidyut TATA Consultancy Serv TCS Incubat Mumbai Maharashtra India TATA Consultancy Serv TCS Res Mumbai Maharashtra India

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

The paper presents a quantum method to optimize the placement of sensors on the surface of a vehicle. The problem, as posted in the BMW quantum Computing Challenge 2021, is to arrive at the optimal positions and configurations (type and orientation) of the sensors on the vehicle surface that maximizes coverage of the Region of Interest (RoI), while minimizing the total cost of the selected sensors. The dataset contains approximately 100,000 points in the RoI, with defined measures of criticality (ranging between 0 to 1), distributed over a volume of approximately 40,000 cubic metres around the vehicle. The types of sensors, their coverage parameters and costs are inputs to the problem. The optimization problem is decomposed and solved individually for each side of the vehicle, and the results for the entire vehicle are collated and presented in the paper. The approach considers a quadratic approximation to the maximum set-coverage formulation with the advantage of obtaining the optimal number of sensors as an output from the model itself. The resultant quadratic program is solved using quantum methods, as well as a standard, state-of-the-art Integer Quadratic Program (IQP) solver, respectively. The quadratic approximation consumes qubits that are linear in the number of total configurations and significantly reduces the number of decision variables required in an Ising formulation of the problem. The quantum and classical methods are found to perform on par with each other. A detailed comparison of the results is presented in the paper. Given the combinatorial nature of the optimization problem, the classical approach may become intractable while solving the problem in an integrated manner (instead of solving individually for each side). Under such scenarios, there is a possibility to expect benefits by adopting the proposed strategies with the availability of large-sized quantum computers in the near future.

关键词： variational quantum algorithms ansatz quantum annealing maximum set-coverage sensor-placement

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A Case for Noisy Shallow Gate-based Circuits in quantum Machine Learning

A Case for Noisy Shallow Gate-based Circuits in Quantum Mach...

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IEEE International Conference on Rebooting Computing (ICRC)

作者： Selig, Patrick Murphy, Niall Sundareswaran, Ashwin R. Redmond, David Caton, Simon Univ Coll Dublin Sch Comp Sci Dublin Ireland Univ Coll Dublin Ctr Quantum Engn Sci & Technol Dublin Ireland Equall Labs Dublin Ireland

ISBN: (纸本)9781665423328

There is increasing interest in the development of gate-based quantum circuits for the training of machine learning models. Yet, little is understood concerning the parameters of circuit design, and the effects of noise and other measurement errors on the performance of quantum machine learning models. In this paper, we explore the practical implications of key circuit design parameters (number of qubits, depth etc.) using several standard machine learning datasets and IBM's Qiskit simulator. In total we evaluate over 6500 unique circuits with n approximate to 120700 individual runs. We find that in general shallow (low depth) wide (more qubits) circuit topologies tend to outperform deeper ones in settings without noise. We also explore the implications and effects of different notions of noise and discuss circuit topologies that are more / less robust to noise for classification machine learning tasks. Based on the findings we define guidelines for circuit topologies that show near-term promise for the realisation of quantum machine learning algorithms using gate-based NISQ quantum computer.

关键词： Qiskit quantum Machine Learning variational quantum algorithms NISQ architecture Noise Gate-based Circuits

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Trainability Barriers in Low-Depth QAOA Landscapes 21

Trainability Barriers in Low-Depth QAOA Landscapes

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21st ACM International Conference on Computing Frontiers (CF)

作者： Rajakumar, Joel Golden, John Bartschi, Andreas Eidenbenz, Stephan Univ Maryland College Pk MD 20742 USA Los Alamos Natl Lab Informat Sci Los Alamos NM USA

ISBN: (纸本)9798400705977

The quantum Alternating Operator Ansatz (QAOA) is a prominent variational quantum algorithm for solving combinatorial optimization problems. Its effectiveness depends on identifying input parameters that yield high-quality solutions. However, understanding the complexity of training QAOA remains an under-explored area. Previous results have given analytical performance guarantees for a small, fixed number of parameters. At the opposite end of the spectrum, barren plateaus are likely to emerge at Omega(n) parameters for n qubits. In this work, we study the difficulty of training in the intermediate regime, which is the focus of most current numerical studies and near-term hardware implementations. Through extensive numerical analysis of the quality and quantity of local minima, we argue that QAOA landscapes can exhibit a superpolynomial growth in the number of low-quality local minima even when the number of parameters scales logarithmically with n. This means that the common technique of gradient descent from randomly initialized parameters is doomed to fail beyond small n, and emphasizes the need for good initial guesses of the optimal parameters.

关键词： quantum Alternating Operator Ansatz variational quantum algorithms Gradient Descent

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Measurement-Based Control for Minimizing Energy Functions in quantum Systems 22

Measurement-Based Control for Minimizing Energy Functions in...

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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Clausen, Henrik Glavind Rahman, Salahuddin Abdul Karabacak, Ozkan Wisniewski, Rafal Aalborg Univ Sect Automat & Control Dept Elect Syst Aalborg Denmark Kadir Has Univ Dept Mechatron Engn Istanbul Turkiye

ISBN: (纸本)9781713872344

In variational quantum algorithms (VQAs), the most common objective is to find the minimum energy eigenstate of a given energy Hamiltonian. In this paper, we consider the general problem of finding a sufficient control Hamiltonian structure that, under a given feedback control law, ensures convergence to the minimum energy eigenstate of a given energy function. By including quantum non-demolition (QND) measurements in the loop, convergence to a pure state can be ensured from an arbitrary mixed initial state. Based on existing results on strict control Lyapunov functions, we formulate a semidefinite optimization problem, whose solution defines a non-unique control Hamiltonian, which is sufficient to ensure almost sure convergence to the minimum energy eigenstate under the given feedback law and the action of QND measurements. A numerical example is provided to showcase the proposed methodology. Copyright (c) 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Lyapunov control quantum non-demolition measurements semidefinite programming variational quantum algorithms

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Using optimal control to guide neural-network interpolation of continuously-parameterized gates 5

Using optimal control to guide neural-network interpolation ...

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2024 International Conference on quantum Computing and Engineering

作者： Bhattacharyya, Bikrant An, Fredy Kozbiel, Dominik Goldschmidt, Andy J. Chong, Frederic T. Illinois Math & Sci Acad Aurora IL 60506 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA

ISBN: (纸本)9798331541378

Control synthesis for continuously-parameterized families of quantum gates can enable critical advantages for mid-sized quantum computing applications in advance of fault-tolerance. We combine quantum optimal control with physicsinformed machine learning to efficiently synthesize control surfaces that interpolate among continuously-parameterized gate families. Using optimal control as an active learning strategy to guide pretraining, we bootstrap a physics-informed neural network to achieve rapid convergence to nonlinear control surfaces sufficient for our desired gates. We find our approach is critical for enabling an expressiveness beyond linear interpolation, which is important in cases of hard quantum control. We show in simulation that by adapting our pretraining to use a few reference pulse calibrations, we can apply transfer learning to quickly calibrate our learned control surfaces when devices fluctuate over time. We demonstrate synthesis for one and two qubit gates with one or two parameters, focusing on gate families for variational quantum algorithm (VQA) ansatz. By avoiding the inefficient decomposition of VQA ansatz into basis gate sets, continuous gate families are a potential method to improve the noise robustness of VQAs in the near term. Our framework shows how accessible optimal control tools can be combined with simple machine learning to enable practitioners to achieve 3x speedups for their algorithms by going beyond the standard gate sets.

关键词： pulse-level control quantum optimal control physics-informed machine learning variational quantum algorithms

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Avoiding barren plateaus in the variational determination of geometric entanglement

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quantum SCIENCE AND TECHNOLOGY 2024年第2期9卷 025016-025016页

作者： Zambrano, L. Munoz-Moller, A. D. Munoz, M. Pereira, L. Delgado, A. Barcelona Inst Sci & Technol Inst Ciencies Foton ICFO Barcelona 08860 Spain Univ Jyvaskyla Fac Informat Technol Jyvaskyla Finland Univ Concepcion Inst Milenio Invest Opt Casilla 160-C Concepcion Chile Univ Concepcion Fac Ciencias Fis & Matemat Dept Fis Casilla 160-C Concepcion Chile Univ Concepcion Fac Ciencias Fis & Matemat Dept Ingn Matemat Casilla 160 C Concepcion Chile Inst Fis Fundamental IFF CSIC Calle Serrano 113b Madrid 28006 Spain

The barren plateau (BP) phenomenon is one of the main obstacles to implementing variational quantum algorithms in the current generation of quantum processors. Here, we introduce a method capable of avoiding the BP phenomenon in the variational determination of the geometric measure of entanglement for a large number of qubits. The method is based on measuring compatible two-qubit local functions whose optimization allows for achieving a well-suited initial condition from which a global function can be further optimized without encountering a BP. We analytically demonstrate that the local functions can be efficiently estimated and optimized. Numerical simulations up to 18 qubit GHZ and W states demonstrate that the method converges to the exact value. In particular, the method allows for escaping from BPs induced by hardware noise or global functions defined on high-dimensional systems. Numerical simulations with noise agree with experiments carried out on IBM's quantum processors for seven qubits.

关键词： entanglement barren plateau variational quantum algorithms

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