Cost function Archives - IEEE Transactions on Quantum Engineering

Parameter Analysis and Optimization of Layer Fidelity for Quantum Processor Benchmarking at Scale

February 26, 2026Maria Jose Lozano Palacio, Hasan Nayfeh, Matthew Ware, David C. McKayQuantum ComputingBenchmark testing, Cost function, Crosstalk, Logic gates, Monitoring, Object recognition, Performance evaluation, Protocols, Qubit, Stability analysis

Abstract: With the continued scaling of quantum processors, holistic benchmarks are essential for extensively evaluating device performance. Layer fidelity is a benchmark well-suited to assessing processor performance at scale. Key advantages of this benchmark include its natural alignment with randomized benchmarking (RB) procedures, crosstalk awareness, fast measurements over large numbers of qubits, high signal-to-noise ratio, […]

Parallel Variational Quantum Algorithms With Gradient-Informed Restart to Speed Up Optimization in the Presence of Barren Plateaus

February 10, 2026Daniel Mastropietro, Georgios Korpas, Vyacheslav Kungurtsev, Jakub MarecekQuantum SoftwareApproximation algorithms, Cost function, Markov processes, Neural networks, Noise, Noise measurement, Quantum algorithm, Quantum circuit, Quantum entanglement, Simulated annealing

Abstract: Inspired by the Fleming–Viot stochastic process, we propose a parallel implementation with restart of variational quantum algorithms, with the aim of reducing the time spent by the algorithm in barren plateaus where the optimization direction is unclear. In the Fleming–Viot tradition, parallel searches are called particles. In the proposed approach, the search by a […]

Relative Entropy-Based Training of Quantum Neural Networks

December 1, 2025Subhadeep Mondal, Amit Kumar DuttaQuantum ComputingCost function, Entropy, Indexes, Machine learning, Neural networks, Noise, Quantum channels, Quantum computing, Qubit, Training

Abstract: Quantum neural networks (QNNs) are gaining attention as versatile models for quantum machine learning, but training them effectively remains a challenge. Most existing approaches, such as quantum multilayer perceptrons, use fidelity-based cost functions. While well-suited for pure states, these measures are less reliable when inputs and outputs are mixed states—a situation common in learning […]

Dissipative Variational Quantum Algorithms for Gibbs State Preparation

December 4, 2024Yigal Ilin, Itai AradQuantum ComputingCost function, Hardware, Logic gates, Noise, Quantum algorithm, Quantum circuit, Quantum computing, Qubit, Resilience, Toy manufacturing industry

In recent years, variational quantum algorithms have gained significant attention due to their adaptability and efficiency on near-term quantum hardware. They have shown potential in a variety of tasks, including linear algebra, search problems, Gibbs, and ground state preparation. Nevertheless, the presence of noise in current day quantum hardware severely limits their performance. In this […]

Approximate Solutions of Combinatorial Problems via Quantum Relaxations

July 4, 2024Bryce Fuller, Charles Hadfield, Jennifer R. Glick, Takashi Imamichi, Toshinari Itoko, Richard J. Thompson, Yang Jiao, Marna M. Kagele, Adriana W. Blom-Schieber, Rudy Raymond, Antonio MezzacapoRegularApproximate Ratio, Approximate Solution, Binary Optimization, Classification Algorithms, Combinatorial Problem, Cost function, Cut Value, Eigenstates, Energy Relaxation, Estimation Algorithm, Hamiltonian Operator, Numerical Simulations, Optimal Cut, Optimal Objective Value, Pauli Matrices, Quantum computing, Quantum Information, Quantum state, Random Code, Relaxation Spectrum, Relaxed State

This article delves into the role of the quantum Fisher information matrix (FIM) in enhancing the performance of parameterized quantum circuit (PQC)-based reinforcement learning agents. While previous studies have highlighted the effectiveness of PQC-based policies preconditioned with the quantum FIM in contextual bandits, its impact in broader reinforcement learning contexts, such as Markov decision processes, […]

Relation Between Quantum Advantage in Supervised Learning and Quantum Computational Advantage

December 26, 2023Jordi Pérez-Guijarro; Alba Pagés-Zamora; Javier R. FonollosaQuantum ComputingComplexity theory, Cost function, Machine learning, Quantum algorithm, Supervised learning, Task analysis, Training

The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantages are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems. This […]

Relation Between Quantum Advantage in Supervised Learning and Quantum Computational Advantage

December 26, 2023Jordi Pérez-Guijarro, Alba Pagés-Zamora, Javier R. FonollosaQuantum ComputingComplexity theory, Cost function, Machine learning, Quantum algorithm, Supervised learning, Task analysis, Training

Relation Between Quantum Advantage in Supervised Learning and Quantum Computational Advantage

December 26, 2023Jordi Pérez-GuijarroQuantum ComputingComplexity theory, Cost function, Machine learning, Quantum algorithm, Supervised learning, Task analysis, Training

Quantum Annealing Methods and Experimental Evaluation to the Phase-Unwrapping Problem in Synthetic Aperture Radar Imaging

November 2, 2022Khaled A. Helal KelanyOpen Access, Quantum ComputingAnnealing, Cost function, Licenses, Phased arrays, Quantum annealing, Qubit, Synthetic aperture radar

The focus of this work is to explore the use of quantum annealing solvers for the problem of phase unwrapping of synthetic aperture radar (SAR) images. Although solutions to this problem exist based on network programming, these techniques do not scale well to larger sized images. Our approach involves formulating the problem as a quadratic […]

Toward Quantum Gate-Model Heuristics for Real-World Planning Problems

October 13, 2020Tobias Stollenwerk; Stuart Hadfield; Zhihui WangOpen Access, Quantum InformationAirports, Annealing, Cost function, Logic gates, Planning, Quantum computing

Many challenging scheduling, planning, and resource allocation problems come with real-world input data and hard problem constraints, and reduce to optimizing a cost function over a combinatorially defined feasible set, such as colorings of a graph. Toward tackling such problems with quantum computers using quantum approximate optimization algorithms, we present novel efficient quantum alternating operator […]