Computing graph edit distance on quantum devices

Incudini, Massimiliano; Tarocco, Fabio; Mengoni, Riccardo; Pierro, Alessandra Di; Mandarino, Antonio

doi:10.1007/s42484-022-00077-x

Cited by 5 publications

(4 citation statements)

References 70 publications

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“…Several methods are known to embed data into a quantum system [42][43][44][45]. In this work we follow the procedure presented in [43] and known as data re-uploading, which allows us to encode multi-dimensional variables into the parametric gates of a circuit as part of their parameters.…”

Section: Qml In a Nutshellmentioning

confidence: 99%

Multi-variable integration with a variational quantum circuit

Cruz-Martinez,

Robbiati,

Carrazza

2024

Quantum Sci. Technol.

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In this work we present a novel strategy to evaluate multi-variable integrals with quantum circuits. The procedure first encodes the integration variables into a parametric circuit. The obtained circuit is then derived with respect to the integration variables using the parameter shift rule technique. The observable representing the derivative is then used as the predictor of the target integrand function following a quantum machine learning approach. The integral is then estimated using the fundamental theorem of integral calculus by evaluating the original circuit. Embedding data according to a reuploading strategy, multi-dimensional variables can be easily encoded into the circuit’s gates and then individually taken as targets while deriving the circuit. These techniques can be exploited to partially integrate a function or to quickly compute parametric integrands within the training hyperspace.

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Section: Qml In a Nutshellmentioning

confidence: 99%

Multi-variable integration with a variational quantum circuit

Cruz-Martinez,

Robbiati,

Carrazza

2024

Quantum Sci. Technol.

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“…Such an approach has been shown to be ineffective (Thanasilp et al 2022). A different approach to optimize the parametric quantum circuit, choosing the basis gates at each point of the circuit as a combinatorial optimization algorithm (possibly a meta-heuristics) has been proposed by Incudini et al (2022); Altares-López et al (2021).…”

Section: Quantum Kernels Implementationmentioning

confidence: 99%

“…The user can take advantage of the efficient optax optimization library. Moreover, we can use Structure Learning techniques (Incudini et al 2022) to optimize the generators of the transformation using a combinatorial-optimization-based technique such as Simulated Annealing (Kirkpatrick et al 1983) or Genetic Algorithms (Forrest 1996).…”

Section: Implement Quantum Kernelsmentioning

confidence: 99%

Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning

Marcantonio

Incudini

Tezza

et al. 2023

Quantum Mach. Intell.

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Exploiting the properties of quantum information to the benefit of machine learning models is perhaps the most active field of research in quantum computation. This interest has supported the development of a multitude of software frameworks (e.g. Qiskit, Pennylane, Braket) to implement, simulate, and execute quantum algorithms. Most of them allow us to define quantum circuits, run basic quantum algorithms, and access low-level primitives depending on the hardware such software is supposed to run. For most experiments, these frameworks have to be manually integrated within a larger machine learning software pipeline. The researcher is in charge of knowing different software packages, integrating them through the development of long code scripts, analyzing the results, and generating the plots. Long code often leads to erroneous applications, due to the average number of bugs growing proportional with respect to the program length. Moreover, other researchers will struggle to understand and reproduce the experiment, due to the need to be familiar with all the different software frameworks involved in the code script. We propose QuASK, an open-source quantum machine learning framework written in Python that aids the researcher in performing their experiments, with particular attention to quantum kernel techniques. QuASK can be used as a command-line tool to download datasets, pre-process them, quantum machine learning routines, analyze and visualize the results. QuASK implements most state-of-the-art algorithms to analyze the data through quantum kernels, with the possibility to use projected kernels, (gradient-descent) trainable quantum kernels, and structure-optimized quantum kernels. Our framework can also be used as a library and integrated into pre-existing software, maximizing code reuse.

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“…QUBO models represent a powerful tool for solving complex optimization problems across various domains [11,12]. In physics, they offer valuable insights into phenomena like spin glass theory, quantum magnetism, and lattice gauge theory [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Diversifying Investments and Maximizing Sharpe Ratio: A Novel Quadratic Unconstrained Binary Optimization Formulation

Mattesi,

Asproni,

Mattia

et al. 2024

Quantum Reports

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The optimization of investment portfolios represents a pivotal task within the field of financial economics. Its objective is to identify asset combinations that meet specified criteria for return and risk. Traditionally, the maximization of the Sharpe Ratio, often achieved through quadratic programming, has constituted a popular approach for this purpose. However, real-world scenarios frequently necessitate more complex considerations, particularly in relation to portfolio diversification with a view to mitigating sector-specific risks and enhancing stability. The incorporation of diversification alongside the Sharpe Ratio into the optimization model creates a joint optimization task, which can be formulated as Quadratic Unconstrained Binary Optimization (QUBO) and addressed using quantum annealing or hybrid computing techniques. These techniques offer promising solutions. We present a novel QUBO formulation for this optimization, detailing its mathematical formulation and demonstrating its advantages over classical methods, particularly in handling diversification objectives. By leveraging available QUBO solvers and hybrid approaches, we explore the feasibility of handling large-scale problems while highlighting the importance of diversification in achieving robust portfolio performance. We finally elaborate on the results showing the trade-off between the observed values of the portfolio’s Sharpe Ratio and diversification, as a natural consequence of solving a multi-objective optimization problem.

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Computing graph edit distance on quantum devices

Cited by 5 publications

References 70 publications

Multi-variable integration with a variational quantum circuit

Multi-variable integration with a variational quantum circuit

Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning

Diversifying Investments and Maximizing Sharpe Ratio: A Novel Quadratic Unconstrained Binary Optimization Formulation

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