QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation

Booth, Michael; Berwald, Jesse; Chukwu, Uchenna; Dawson, John; Dridi, Raouf; Le, DeYung; Wainger, Mark; Reinhardt, Steven P.

doi:10.48550/arxiv.2005.11294

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…Each blob contains 90 data points, with a standard deviation of 2. The coordinates of all data points are used as input for the selector algorithm, which then chooses k representative points by minimizing the cost function in Equation 3 using the D-Wave decomposing solver qbsolv [19,20] which uses a modified tabu algorithm [21] to minimize the objective function. In this example, we have generated two clusters of data points and tasked the selector algorithm with choosing k = 2 representative points.…”

Section: Experiments With Synthetic Datamentioning

confidence: 99%

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A Quantum-Inspired Binary Optimization Algorithm for Representative Selection

Hughes¹,

Baker²,

Radha³

2023

Preprint

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Advancements in quantum computing are fuelling emerging applications across disciplines, including finance, where quantum and quantum-inspired algorithms can now make market predictions, detect fraud, and optimize portfolios. Expanding this toolbox, we propose the selector algorithm: a method for selecting the most representative subset of data from a larger dataset. The selected subset includes data points that simultaneously meet the two requirements of being maximally close to neighboring data points and maximally far from more distant data points where the precise notion of distance is given by any kernel or generalized similarity function. The cost function encoding the above requirements naturally presents itself as a Quadratic Unconstrained Binary Optimization (QUBO) problem, which is well-suited for quantum optimization algorithms -including quantum annealing. While the selector algorithm has applications in multiple areas, it is particularly useful in finance, where it can be used to build a diversified portfolio from a more extensive selection of assets. After experimenting with synthetic datasets, we show two use cases for the selector algorithm with real data: (1) approximately reconstructing the NASDAQ 100 index using a subset of stocks, and (2) diversifying a portfolio of cryptocurrencies. In our analysis of use case (2), we compare the performance of two quantum annealers provided by D-Wave Systems.

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Section: Experiments With Synthetic Datamentioning

confidence: 99%

“…In this first application on real financial data, we use the selector algorithm to approximately reconstruct the NASDAQ 100 by choosing a subset (S k ) of k stocks from all 102 stocks in the market index. We perform the optimization with the D-Wave decomposing solver qbsolv [19,20].…”

Section: Use Case: Building a Diversified Portfoliomentioning

confidence: 99%

A Quantum-Inspired Binary Optimization Algorithm for Representative Selection

Hughes¹,

Baker²,

Radha³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…An important QML formulation is known as the quadratic unconstrained binary optimisation (QUBO) [234,235]. This is generally a classical problem, but using the Ising model -see [236] -this can be solved on a quantum computer [237,238]. A common demonstration of the latter is the max cut problem [239] -see Fig 6 . There are solutions for the QUBO problem on both gate-based quantum computers and quantum annealers [240][241][242], and this general concept has seen use in many sectors [243].…”

Section: Quantum-native Problemsmentioning

confidence: 99%

Quantum Machine Learning: from physics to software engineering

Melnikov¹,

Kordzanganeh²,

Alodjants³

et al. 2023

Preprint

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Quantum machine learning (QML) is a new, rapidly growing, and fascinating area of research where quantum information science and quantum technologies meet novel machine learning and artificial intelligent facilities. A comprehensive analysis of the main directions of current QML methods and approaches is performed in this review. The aim of our work is twofold. First, we show how classical machine learning approach can help improve the facilities of quantum computers and simulators available today. It is most important due to the modern noisy intermediate-scale quantum (NISQ) era of quantum technologies. In particular, the classical machine learning approach allows optimizing quantum hardware for achieving desired quantum states by implementing quantum devices. Second, we discuss how quantum algorithms and quantum computers may be useful for solving keystone classical machine learning tasks. Currently, quantum-inspired algorithms, which use a quantum approach to classical information processing, represent a powerful tool in software engineering for improving classical computation capacities. In this work, we discuss various quantum neural network capabilities that can be implemented in quantum-classical training algorithms for variational circuits. It is expected that quantum computers will be involved in routine machine learning procedures. In this sense, we are showing how it is essential to elucidate the speedup problem for random walks on arbitrary graphs, which are used in both classical and quantum algorithms. Quantum technologies enhanced by machine learning in fundamental and applied quantum physics, as well as quantum tomography and photonic quantum computing, are also covered.

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“…-Circuit optimizer, back-end compilers and interpreters. For example Tket [24], TriQ [25], and Qbsolv [26].…”

Section: B the State Of Quantum Software Todaymentioning

confidence: 99%

A Tool For Debugging Quantum Circuits

Metwalli¹,

Meter²

2022

Preprint

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As the scale of quantum programs grows to match that of classical software, the nascent field of quantum software engineering must mature and tools such as debuggers will become increasingly important. However, developing a quantum debugger is challenging due to the nature of a quantum computer; sneaking a peek at the value of a quantum state will cause either partial or complete collapse of the superposition and may destroy the necessary entanglement. As a first step to developing a full quantum circuit debugger, we have designed and implemented a quantum circuit debugging tool. The tool allows the user to divide the circuit vertically or horizontally into smaller chunks known as slices, and manage their simulation or execution for either interactive debugging or automated testing. The tool also enables developers to track gates within the overall circuit and each chunk to understand their behavior better. Feedback on usefulness and usability from early users shows that using the tool to slice and test their circuits has helped make the debugging process more time-efficient for them.

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QCI Qbsolv Delivers Strong Classical Performance for Quantum-Ready Formulation

Cited by 5 publications

References 9 publications

A Quantum-Inspired Binary Optimization Algorithm for Representative Selection

A Quantum-Inspired Binary Optimization Algorithm for Representative Selection

Quantum Machine Learning: from physics to software engineering

A Tool For Debugging Quantum Circuits

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