New frontier of machine learning and computation initiated by quantum annealing

Hadi Salloum, Ali Salloum, Manuel Mazzara +1 more

Quantum computing (QC) has become a fascinating and popular topic due to its broad range of applications, particularly in machine learning (ML). The intersection of QC and ML is known as Quantum Machine Learning (QML). QML is a field that investigates how QC can enhance ML, making it one of the most exciting areas of research due to the potential of QC in solving complex problems. In this paper, we demonstrate the Quantum Annealing (QA) approach to improving ML in binary classification tasks. We implemented the QBoost algorithm on a few datasets using D-Wave’s quantum computers, specifically the Advantage 1 and Advantage 2 prototypes, incorporating the new feature Fast Anneal.

Hadi Salloum, Hamza Shafee Aldaghstany, Osama Orabi +3 more

Elizabeth Behrman, James E. Steck

Commercial quantum annealing (QA) machines are now being built with hundreds of quantum bits (qubits). These are used as analog computers, to solve optimization problems by annealing to an unknown ground state (the solution), given the Hamiltonian for that problem. We propose and develop a new approach, in which we use machine learning to do the inverse problem: to find the Hamiltonian that will produce a given, desired ground state. We demonstrate successful learning to produce a desired fully entangled state for a two-qubit system, then bootstrap to do the same for three, four, five and six qubits; the amount of additional learning necessary decreases. With these new capabilities the computing possibilities for QA arrays are greatly expanded.

Rajdeep Kumar Nath, Himanshu Thapliyal, Travis S. Humble

Ameya Khot, Taesic Kim, Alve Rahman Akash +2 more

Quantum machine learning (QML) is transforming the field of machine learning (ML), which typically utilizes a quantum computing such as gate model-based and annealing-based computing. Recently, Ising solver chips have been developed to emulate the superposition and entanglement properties of quantum physics using the current silicon manufacturing technologies. The chips have potential to solve complex combinatorial optimization problems (COP) with significantly low power consumption and price. This paper proposes quantum-inspired ML (QiML) framework that provides an approach to train the traditional ML models using a physics-based Ising solver chip to develop QiMLs. To validate the feasibility of the proposed framework, a quantum-inspired support vector machine (QiSVM) is developed and the results are compared with a quantum SVM trained by a commercial D-Wave quantum annealer and a conventional SVM to solve real-world problems. The proposed QiML framework will open new frontiers for advancing ML technologies for industry cyber-physical system applications.

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