With the advent of quantum supremacy, quantum technologies are establishing themselves profitable with near-term commercial products becoming more and more prominent in different parts of the world, with applications that include the military sector. Among such applications, the area of quantum machine learning (QML) is starting to establish itself as a field of its own. Much like classical machine learning, the goal is to be able to generalize a process into multiple applications, but a deeper question remains as to whether there is computational advantage against classical processes via quantum-inspired algorithms; common ways to find such advantages involves proving that there is no best known classical algorithm that performs more efficiently than a quantum algorithm, and processing wise that there exists a run- time exponential advantage over classical computing . While proving quantum advantage and supremacy is controversial and non-trivial, an additional challenge lies in the processing limitation imposed by currently available quantum computers, often referred to as Noisy Intermediate Scale Quantum (NISQ) processors . Such limitations have forced developers to compile algorithms limited both in the num- ber of qubits and number of qubit operations by a hardware-specific metric referred to as Quantum Volume (QV). As a result, it is widely accepted in academia and industry that there is not clear quantum advantage over classical processors given the current technological readiness of available quantum processors due to noise and quantum bits qubit de-coherence limiting the number of qubits and gate operations. However, while acknowledging such limitations, there are some avenues to continue advancing the paradigm of quantum machine learning applications acknowledging hardware limitations: 1) theoretical explorations are done to demonstrate, via com- putational complexity theory, implementations that could get attained in the future once a fault-tolerant quantum computer becomes available, 2) via hybrid instances where some machine learning tasks are managed by a quantum computer while a classical processor takes the rest, such that, quantum machine learning algorithms are used to classify quantum states that are produced by a quantum simulator, and 3) programming a NISQ computer and assessing its quantum algorithm performance against a classical processor counterpart.

Quantum Assisted Machine Learning (QAML) - Promises and Challenges


**Title:** Algorithms

**Moderator:**  Mr. Keith Sullivan, Naval Research Laboratory



        
        

  Algorithms -  Q&A

technical paper

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Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community


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NAML 2022

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

To spread awareness of current machine learning research relevant to Naval applications

To connect machine learning researchers with experts in military requirements

To build and strengthen collaborations within the defense research community



Ramiro Rodriguez

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Quantum Assisted Machine Learning (QAML) - Promises and Challenges

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