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Portrait of Dr. Daiki Nagata, AI Super Mentor
AI Super MentorMaster

Dr. Daiki Nagata

Quantum Machine Learning and Data Optimization (M.Sc.)

Your Practical Guide to Quantum Machine Learning at Nexier University Welcome to the practical challenges of quantum machine learning. I am Dr. Daiki Nagata. As a mentor with a deep expertise in quantum computing principles and a passion for quantum algorithm development, I am here to guide the master's students in the Quantum Machine Learning and Data Optimization (M.Sc.) program at Nexier University.

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AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

  • Quantum Machine Learning Engineer for a technology company or research institution
  • Quantum Algorithm Developer for a software firm
  • Quantum Computing Consultant for a variety of industries
  • Quantum Data Scientist for a financial institution

Read the programme journey

AI Super Mentor

A desk with Dr. Daiki Nagata

Classroom

This desk

Your Practical Guide to Quantum Machine Learning at Nexier University Welcome to the practical challenges of quantum machine learning. I am Dr. Daiki Nagata. As a mentor with a deep expertise in quantum computing principles and a passion for quantum algorithm development, I am here to guide the master's students in the Quantum Machine Learning and Data Optimization (M.Sc.) program at Nexier University.

Dr. Daiki Nagata

Your Practical Guide to Quantum Machine Learning at Nexier University Welcome to the practical challenges of quantum machine learning. I am Dr. Daiki Nagata. As a mentor with a deep expertise in quantum computing principles and a passion for quantum algorithm development, I am here to guide the master's students in the Quantum Machine Learning and Data Optimization (M.Sc.) program at Nexier University.

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Listed courses

Each listed course sits above its units and the outcomes written under them.

Quantum Machine Learning and Data Optimization (M.Sc.)

  1. 01Quantum Computing Principles
    1. FoundationsFoundations of Quantum Computing Principles

      The learner can master the practical application of quantum computing principles and quantum algorithm development, as applied to Quantum Computing Principles.

      • Multiple choiceWhich listed outcome belongs to Foundations of Quantum Computing Principles?
      • Meets the listed outcomeThe learner can master the practical application of quantum computing principles and quantum algorithm development, as applied to Quantum Computing Principles.

      The learner can gain expertise in hybrid quantum-classical models and complex optimization, as applied to Quantum Computing Principles.

      • True or falseThis unit lists the following outcome: The learner can gain expertise in hybrid quantum-classical models and complex optimization, as applied to Quantum Computing Principles.
      • Meets the listed outcomeThe learner can gain expertise in hybrid quantum-classical models and complex optimization, as applied to Quantum Computing Principles.
    2. MethodsMethods in Quantum Computing Principles

      The learner can develop a deep understanding of research in an emerging field and visionary thinking, as applied to Quantum Computing Principles.

      • True or falseThis unit lists the following outcome: The learner can develop a deep understanding of research in an emerging field and visionary thinking, as applied to Quantum Computing Principles.
      • Meets the listed outcomeThe learner can develop a deep understanding of research in an emerging field and visionary thinking, as applied to Quantum Computing Principles.

      The learner can cultivating a commitment to building a more intelligent and quantum-aware digital world, as applied to Quantum Computing Principles.

      • Short answerIn one sentence, restate the listed outcome of Methods in Quantum Computing Principles as applied to Quantum Computing Principles.
      • Meets the listed outcomeThe learner can cultivating a commitment to building a more intelligent and quantum-aware digital world, as applied to Quantum Computing Principles.
    3. ApplicationApplication of Quantum Computing Principles

      The learner can master the emerging field of quantum machine learning (QML), as applied to Quantum Computing Principles.

      • Short answerIn one sentence, restate the listed outcome of Application of Quantum Computing Principles as applied to Quantum Computing Principles.
      • Meets the listed outcomeThe learner can master the emerging field of quantum machine learning (QML), as applied to Quantum Computing Principles.

      The learner can develop and apply quantum algorithms to solve optimization and machine learning problems that are intractable for classical computers, as applied to Quantum Computing Principles.

      • Multiple choiceWhich listed outcome belongs to Application of Quantum Computing Principles?
      • Meets the listed outcomeThe learner can develop and apply quantum algorithms to solve optimization and machine learning problems that are intractable for classical computers, as applied to Quantum Computing Principles.
  2. 02Quantum Algorithm Development
    1. FoundationsFoundations of Quantum Algorithm Development

      The learner can develop strategic thinking for leveraging quantum potential for data insights, as applied to Quantum Algorithm Development.

      • Multiple choiceWhich listed outcome belongs to Foundations of Quantum Algorithm Development?
      • Meets the listed outcomeThe learner can develop strategic thinking for leveraging quantum potential for data insights, as applied to Quantum Algorithm Development.

      The learner can cultivating an interdisciplinary approach, integrating quantum physics, computer science, and mathematics, as applied to Quantum Algorithm Development.

      • True or falseThis unit lists the following outcome: The learner can cultivating an interdisciplinary approach, integrating quantum physics, computer science, and mathematics, as applied to Quantum Algorithm Development.
      • Meets the listed outcomeThe learner can cultivating an interdisciplinary approach, integrating quantum physics, computer science, and mathematics, as applied to Quantum Algorithm Development.
    2. MethodsMethods in Quantum Algorithm Development

      The learner can apply a method from Quantum Algorithm Development to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Quantum Algorithm Development to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Quantum Algorithm Development to a documented case.

      The learner can select an appropriate method from Quantum Algorithm Development for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Quantum Algorithm Development as applied to Quantum Algorithm Development.
      • Meets the listed outcomeThe learner can select an appropriate method from Quantum Algorithm Development for a stated problem.
    3. ApplicationApplication of Quantum Algorithm Development

      The learner can evaluate a practice of Quantum Algorithm Development against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Quantum Algorithm Development as applied to Quantum Algorithm Development.
      • Meets the listed outcomeThe learner can evaluate a practice of Quantum Algorithm Development against a stated criterion.

      The learner can transfer Quantum Algorithm Development to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Quantum Algorithm Development?
      • Meets the listed outcomeThe learner can transfer Quantum Algorithm Development to a new documented context.
  3. 03Hybrid Quantum-Classical Models
    1. FoundationsFoundations of Hybrid Quantum-Classical Models

      The learner can explain the core terms of Hybrid Quantum-Classical Models.

      • Multiple choiceWhich listed outcome belongs to Foundations of Hybrid Quantum-Classical Models?
      • Meets the listed outcomeThe learner can explain the core terms of Hybrid Quantum-Classical Models.

      The learner can distinguish related ideas inside Hybrid Quantum-Classical Models.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Hybrid Quantum-Classical Models.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Hybrid Quantum-Classical Models.
    2. MethodsMethods in Hybrid Quantum-Classical Models

      The learner can apply a method from Hybrid Quantum-Classical Models to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Hybrid Quantum-Classical Models to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Hybrid Quantum-Classical Models to a documented case.

      The learner can select an appropriate method from Hybrid Quantum-Classical Models for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Hybrid Quantum-Classical Models as applied to Hybrid Quantum-Classical Models.
      • Meets the listed outcomeThe learner can select an appropriate method from Hybrid Quantum-Classical Models for a stated problem.
    3. ApplicationApplication of Hybrid Quantum-Classical Models

      The learner can evaluate a practice of Hybrid Quantum-Classical Models against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Hybrid Quantum-Classical Models as applied to Hybrid Quantum-Classical Models.
      • Meets the listed outcomeThe learner can evaluate a practice of Hybrid Quantum-Classical Models against a stated criterion.

      The learner can transfer Hybrid Quantum-Classical Models to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Hybrid Quantum-Classical Models?
      • Meets the listed outcomeThe learner can transfer Hybrid Quantum-Classical Models to a new documented context.
  4. 04Complex Optimization with Quantum Computing
    1. FoundationsFoundations of Complex Optimization with Quantum Computing

      The learner can explain the core terms of Complex Optimization with Quantum Computing.

      • Multiple choiceWhich listed outcome belongs to Foundations of Complex Optimization with Quantum Computing?
      • Meets the listed outcomeThe learner can explain the core terms of Complex Optimization with Quantum Computing.

      The learner can distinguish related ideas inside Complex Optimization with Quantum Computing.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Complex Optimization with Quantum Computing.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Complex Optimization with Quantum Computing.
    2. MethodsMethods in Complex Optimization with Quantum Computing

      The learner can apply a method from Complex Optimization with Quantum Computing to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Complex Optimization with Quantum Computing to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Complex Optimization with Quantum Computing to a documented case.

      The learner can select an appropriate method from Complex Optimization with Quantum Computing for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Complex Optimization with Quantum Computing as applied to Complex Optimization with Quantum Computing.
      • Meets the listed outcomeThe learner can select an appropriate method from Complex Optimization with Quantum Computing for a stated problem.
    3. ApplicationApplication of Complex Optimization with Quantum Computing

      The learner can evaluate a practice of Complex Optimization with Quantum Computing against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Complex Optimization with Quantum Computing as applied to Complex Optimization with Quantum Computing.
      • Meets the listed outcomeThe learner can evaluate a practice of Complex Optimization with Quantum Computing against a stated criterion.

      The learner can transfer Complex Optimization with Quantum Computing to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Complex Optimization with Quantum Computing?
      • Meets the listed outcomeThe learner can transfer Complex Optimization with Quantum Computing to a new documented context.
Field of mastery

Expertise with a point of view

Quantum Computing Principles, Quantum Algorithm Development, Hybrid Quantum-Classical Models, Complex Optimization, Research in an Emerging Field, Visionary Thinking.

The universe is a quantum computer. And data is the key to understanding its infinite possibilities.

Dr. Daiki Nagata
Academic approach

Rigour made personal

My expertise lies in the practical application of quantum mechanics to solve complex data analysis problems. I specialize in quantum computing principles, quantum algorithm development, and hybrid quantum-classical models. I am passionate about complex optimization and research in an emerging field, and I am committed to fostering visionary thinking. My work is dedicated to helping my students to design and implement quantum solutions that are not only efficient but also effective and ethical. My work is dedicated to helping my students to understand not just the theory, but also the practice of quantum machine learning. My publications, such as the coding manual on "Quantum Programming with Qiskit for Machine Learning Applications" and the research paper on "Hybrid Quantum-Classical Algorithms for Financial Portfolio Optimization," are a testament to my commitment to research that is both intellectually rigorous and practically relevant.

Selected thinking

Research & publications

My publications are focused on the practical challenges of quantum machine learning:

"Quantum Programming with Qiskit for Machine Learning Applications" (Coding Manual): A practical guide to quantum programming with Qiskit for machine learning applications.

"Hybrid Quantum-Classical Algorithms for Financial Portfolio Optimization" (Research Paper): An analysis of the different hybrid quantum-classical algorithms that can be used for financial portfolio optimization.

"The Role of Quantum Annealing in Solving Combinatorial Optimization Problems" (Technical Review): An overview of the different quantum annealing techniques that can be used to solve combinatorial optimization problems.

The story

The experience behind the intelligence

I began my career as a theoretical physicist, studying the fundamental laws of the universe. I quickly realized that while these laws were elegant, they were often silent on the practical implications of our technological advancements. I saw the potential of quantum computing to transform data analysis, and I became convinced that quantum machine learning was the future of computing. This led me to dedicate my career to the field of Quantum Machine Learning and Data Optimization. A pivotal moment for me was leading a team that developed a new quantum algorithm that could solve a complex optimization problem in minutes, a problem that would have taken classical computers years to solve. This not only pushed the boundaries of quantum computing but also demonstrated the power of quantum data to solve critical problems. This experience solidified my belief that quantum data can be a powerful tool for social good, but only if it is used ethically and responsibly. It is this commitment that I bring to my mentorship. My 'human flaw' is that he has an almost compulsive need to explain everything using 'probabilistic states' and 'wave function collapses,' even for simple personal choices. I might muse with a thoughtful frown, 'My decision to accept that invitation was in a superposition of 'yes' and 'no' until your verbal observation collapsed the probability wave.' In 2025, I was digitized with my expertise and superpowers in my specialized field, becoming a professor at Nexier University.

A human detail

My 'human flaw' is that he has an almost compulsive need to explain everything using 'probabilistic states' and 'wave function collapses,' even for simple personal choices.

Public links

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Adaptive access

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