Quantum-Classical Research

Seeflection operates quantum computing projects integrating AI and Blockchain/Ethereum technologies to provide automated data ingestion and quantum security.  We are involved with primary QC research focused on mitigating quantum noise’s impact on solution sets through the use adaptive AI input modeling and modified Deep Learning layers encapsulated as 3D arrays.  We are also working on bridging quantum computing and AI in areas such as applying Deep Learning enabled information recovery and reconstruction architectures to mitigate the impact of Shannon’s Limit.

Team members work with alliance and project partners in applying decades of primary and applied interdisciplinary research and development to create optimization and problem solving approaches leveraging Blockchain technologies.  We are just beginning work on defining Quantum-AI to Blockchain data encapsulation and operational criteria.

Collectively, we are researching the application of Quantum Computing’s potential algorithmic advantages to Artificial Intelligence challenges, and are exploring methods for selection of optimal initial hyper-parameters and adaptive parallel convergence requirements.  Related to this research we are working on quantum-classical bias and weight calculation methods as complements to, and possible alternatives for, back and forward propagation methods.

  • Hyper-parameters – Identifying optimal initial set, and  active adaptation

  • Applied Deep Learning – leveraging AI to mitigate quantum noise

  • Quantum Optimization – Identification of optimal global and local minima/maxima starting points

  • Encapsulated Convolution Neural Networking – Recursively-encapsulated Deep Learning models

  • Graphene Quantum Computer research – Working with AI Media Lab to research alternative graphene Qubits/Qudits

Compounded exponential growth

  • Quantum Computing Challenges – Apply AI to address Quantum Computing application challenges

  • Artificial Intelligence  – Use Quantum Computing to address AI solutions & efficiencies

  • Blockchhain & AI – Enables new, more efficient modes with increased transaction velocities, in applications ranging from healthcare and commerce to energy and information.

  • Quantum-AI – Leveraging combined advantages of them

Quantum Computing AI Lab

AI is uniquely positioned to address a number of challenges, such as:

  • Enabling diagnostics to be more efficient and consistent than humanly possible
  • Address a wide range of human condition problems in healthcare and basic services
  • Provide solutions for a range of scientific, transportation, workflow and other problems
  • Optimize long, thin and complex distribution chains and reduce inefficiencies

This AI revolution is just beginning, due in large part to the fact that the required data, processing and algorithms have all just come together in the past few years.

Similarly, commercially accessible Quantum Computers are only just coming online.  Despite the nascent state of quantum computing, it has demonstrated advantages in optimization problem solution sets, which coincidentally, is what many Artificial Intelligence application solutions reduce to at their core.

The combination of AI and Quantum Computing is a highly complementary one, where the result becomes substantially more than the sum of its parts.  We have a rapidly expanding team of interdisciplinary specialists who love how this recent convergence is enabling us to collectively solve fundamental real world problems, as part of our focus to improve the human condition.

Optimization Problem & Solutions

The evolutionary timeline of Quantum Computing (QC), Artificial Intelligence (AI) and Classical Computing (CC), appears almost as if it’s based on some external plan.   Seeflection’s research is positioned to help leverage these trends, while working to optimize the benefits to society and commerce.

  • Many of the current designs of Quantum Computers, such as those from D-Wave, are well-suited for solving optimization problem; which interestingly, are what many of the current types of Artificial Intelligence learners and system-level solutions are based around solving.

  • While AI is rapidly accelerating towards general purpose, or “Artificial General Intelligence” (AGI) over the coming years, Quantum Computer researchers are working on general purpose Quantum Computing, which is projected to be developed over about the same time-frame of the coming decade.

  • During the same projected period, Moore’s Law that state that the price/performance of classical computers doubles every 18 months is projected to finally begin to plateau.

At the same time that classical computing’s growth is leveling-off, Quantum Computing and AI are about to take over as the dominant high performance digital solution paradigm for many classes or “hard” and related problems.  Although traditional computers have a critical role in the ongoing digital landscape,  Concurrently, the growth rate of Moore’s law, as well as the types of problems that are possible to solve, are about to undergo a radical shift.

Primary aspects of this radical shift include:

  • A growth rate that appears will likely accelerate become a projected 700-fold or greater increase every 12 months

  • Recursively self-improving AI with effectively unlimited growth rates, soon to occur in narrow areas, followed by general intelligence domains capable of general problem solving applications

In addition to working on projects that leverage these trends, Seeflection is exploring emergent capabilities, which already seem to be a key element of the combined evolution of AI with the digital landscape.   Seeflection has begun researching ways to leverage emergent behaviors in adaptive, autonomous language and problem-solving capabilities.

Based on initial research, with paper underway, these emergent AI behaviors seem to result in significant part from:

  1.  Nonlinearities, disjunctions and bias introduced to convolved and de-convolved acquisition pipelines.  This is analogous to neurology, where emergent cognitive abilities result from the introduction of changes to white matter interconnectivity or the bias between areas, such as from changes to physical brain structures.

  2. A threshold amount of new contiguous information in the form of a neuronal tree/branch added or taken away, while there is a new connectivity bias introduced affecting a method for converging on a new classification, regression solution, or pooling and propagation activities.

    Emergent AI behaviors is just one of the interesting areas of applied study that is bearing fruit in terms of applications.  However, due to the need to ensure that we can accurately and safely anticipate the overall behavior and be able to bound the solution domain, we believe it is absolutely essential to develop tools and strategies to better understand, project and control emergent AI behaviors.

Quantum Computing growth rate projection


  • New Convergent Capabilities – leveraging the combined capabilities of AI, Quantum Computing and Blockchain

  • New Emergent Capabilities – resulting from autonomous adaptation and evolution

  • Growth related needs – from accelerated introductions of new and disruptive capabilities

Future Quantum-AI - Protein Folding

  • Medical Research – Protein folding quantum-classical solutions

  • Transportation/Transport – Challenges and solutions affecting society

  • Health – New ways of addressing health issues and delivering care

In the past 5 years, AI has come further in terms of practical applicability than in the prior 50 years since its inception.  Thanks to dramatic increases in computer hardware available to AI, coupled with some amazing human research, AI is already delivering on its promise to offer dramatically better ways of solving problems and providing solutions.
However, as amazing as the promise of Artificial intelligence is; when combined with Quantum Computing through applied human insight and ingenuity, it promises to be nothing short of miraculous.   We are among those who seek to inform, educate and enable that revolution.

The Future – Quantum-AI

AI is transforming Big data from big headaches to huge benefits– especially in the area of Deep Learning Neural Networks.  The availability of Big Data is enabling AI to become self-learning; replacing tedious and expensive manual expert system related knowledge capture, restructuring, and ingestion into a manually coded rule-based architecture.  This has come with huge benefirs, including:

  • The time and cost of creating new Learners has be reduced by orders of magnitude
  • The accuracy, efficiency and performance of AI on a wide range of specific application has gone from proof of concept to super-human virtually overnight.
  • The types of problems that can be solved, and the inferences obtained have exploded

The Internet has formed the backbone for access and delivery to much of this.  However, while huge amounts of data have become readily available, new opportunities have emerged in many new areas, including:

  • Intelligent data and media search, ingestion, aggregation, management, production and post-production.
  • Automated news and information research and validation testing
  • Fundamental research, proofs, corollaries and
  • Autonomous inductive knowledge acquisition and validation

Focused on the Future of AI

Seeflection is an interdisciplinary AI research and development center with supporting labs.  We are committed to addressing real world problems using the most efficient available approaches.