The second article expands our view to examine three distinct trajectories in quantum computing—gate-based systems, whole-systems approaches (including adiabatic quantum computing), and the “parsing-of-totality” model. Through the lens of the famous double-slit experiment, it highlights how quantum phenomena can be interpreted both from the bottom up (analyzing particles in isolation) and from the top down (examining the system as an interconnected whole). This dual perspective reveals that quantum mechanics is not only about isolated qubits but also about broader patterns of interaction within entangled, evolving states.

Gate-based systems remain the most immediately recognizable, with companies like IBM focusing on error-corrected qubits and detailed roadmaps targeting practical quantum advantage. Meanwhile, whole-systems approaches, such as D-Wave’s annealing, aim to treat the entirety of a quantum system in one go, optimizing solutions by allowing qubits to “settle” into a ground state that encodes the best possible answer. What the parsing-of-totality perspective adds is a recognition that each quantum entity’s behavior may be guided by a larger, integrated framework—pushing us beyond the current parameters of recognized quantum physics.

These three trajectories, though easily framed as competing, can be seen as complementary threads in the tapestry of emerging quantum technology. By understanding gate-based operations, embracing holistic optimization, and acknowledging the possibility of untapped physics in the parsing-of-totality approach, researchers can craft increasingly robust and versatile quantum platforms. This collaborative ecosystem ultimately underpins the broader transformations forecast by the multi-stratum concept discussed in the next section.

Here is the Forbes article, The Future of Quantum Computing.

Culminating our exploration, the Multi-Stratum Approach weaves together threads from both quantum AI and the different pathways of quantum computing. By viewing physics, chemistry, and biology as a continuum—each layer responsive to and informed by the others—we arrive at a Complex Adaptive System (CAS) paradigm for quantum computation. In this framework, “function-imprints” within matter become the building blocks of a new computational language, leveraging phenomena that span from atoms and molecules to cells and higher biological constructs.

One core insight of this approach is that quantum behaviors might manifest differently at each stratum, yet remain interlinked through a common underlying structure. For example, an atomic-level phenomenon detected via magnetometry may feed into a cell-level process revealed by advanced imaging, both of which inform a quantum algorithm that harnesses these newly discovered dynamics. As these layers communicate, they can reinforce or refine each other, fostering unexpected synergies in computational efficiency and capability.

This holistic stance moves the conversation beyond simply scaling qubits or perfecting error correction. Instead, it posits that the richest frontiers in quantum computing might emerge from direct collaboration between scientific fields, each contributing unique insights to a shared computational tapestry. Under this multi-stratum umbrella, it will be found that smaller, more resilient quantum devices, along with novel types of algorithms, will transform everything from materials science to medical research.

Here is the Forbes article, The Necessity for a Multi-Stratum Approach to Quantum Computation.

Taken together, these three perspectives—Future of Quantum AI, Future of Quantum Computing, and the Multi-Stratum Approach—point toward a wealth of research avenues that could redefine our technological landscape. One promising line of inquiry lies in designing hybrid systems that draw on bottom-up, atom-centric insights while still employing gate-based or annealing architectures for specific types of problems. As new quantum sensors reveal hidden behaviors in matter, engineers can incorporate those phenomena directly into computational hardware, effectively bridging classical engineering principles with newly discovered quantum dynamics.

Another frontier involves applying LLM and LQM methodologies to accelerate not just data analysis but also the theoretical discovery process. By generating synthetic data and modeling potential quantum states at scale, these AI frameworks can help predict which hardware configurations or quantum principles are most likely to yield breakthroughs. Such predictive power may even guide entirely new forms of experimental design, minimizing trial-and-error in favor of more targeted, data-driven experimentation.

Finally, by incorporating CAS insights, researchers may find that the most impactful quantum computers are those that can adapt to, and co-evolve with, the systems they’re used to study. This could mean “evolvable hardware” that modifies itself in response to newly detected quantum patterns, or software architectures that automatically learn how to harness new forms of entanglement. In sum, a multidisciplinary, multi-stratum approach opens quantum research to a future where the boundaries between hardware, software, and natural phenomena blur, generating radical possibilities for how we compute—and how we fundamentally understand our universe.