The Quantum Challenge: Manufacturing the Synthetic Brain

 The Quantum Challenge: Manufacturing the Synthetic Brain

For decades, the goal of creating a "synthetic brain" relied on traditional silicon transistors to mimic neurons and synapses. However, the sheer scale of the human brain—100 billion neurons and nearly a quadrillion synapses—would require massive server rooms and immense power if built with current technology.

A new frontier, Quantum Materials, promises to compress this architecture into a device the size of a human brain using only 20 Watts of power. Yet, moving from laboratory breakthroughs to mass manufacturing presents fundamental challenges that could redefine the future of industry.

1. Replicating Biological "Non-Locality"

One of the most difficult brain functions to manufacture is non-locality—the brain's ability for a stimulus in one area to affect non-neighboring neurons.

• The Challenge: In traditional electronics, signals follow rigid, local paths through physical wires.
• The Quantum Solution: Researchers have discovered that quantum materials like transition metal oxides exhibit "correlated behavior," where electrons interact collectively across distances.
• Manufacturing Hurdle: Fabricating these materials so they maintain this delicate collective state consistently across billions of artificial synapses is exponentially more complex than printing standard silicon circuits.

2. Manufacturing "Frustrated" Lattices

To mimic the brain's ability to learn and forget, engineers are turning to "frustrated" magnetic states in quantum materials.

• The Goal: Use triangular arrangements of lanthanide elements to create a state of "intrinsic quantum disorder" that can store memory like a synapse.
• The Hurdle: This requires ultra-high crystal purity and atomic-scale precision during fabrication. Even a single misplaced atom can disrupt the quantum state, making large-scale manufacturing highly prone to defects.

3. The Temperature and Coherence Paradox

While some neuromorphic (brain-like) systems can work at room temperature, many high-performance quantum components require extreme conditions.

• The Issue: Superconducting materials often need temperatures near absolute zero to function.
• The Manufacturing Conflict: Building a portable "synthetic brain" that requires a massive cooling infrastructure is a logistical nightmare. Manufacturers are currently racing to develop materials like germanium-gallium alloys that can bridge the gap between quantum superconducting phases and standard room-temperature semiconductors.

4. Moving Beyond the "Edisonian" Approach

The traditional trial-and-error method of manufacturing (the Edisonian approach) is no longer feasible due to the complexity of quantum interactions.

• The Shift: Future factories will likely need to integrate AI-driven materials design and first-principles calculations to predict how these materials will behave before a single chip is produced.
• Integration: Companies like Samsung are already exploring how to integrate these quantum materials with existing CMOS manufacturing processes to create hybrid systems.

The Verdict

Quantum materials offer the only viable path to a true "brain-on-a-chip," but they force us to abandon 50 years of traditional semiconductor logic. The challenge is no longer just about making transistors smaller; it is about manufacturing emergence—the ability for a material to think, learn, and adapt by changing its own physical state.

Bibliography

• Alexeev, Y., et al. (2021). "Quantum Computer Systems for Scientific Discovery." PRX Quantum. This source discusses the co-design of quantum systems and their applications, including the need for interdisciplinary approaches to overcome fabrication hurdles.
• Basov, D. N., et al. (2017). "Towards properties and applications of quantum materials." Nature Materials. Provides the foundation for understanding how lattice, charge, and orbital degrees of freedom create the electronic states used in artificial synapses.
• de Leon, N. P., et al. (2021). "Materials challenges and opportunities for quantum computing hardware." Science. A key reference for the manufacturing difficulties regarding material heterogeneity and the impact of impurities on quantum coherence.
• Dai, S., et al. (2024). "Exploring quantum materials and applications: a review." Journal of Advanced Ceramics. Highlights the role of quantum materials (QMs) in artificial intelligence and the specific need for artificial synapses to match biological signal response.
• Frañó, A., et al. (2023). "Non-locality in Quantum Materials." Nano Letters (referenced via UC San Diego News). This primary research breakthrough explains how electrical stimuli can affect non-neighboring electrodes, mimicking biological brain function.
• Goh, K. E. J., et al. (2022). "Quantum Technologies for Engineering: the materials challenge." ResearchGate. Outlines the engineering-specific hurdles in scaling quantum hardware for practical use.
• Marr, B. (2025). "7 Quantum Computing Trends That Will Shape Every Industry In 2026." Forbes. Provides context on the race for room-temperature quantum operations and the infrastructure challenges involved.
• Schuller, I. K., et al. (2022). "Neuromorphic computing: Challenges from quantum materials to systems." Applied Physics Letters. Discusses the "holistic rethinking" required for computation and the energy-efficiency limitations of conventional semiconductors.
• Zhang, H., et al. (2018). "Quantum materials pave the path for synthetic neuroscience." MRS Bulletin. Details how vanadium oxide and samarium nickelate act as tunable materials to replicate neuronal firing and synaptic gatekeeping. 

Industry derived holistic career development process for academic institutions

This article deals with ideas on converting the momentum required for industry lead training initiative in academia. The technique involves a  structured approach and is performed in different, distinct steps. Here is an explanation of a two-step (training & placement) view. This is toward skilling needs with the potential of students’ career development from a fresher to an early career professional.
 

• LEVERAGING  MOMENTUM, NOT ADDING WEIGHT - An Industry perspective

 A worker needs to load a heavy tire onto a truck. Instead of relying on sheer strength, he takes a smarter route—rhythmically bouncing the tire, building energy and momentum until it rises to the right height. It’s not just about force—it’s about finesse.

 
Learning works the same way. Instructional Systems Design isn’t about flooding learners with content. It’s about pacing, feedback, and interaction—each element intentionally sequenced to build momentum. Like the tire, each step helps lift the next. In learning and development, real impact comes from structure and flow, not from more content or greater pressure. Thoughtful design delivers knowledge with purpose, encourages engagement, and keeps learners moving forward.  The best learning experiences respect cognitive limits. They don’t create friction—they create lift.
 
The industry's internal development programs are structured according to the aforementioned logic of distributing responsibilities across roles. This, in turn, is customized business-wise to yield maximum benefit for scaling training and development, rather than burning out employees. A similar analogy to be applied in the academic setting is what is derived in the next section of this work.
 

• LEVERAGING  POTENTIAL, NOT ACCESSING PERFORMANCE - An Academic Perspective

Training as a system thinking activity occurs daily. And specifically, if it happens during the adult learning stages, with a set framework, the outcomes are just the evolution itself. Thus, within the education institutions, the training program is designed according to the industry requirements with great collaboration with experts as part of the board of studies panel meeting twice a year.  An effort is also made to develop communication skills and group discussion skills so that learners can perform better in a team. In this regard, 60 hours of intensive in-house training for a year is implemented along with the academic training (AT)
 
Job readiness year on year shall be achieved with a well-rounded placement process. A pre-placement training (PT) shall be organized in the summer break for n-1 years of students' regular degree programs, just before the final nth year 1st semester. A highly structured, customized 30-hour training shall be imparted every year. Topics such as Quantitative Ability, Verbal Ability, Logical Reasoning, resume preparation mock interviews shall be covered as well.
 
Quality domain-specific interviewing techniques training sessions are to be conducted to gear up the students for group discussions on various specializations. This program enables the students to acquire sufficient knowledge to qualify in their own domains, if any, written tests of various industry requirements, and every sort of company that shall be visiting the campus during the final/ nth year’s semester onwards.
 
Guest lectures, industry visits, and special invited lecture series, workshops are organized for all the branches of students from industry experts in collaboration with respective schools through regular graduate program syllabus implementation. These trainings are aimed at improving the placement percentage year on year.
 The Training Module shall consist of the following

●            Domain-specific practice and tests
●            Communication Skills
●            General Aptitude practice and tests
●            Quantitative Reasoning
●            Logical Reasoning
●            Verbal Reasoning
●            Group Discussion
●            Interview Skills

 Here is the high-level process of the training and placement process.

 
LEVERAGING  AVENUES, NOT ONE SIZE FITS ALL
 
During the lifecycle of a student in academics, it is bound responsibility of the institutions to be a partner for every student to set the course direction of a specialized career track for each and every individual. The opportunities are looked at from graduates pursuing post-graduation, or graduates seeking employment skills for jobs. Then there is also another category of graduates who have ideas in the vein of creating something for scale to get jobs out of their entrepreneurial spirit.

 The fundamental trait for the avenues starts with communication through languages. In the context of English reading, writing, and practice is in the main flow. As the medium has are varied nature, a common language eases interpersonal communication.

 The matching of patterns in day-to-day design and such logical thinking goes under aptitude thinking. This career development exercise is to train the brain constantly to practice the logic between nature and presence. Context-based domains dominate the industry, from finance, marketing, commerce, Information technology, automotives, and supply chains, to name a few. The skills related to each of these is the need for enterprise knowledge adoption in the industry.

 CONCLUSION

The integration of an industry-derived framework into academic career development is not merely an enhancement of existing programs; it represents a paradigm shift in preparing students for the modern workforce. By moving beyond traditional academic silos to incorporate practical skills, real-world problem-solving, and critical soft skills such as communication, teamwork, and adaptability, academic institutions can ensure their graduates are not just academically proficient but also immediately employable and career-ready. The research presented here establishes a strong correlation between holistic, industry-aligned development processes and enhanced student awareness of personalized career pathways and professional identity.

The key findings underscore the significance of training and development, such as soft skills and domain-based learning, in bridging the gap between academia and industry expectations. This approach fosters a symbiotic relationship, where institutions gain valuable market insights to inform curriculum development and employers benefit from a pipeline of talent equipped with demand-driven skills. Acknowledging the cultural and structural differences between academic and job environments is crucial, and successful implementation requires strong commitment from both educational leadership and industry partners.

While this study focused on the development and initial implementation of such a model, its broader implications suggest a future where the current model serves as a foundation for sustainable, lifelong learning. Future research should explore the long-term career trajectories of students who participated in these holistic programs, using multi-institutional cohorts to improve generalizability and refine the framework. Ultimately, by embracing this integrated approach, academic institutions can empower students to navigate their professional lives effectively, equipping them with the versatility and resilience needed for sustained success in an ever-evolving global market. The time to transition from an education-centric to a career-centric model, guided by industry needs, is now, ensuring that higher education remains a vital engine for both personal fulfilment and professional growth.
 

Linux of the cloud is OpenStack!

Recently, I have turned back to Linux [1] as I find it overwhelming to see the potential for what OpenStack [2]  has to offer to the community. Yes, in the world of the internet, Linux made a revolution by booting any OS on any Hardware. Similarly, I see only parallels with OpenStack and such open source tools, with respect to how these can scale the computing power in the cloud and beyond. This analogy describes far more reaching of any type of computing power, even needed with mechanics to thrive in the Quantum spaces. Hence, I am writing down here are few similarities I see that help support the open source nature of both technologies. The comparison highlights several key characteristics.

• Open Source: Just as Linux is a free and open-source operating system, OpenStack is an open-source cloud platform. This means its source code is publicly available, allowing for community collaboration, customization, and innovation.
• Foundation for Infrastructure: Linux serves as the foundation for many operating systems and applications. Similarly, OpenStack provides the foundational Infrastructure-as-a-Service (IaaS) layer for building and managing private and public clouds. It controls and orchestrates compute, storage, and networking resources.
• Flexibility and Customization: Linux offers immense flexibility and can be tailored to specific needs. OpenStack, with its modular architecture and numerous projects (components), also provides a high degree of flexibility, allowing organizations to build and customize their cloud environments to meet unique requirements.
• Vendor Neutrality: Linux is not tied to a single hardware vendor, promoting choice and avoiding vendor lock-in. OpenStack similarly aims for vendor neutrality, supporting multi-vendor hardware pools and allowing organizations to choose their preferred hardware and software components.
• Community-Driven Development: Both Linux and OpenStack benefit from large and active open-source communities that contribute to their development, maintenance, and support.

In essence, the analogy suggests that OpenStack provides an open, flexible, and community-driven platform for cloud infrastructure, much like Linux does for traditional operating systems. There will be a time when dominant cloud vendors like AWS, Google, and Microsoft, among others, assume a gatekeeper role for computing and dissolve their proprietary systems to OpenStack, as AT&T did with Unix, allowing Linux to become a powerful OS for over three decades since its invention. Red Hat [3] and Oracle [4], while settling scores themselves, the similarities will emerge for the benefit of end-users only. Ultimately, if the scientific community adopts OpenStack, industry players will follow suit. Hence, my Thesis is titled Elastic Intelligent Cloud Architecture for the academic community,[5] elucidating the frameworks that need strengthening. 

In the future, if AI holds features alongside OpenSource like OpenStack, why not Quantum Mechanics follow the same path? There is a lot of future work required to enable Quantum [6] as an open source to match and then to surpass its predecessors, like Linux, OpenStack. I welcome your comments and suggestions.

References

[1] Linux Foundation - Decentralized innovation, built with trust

[2] OpenStack: Open Source Cloud Computing Infrastructure

[3] Red Hat Announces Preview Version of Enterprise-Ready OpenStack Distribution

[4] Oracle announces Oracle OpenStack for Oracle Linux — and cooperation deal with Canonical seen as poking Red Hat

[5] Elastic Intelligent Cloud Architecture for the academic community,

[6] The Quantum Open Source Foundation

OpenStack could for quantum computing

 

Even though OpenStack does not directly manage quantum hardware, it can serve as the foundational classical infrastructure for a quantum cloud computing platform. In this article the several parameters that make the OpenStack cloud as fundamental model to extend techniques like quantamizing.

For example: 

Orchestration: An OpenStack-managed classical cloud can host the middleware and APIs that send quantum circuits to remote quantum processing units (QPUs).

Hybrid workloads: Many quantum problems are hybrid, meaning they require both classical and quantum processing. OpenStack could manage the classical portion of the workload, such as data preparation and post-processing, that runs alongside a quantum job.

Quantum simulators: Quantum simulators, which run on classical supercomputers, could be managed and made available to users through an OpenStack-based infrastructure. 

Distributed computing: In all computing requirements there is never a time one size fits for all. Coming to the way the resources discovered and distributed to only merge as qbits OpenStack cloud shall support a hybrd model.

OpenStack vs. Quantum Computing features  

Technology

An open-source suite of tools for building and managing classic cloud computing infrastructure.

An emerging technology that uses the principles of quantum mechanics (qubits, superposition, entanglement) to solve highly complex problems.

Primary Resources

Manages large pools of conventional compute (CPU/GPU), storage, and networking hardware.

Utilizes qubits, which can exist in multiple states simultaneously, to perform certain calculations exponentially faster than classical computers.

Use Cases

Deploying virtual machines and containers, managing storage, and running traditional enterprise and scientific applications.

Solving specialized problems in cryptography, drug discovery, financial modeling, and materials science.

Hardware

Runs on conventional server hardware, which can be virtualized.

Requires specialized hardware that often needs to operate in extreme environmental conditions, such as cryogenic temperatures.

Purpose of Information sharing for scaling organisations

 Information sharing is crucial for scaling an organization because it fosters collaboration, accelerates decision-making, and reduces inefficiencies. By breaking down silos and promoting a culture of transparency, organizations can leverage collective knowledge, improve performance, and achieve sustainable growth. [1, 2, 3, 4, 5, 6, 7]  

Here's a more detailed breakdown: 

1. Fosters Collaboration and Teamwork: 

• Breaking down silos: Information sharing ensures that teams and departments have access to the same information, preventing duplication of efforts and promoting a more unified approach. [3, 6]  

• Cross-functional collaboration: Open information exchange enables teams to collaborate effectively on projects, share expertise, and leverage diverse perspectives. [1, 8]  

• Building trust: Transparency in information sharing builds trust and strengthens relationships within the organization. [3]  

2. Accelerates Decision-Making: 

• Informed decisions: Access to relevant information allows leaders to make quicker and more informed decisions, leading to faster execution and better outcomes. [3, 9]  

• Reduced bottlenecks: Timely access to information streamlines processes and reduces bottlenecks, particularly in large organizations where information can be difficult to locate. [3, 6]  

3. Improves Efficiency and Productivity: 

• Reduced redundancy: When information is readily available, employees avoid spending time searching for it, leading to increased productivity. [3, 3, 6, 6]  

• Streamlined processes: Shared information can optimize workflows, identify areas for improvement, and enhance overall operational efficiency. [5, 5, 6, 6, 10, 11, 12]  

• Faster time to insights: Data sharing can accelerate the process of gaining insights from data, allowing organizations to identify trends, opportunities, and potential problems more quickly. [6, 6]  

4. Drives Innovation and Growth: 

• New ideas and perspectives: Open information sharing encourages diverse perspectives and fosters a culture of innovation. [1, 1, 3, 3]  

• Adaptability to change: With access to a wider range of information, organizations can adapt more quickly to market changes and emerging trends. [1, 1, 11, 11, 13, 14]  

• Improved performance: By leveraging collective knowledge and insights, organizations can improve overall performance and achieve sustainable growth. [6, 6, 11, 11]  

5. Enhances External Relationships: [6, 10]  

• Collaboration with partners: Sharing data with partners can lead to increased reach, improved customer service, and greater operational efficiency. 

• New revenue streams: Data sharing can also open up opportunities for new data products and services, creating new revenue streams. 

By prioritizing information sharing, organizations can create a more collaborative, efficient, and innovative environment that supports sustainable growth and success. [1, 6, 10, 11, 15]  

Reference s

[1] https://zeenea.com/what-is-data-sharing-benefits-challenges-and-best-practices/

[2] https://www.sciencedirect.com/science/article/pii/S0740624X10001322

[3] https://blogs.joinassembly.com/blog/how-to-share-information-with-team-members-effectively

[4] https://disprz.ai/blog/workplace-knowledge-sharing-barriers-best-practices

[5] https://www.joinassembly.com/blog/how-to-share-information-with-team-members-effectively

[6] https://www.databricks.com/glossary/data-sharing

[7] https://redlearning.org/breaking-down-silos-effective-communication-and-collaboration-in-lean-six-sigma/

[8] https://www.starmind.ai/blog/sharing-knowledge-with-others

[9] https://www.tandfonline.com/doi/full/10.1080/23311975.2023.2195028

[10] https://www.atscale.com/blog/data-sharing-make-smarter-decisions-at-scale/

[11] https://www.freedomgpt.com/wiki/information-sharing

[12] https://www.easygenerator.com/en/blog/knowledge-sharing/benefits-knowledge-sharing/

[13] https://www.crownrms.com/insights/information-management-scalability-flexibility/

[14] https://affine.pro/blog/shared-knowledge-app

[15] https://www.linkedin.com/advice/1/what-main-goals-benefits-developing-knowledge

Generative Pre-trained Transformer's words on the Top 10 roles from my current curricula vitae

Potential roles and corresponding descriptions that align with skills, experience, and aspirations:

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1. Program Director – Digital Transformation / Enterprise Solutions

Description:
Lead cross-functional enterprise programs leveraging SAP, AI, cloud computing, and digital technologies. Collaborate with business leaders to drive innovation, streamline operations, and scale delivery excellence across global teams.

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2. Head – Technology Consulting / Practice Lead (SAP & Cloud)

Description:
Drive SAP practice strategy and growth, overseeing delivery excellence, solutioning, and capability building. Manage pre-sales, delivery, and talent development for large SAP landscapes and BTP migrations.

---

3. Director – Learning & Development / Corporate Training

Description:
Design and lead organization-wide learning initiatives, especially in emerging technologies such as AI, Cloud, and Agile. Combine industry and academic insights to develop future-ready workforce programs.

---

4. Professor of Practice / Academic-Industry Liaison

Description:
Bridge academia and industry through curriculum design, skill-building initiatives, and faculty-industry collaboration. Mentor students on cloud, blockchain, AI, and lead university-industry partnerships.

---

5. Agile Coach / Enterprise Scrum Master

Description:
Guide agile transformations at scale across departments. Coach teams on Lean, Kanban, and Agile methodologies, improving delivery cycles, stakeholder collaboration, and product evolution.

---

6. Product Manager / Solution Architect (SAP & Cloud Platforms)

Description:
Lead end-to-end lifecycle of SAP and cloud-based products—from ideation to sunset. Manage solution architecture and integration, especially with BTP, SAP HANA, CRM, and analytics platforms.

---

7. Innovation & Research Leader (AI / Cloud / EdTech)

Description:
Drive applied research in AI, cloud, and educational technologies. Innovate scalable solutions using generative AI, intelligent architectures, and cloud platforms aligned with academic and business needs.

---

8. Digital Skills Evangelist / Technology Coach

Description:
Promote digital fluency through workshops, certifications, and thought leadership. Coach professionals and students in technologies such as AWS, SAP, generative AI, and Python.

---

9. Pre-Sales / Bid Manager – Enterprise Solutions

Description:
Lead pre-sales solutioning, proposal design, and client engagement for large-scale SAP and cloud transformation programs. Collaborate with stakeholders to deliver winning RFP responses.

---

10. Chief Learning Officer (CLO) / Strategic Education Partner

Description:
Shape the future of corporate learning through curriculum innovation, strategic partnerships, and institutional capability building. Align L&D with organizational goals and technology advancements.

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A Journey Rekindled After Two Decades

 

A Presentation with Purpose

What began as a long and steady professional journey eventually started to feel less meaningful. It was then that pursuing a PhD brought a renewed sense of curiosity, purpose, and intellectual challenge into my life.

On a quiet morning at CHRIST University, I found myself once again behind a podium—not only as a student this time, but as a speaker, mentor, and researcher. Presenting on VM scheduling policies in a research forum, I stood before a group of curious minds and experienced faculty, sharing insights shaped over years of exploration and persistence.

An Unexpected Reunion
But the day held more than just academic exchanges. Amid the bustle of post-session chats and handshakes, a familiar face emerged—my MCA classmate from two decades ago. We greeted each other with the warmth only time can deepen. What began as a catch-up soon became a powerful moment of realization: she was not only a friend from the past but my PhD guide for the past seven years.

A Community of Scholars
We posed for group photos with our peers, fellow presenters, and organizers—some in person, while others joined virtually. Each face in the frame told a story of dedication and academic camaraderie. These were not just colleagues but co-travelers on the same challenging yet rewarding path of knowledge creation.

Capturing Bonds

As we stood together once more, smiling under the soft lights of the seminar hall, I couldn’t help but feel grateful for the chance to collaborate, for the encouragement shared through every milestone, and for the constant reminder that the best journeys are walked together.

A Milestone Preserved
Later, in a quieter corner of campus, we held the symbol of years of research—a bound PhD thesis. Surrounded by mentors and teammates, this moment celebrated more than just an academic milestone; it acknowledged resilience, teamwork, and the unbreakable bond between a student and her guide.

---

Gratitude, Guidance, and Growth
What began as a university friendship has grown into a mentoring relationship that continues to inspire me every day. Seven years into this PhD journey, with countless drafts, presentations, and reflections behind us, I remain deeply thankful for a friend who became my guide—and for a guide who continues to believe in me. The road ahead looks bright, especially when walked with such strong support and shared purpose.