Quantum computing is often associated with future breakthroughs and next-generation hardware. But for many engineering organizations, the value is already emerging today through quantum-inspired software running on existing infrastructure.

In this episode of Tech Talks Daily, Nathan Mason, VP of Strategic Growth at BQP, discusses how quantum-inspired computing is helping aerospace and defense organizations solve complex simulation, optimization, and decision-making challenges without waiting for large-scale quantum computers.

→ Listen Now

‍

Key Takeaways

• Quantum-inspired computing delivers practical value on existing CPU and GPU infrastructure

• Aerospace and defense present some of the most complex optimization challenges in engineering

• Simulation accuracy and computational efficiency can improve without increasing hardware resources

• Data-driven decision making is becoming essential across engineering and operational environments

• Quantum readiness begins with workflow transformation, not hardware adoption

‍

From Military Service to Deep Tech Innovation

Tech Talks Daily:

Your career spans military service, intelligence, venture capital, and now quantum-inspired computing. What led you here?

‍

Nathan Mason:

My career has always centered around solving difficult problems through engineering and data. Whether in national security, venture investing, or deep technology, the common thread has been understanding how emerging technologies can create real operational advantages.

What attracted me to quantum-inspired computing was its ability to solve challenges across multiple industries rather than being confined to a single application area.

‍

What Quantum-Inspired Computing Looks Like Today

Tech Talks Daily:

Many people still think quantum is years away. What does practical adoption look like right now?

‍

Nathan Mason:

One of the biggest misconceptions is that organizations need quantum hardware before they can benefit from quantum technologies.

At BQP, we apply quantum-inspired methods on existing CPUs and GPUs to improve optimization, simulation, and modeling workflows. Organizations can achieve meaningful performance gains today while preparing for future quantum integration.

‍

Why Aerospace Is an Early Adoption Sector

Tech Talks Daily:

Why are aerospace and defense such important application areas?

‍

Nathan Mason:

These industries operate some of the most computationally demanding systems in the world. Whether you're designing aircraft, optimizing missions, modeling physical systems, or managing space assets, the complexity grows exponentially.

Traditional approaches often force engineers to simplify problems. Quantum-inspired optimization allows organizations to explore larger solution spaces more efficiently and make better engineering decisions.

‍

Beyond Adding More Hardware

Tech Talks Daily:

How are organizations approaching growing computational demands?

‍

Nathan Mason:

Many organizations respond by adding more compute resources. But increasing hardware alone doesn't always solve the underlying challenge.

The real opportunity is improving how efficiently systems utilize existing infrastructure. By applying more advanced optimization methods, organizations can unlock significantly greater performance without continually expanding hardware investments.

‍

The Role of Data-Driven Decision Making

Tech Talks Daily:

You often talk about decision making. Why is that becoming increasingly important?

‍

Nathan Mason:

The scale and complexity of modern systems make intuition alone insufficient. Whether you're working in aerospace, defense, AI, or operations, decisions increasingly depend on the ability to process large volumes of information quickly and accurately.

Advanced computational methods help organizations make better decisions under uncertainty while reducing operational risk.

‍

Building Careers at the Intersection of AI, Aerospace and Quantum

Tech Talks Daily:

What advice would you give students and professionals entering these fields?

‍

Nathan Mason:

Stay curious and continue learning. Many of the opportunities shaping future industries sit at the intersection of multiple disciplines.

The technologies may evolve, but curiosity, adaptability, and a willingness to engage with new ideas remain constant advantages throughout a career.

‍

Moving Quantum from Theory to Deployment

The conversation highlights an important shift occurring across the technology landscape.

Quantum-inspired computing is no longer purely a research topic. Organizations are already using these approaches to improve simulation accuracy, accelerate optimization workloads, and solve engineering challenges across aerospace and defense environments.

The transition to quantum computing will happen over time, but the journey has already begun.

‍

→ Listen to the Episode

‍

Quantum computing is moving beyond theoretical research into real-world engineering and optimization workflows. In this episode, Nate Mason from BQP discusses how quantum-inspired algorithms are already improving GPU efficiency, accelerating complex simulations, and helping organizations prepare for the transition toward quantum-ready infrastructure.

The conversation explores the convergence of quantum computing, AI, aerospace systems, cybersecurity, and large-scale optimization — and why organizations should begin preparing now rather than waiting for fully commercial quantum hardware.

→ Listen Now

‍

Key Takeaways

• Quantum-inspired algorithms already improve real-world optimization workflows

• Quantum readiness is about infrastructure, workflows, and engineering adaptation

• GPU inefficiencies create major compute and energy bottlenecks

• AI and quantum-inspired systems are beginning to converge in practical applications

• Aerospace, defense, and simulation environments are early adoption sectors

‍

From National Security to Quantum Systems

Chris Gannatti:

You have a very unconventional background for someone working in quantum computing. How did that journey happen?

‍

Nate Mason:

My background started in the Air Force working in electrical engineering and cryptography, much of that alongside the National Security Agency. Over time, I became exposed to emerging technologies through venture capital and started looking at the convergence between AI, quantum computing, and national security applications.

What attracted me to quantum was that it is not isolated to one industry. It applies across aerospace, defense, logistics, finance, AI, and engineering optimization.

‍

Quantum Computing Beyond Hardware

Elvira Shishkina:

Many people still associate quantum computing only with hardware. How do you explain what BQP does?

‍

Nate Mason:

At BQP, we focus on quantum-inspired algorithms that run on existing infrastructure. We apply principles from quantum mechanics mathematically to improve optimization and computational efficiency on today’s CPU and GPU systems.

The key point is that organizations do not need to wait for large-scale quantum hardware to start seeing value.

‍

Solving the GPU Efficiency Problem

Chris Gannatti:

What problem are you trying to solve inside modern compute systems?

‍

Nate Mason:

A major issue today is GPU underutilization. Much of the mathematical framework powering current GPU systems was originally developed decades ago for CPUs.

That creates idle compute capacity and forces organizations to scale infrastructure inefficiently. Our quantum-inspired optimization methods improve throughput and reduce the need for excessive hardware expansion.

‍

AI and Quantum Convergence

Elvira Shishkina:

We hear increasingly about the convergence of AI and quantum systems. What are you seeing today?

‍

Nate Mason:

One major area is quantum transfer learning. We have reduced AI model sizes dramatically while maintaining effectiveness.

In one example, we reduced a model with 14.5 million trainable parameters down to approximately 2,000. That creates major gains in efficiency, deployment flexibility, and edge computing capability.

‍

Aerospace and Space Domain Optimization

Chris Gannatti:

You mentioned space systems and orbital propagation work. Where does quantum-inspired optimization fit there?

‍

Nate Mason:

We developed orbital propagation models for space domain awareness challenges. Using quantum-inspired methods, we achieved approximately 250x faster propagation performance while maintaining highly accurate positioning predictions.

The broader challenge is that space infrastructure is scaling rapidly. Optimization becomes essential for managing satellite constellations, collision prediction, and operational efficiency.

‍

Preparing for the Quantum Transition

Elvira Shishkina:

What should organizations be doing today to prepare for quantum computing?

‍

Nate Mason:

Organizations should start building quantum-ready workflows now.

That means:

• Preparing infrastructure
• Training engineering teams
• Understanding optimization-first approaches
• Integrating quantum-inspired methods into existing environments

The transition will not happen overnight. It will be gradual, and organizations that prepare early will have a major competitive advantage.

‍

Quantum Readiness Starts Before Quantum Hardware

The discussion reinforces an important shift happening across the industry:

Quantum adoption is no longer only about future hardware breakthroughs.

It is increasingly about:

• Optimization efficiency
• Workflow modernization
• GPU utilization
• Hybrid compute systems
• Quantum-inspired algorithmic infrastructure

‍

→ Listen to the Episode

‍

Key Takeaways

• The core bottleneck in simulation is mathematical models—not hardware

• Quantum-inspired algorithms are already delivering ~10× improvements on classical systems

• Future gains (~1000×) depend on both algorithmic evolution and hardware maturity

• India’s challenge is not talent, but risk appetite, execution, and ecosystem alignment

• Adoption requires integration with existing tools, not disruption of workflows

‍

Rethinking the Quantum Narrative

Sudhi Sachdev:
Quantum computing is often seen as a hardware race. Do you agree with that framing?

‍

Aditya Singh:
That’s the common perception—but it misses the real issue. The limitation today is the mathematics used in simulations. These models haven’t fundamentally changed in decades. Hardware has improved, but it’s only compensating for inefficient methods.

‍

From Hardware Scaling to Mathematical Innovation

Sudhi Sachdev:
What led BQP to focus on changing the math instead of waiting for quantum hardware?

‍

Aditya Singh:
We saw real problems across industries—simulations taking months, increasing complexity, and no scalable solution. Instead of relying on more compute, we rebuilt the algorithms from scratch using quantum-inspired approaches that work on current systems.

‍

Real-World Impact: Speed and Solution Quality

Sudhi Sachdev:
What kind of improvements are you seeing in practice?

‍

Aditya Singh:
It depends on the use case, but we’ve seen problems go from 12 hours to a few minutes—with the same accuracy. More importantly, we don’t just find one solution. We explore the full design space and provide multiple viable outcomes.

‍

Why Classical Systems Fall Short

Sudhi Sachdev:
Where do traditional approaches struggle the most?

‍

Aditya Singh:
As complexity increases, classical methods require exponentially more hardware. They also tend to get stuck in local optima. That limits both speed and the quality of decisions derived from simulations.

‍

India’s Quantum Opportunity—and Gaps

Sudhi Sachdev:
India has strong talent. What’s holding progress back?

‍

Aditya Singh:
Talent isn’t the issue. The gaps are in risk-taking, collaboration, and execution. We don’t see enough consortium-driven innovation, and adoption is slow because organizations wait for proven results instead of experimenting early.

‍

Bridging the Adoption Barrier

Sudhi Sachdev:
How do you address resistance from industries using legacy systems?

‍

Aditya Singh:
You don’t force change—you integrate. We’ve built our solutions to work with existing tools like MATLAB. Engineers don’t need to relearn systems. That makes experimentation much easier.

‍

Transition to Quantum Computing

Sudhi Sachdev:
How should organizations approach quantum adoption today?

‍

Aditya Singh:
It’s a gradual transition—from classical to quantum-inspired, then hybrid, and eventually full quantum. If you wait for mature hardware, you’ll already be behind. The advantage comes from starting now.

‍

Final Perspective

Quantum computing is no longer just theoretical. The algorithms, infrastructure, and talent are already in place. The key differentiator is who starts experimenting first.

→ Listen to the Episode

‍

Guest: Abhishek Chopra, Founder & CEO, BosonQ Psi (BQP)

Podcast: Tech Scenes Unplugged with Jeff James Martin

BQP’s Founder discusses how BQPhy® uses quantum-inspired algorithms to accelerate simulations by over 10x in aerospace and defense. The conversation spans from the science behind hybrid computing to building scalable software from quantum physics.

‍→ Listen Now

‍

Podcast: Aerospace & Defense Technology

This episode explores how quantum-accelerated simulation is redefining digital twins — fromfaster design cycles to real-time mission analytics. Learn how BQP’s collaboration with the U.S.Air Force Research Laboratory (AFRL) is advancing mission-critical modeling through quantum-inspired solvers that bridge today’s HPC systems with tomorrow’s quantum-readyinfrastructure.

→ Listen Now

Podcast: ASV Ventures Tech Series

Abhishek Chopra reflects on BQP’s journey — from a computational challenge at RPI to pioneering quantum-inspired engineering. This conversation touches on startup lessons,algorithmic breakthroughs, and the human side of innovation — where spirituality, physics, andpurpose converge to shape a new era of intelligent simulation.

→ Learn more

In this episode of Coffee with a Founder, Abhishek Chopra shares how outdated mathematics —not compute power — is slowing aerospace innovation. He explains how BQP uses quantum-inspired algorithms to modernize simulation workflows, reduce aircraft design time, and unlockscalable engineering performance.

‍‍

→ Listen Now

‍

Key Takeaways

• Aerospace simulation bottlenecks are mathematical, not computational

• Quantum-inspired algorithms deliver value today

• Hybrid classical-quantum systems are the future

• Founder success depends on coachability and balance

‍

Beck Bamberger:

Welcome to Coffee with a Founder. Today we’re joined by Abhishek Chopra, founder and CEOof BQP. You’re building next-generation solutions to solve computational challenges in aerospace and defense using quantum-inspired algorithms. What made you leave academiaand start this company?

‍

Abhishek Chopra:

Thank you for having me. I’m an aerospace engineer and computational scientist by training. During graduate school, I developed large-scale turbulence simulations. One simulation took sixmonths on 250,000 computers.

‍

Industry typically runs thousands of simulations per component. When you multiply that across millions of components, it becomes unsustainable.

‍

That’s when I started asking: how can we make this faster and more practical?

‍

The Compute vs Math Problem

‍

Beck Bamberger:

Is the issue simply that we need more powerful computers?

‍

Abhishek Chopra:

No. That’s the surprising part.

We already have immense compute power. The problem is that the mathematical foundations of these tools haven’t changed in forty years.

Even when I migrated codes from CPUs to GPUs, including work with national labs and NVIDIA, we saw only incremental gains. The algorithms themselves were outdated.

So the bottleneck is math — not hardware.

‍

Quantum-Inspired Approach

‍

Beck Bamberger:

What does quantum-inspired mean in this context?

‍

Abhishek Chopra:

Quantum-inspired algorithms use mathematical frameworks from quantum information science but run on classical hardware like CPUs and GPUs.

Quantum computers will eventually become part of data centers. But they will sit alongside CPUs and GPUs, not replace them.

Today, we can extract significantly more performance from existing infrastructure by improving the mathematics.

‍

Real-World Impact

‍

Beck Bamberger:

Can you share an example?

‍

Abhishek Chopra:

We worked with an aerospace company to redesign aircraft wings. By improving simulation efficiency, they were able to reduce weight, cut fuel costs, and accelerate design cycles.

This is what better math enables.

‍

Founder Growth and Mentorship

‍

Beck Bamberger:

What has been your biggest unlock as a founder?

‍

Abhishek Chopra:

penness and coachability.

Coming from academia, it’s easy to carry pride in science. But building a company requires humility. It requires being open to new business models, pricing strategies, and partnerships.

A mentor once told me: always walk in the path of opening more doors. When making decisions, choose the path that expands opportunity.

That principle still guides me

‍

Work-Life Balance

‍

Beck Bamberger:

How do you sustain this long-term?

‍

Abhishek Chopra:

Entrepreneurship is a marathon. Maintaining health, relationships, and balance is critical.

Professional success matters, but personal well-being sustains the journey.


→ Listen Now

Podcast: The David Daily Show

Quantum computing is transitioning from theory to real aerospace and defense workflows. Inthis episode, Abhishek discusses how quantum-inspired simulations are already solvingcomplex flight challenges, the importance of partnerships like NVIDIA, and why buildingquantum-ready infrastructure today is critical for long-term competitiveness.

→ Listen Now

‍

Key Takeaways

• Quantum-inspired computing already delivers real engineering value

• Aerospace simulation is a leading application domain

• GPU infrastructure is enabling near-term scalability

• Quantum readiness depends on system-level preparation, not just hardware

‍

David Goecke:

Quantum computing is often discussed as a future technology. From your perspective, where are we seeing real-world applications today?

‍

Abhishek Chopra:

We are already seeing value in engineering workflows, particularly in simulation. Instead of waiting for large-scale quantum hardware, we use quantum-inspired algorithms that run on classical systems to solve complex optimization and physics problems.

‍

Quantum in Aerospace Workflows

David Goecke:

What makes aerospace a strong early application area?

‍

Abhishek Chopra:

Aerospace simulations involve extremely complex physics and large datasets. Traditional approaches require significant simplifications. Quantum-inspired methods help reduce computational bottlenecks and allow higher-fidelity modeling.

‍

Infrastructure and GPU Ecosystems

David Goecke:

How important is the role of modern computing infrastructure?

‍

Abhishek Chopra:

It is critical. Advances in GPU computing have enabled us to scale these algorithms effectively. By leveraging high-performance computing systems, we can deliver practical solutions today while building pathways toward future quantum integration.

‍

Preparing for the Quantum Transition

‍

David Goecke:

What should organizations focus on now?

‍

Abhishek Chopra:

They should focus on infrastructure readiness, workforce capabilities, and scalable computational workflows. The transition to quantum computing will be gradual, andpreparation today determines long-term advantage.

‍

→ Listen to the Episode

Quantum computing is often associated with future breakthroughs and next-generation hardware. But for many engineering organizations, the value is already emerging today through quantum-inspired software running on existing infrastructure.

In this episode of Tech Talks Daily, Nathan Mason, VP of Strategic Growth at BQP, discusses how quantum-inspired computing is helping aerospace and defense organizations solve complex simulation, optimization, and decision-making challenges without waiting for large-scale quantum computers.

→ Listen Now

‍

Key Takeaways

• Quantum-inspired computing delivers practical value on existing CPU and GPU infrastructure

• Aerospace and defense present some of the most complex optimization challenges in engineering

• Simulation accuracy and computational efficiency can improve without increasing hardware resources

• Data-driven decision making is becoming essential across engineering and operational environments

• Quantum readiness begins with workflow transformation, not hardware adoption

‍

From Military Service to Deep Tech Innovation

Tech Talks Daily:

Your career spans military service, intelligence, venture capital, and now quantum-inspired computing. What led you here?

‍

Nathan Mason:

My career has always centered around solving difficult problems through engineering and data. Whether in national security, venture investing, or deep technology, the common thread has been understanding how emerging technologies can create real operational advantages.

What attracted me to quantum-inspired computing was its ability to solve challenges across multiple industries rather than being confined to a single application area.

‍

What Quantum-Inspired Computing Looks Like Today

Tech Talks Daily:

Many people still think quantum is years away. What does practical adoption look like right now?

‍

Nathan Mason:

One of the biggest misconceptions is that organizations need quantum hardware before they can benefit from quantum technologies.

At BQP, we apply quantum-inspired methods on existing CPUs and GPUs to improve optimization, simulation, and modeling workflows. Organizations can achieve meaningful performance gains today while preparing for future quantum integration.

‍

Why Aerospace Is an Early Adoption Sector

Tech Talks Daily:

Why are aerospace and defense such important application areas?

‍

Nathan Mason:

These industries operate some of the most computationally demanding systems in the world. Whether you're designing aircraft, optimizing missions, modeling physical systems, or managing space assets, the complexity grows exponentially.

Traditional approaches often force engineers to simplify problems. Quantum-inspired optimization allows organizations to explore larger solution spaces more efficiently and make better engineering decisions.

‍

Beyond Adding More Hardware

Tech Talks Daily:

How are organizations approaching growing computational demands?

‍

Nathan Mason:

Many organizations respond by adding more compute resources. But increasing hardware alone doesn't always solve the underlying challenge.

The real opportunity is improving how efficiently systems utilize existing infrastructure. By applying more advanced optimization methods, organizations can unlock significantly greater performance without continually expanding hardware investments.

‍

The Role of Data-Driven Decision Making

Tech Talks Daily:

You often talk about decision making. Why is that becoming increasingly important?

‍

Nathan Mason:

The scale and complexity of modern systems make intuition alone insufficient. Whether you're working in aerospace, defense, AI, or operations, decisions increasingly depend on the ability to process large volumes of information quickly and accurately.

Advanced computational methods help organizations make better decisions under uncertainty while reducing operational risk.

‍

Building Careers at the Intersection of AI, Aerospace and Quantum

Tech Talks Daily:

What advice would you give students and professionals entering these fields?

‍

Nathan Mason:

Stay curious and continue learning. Many of the opportunities shaping future industries sit at the intersection of multiple disciplines.

The technologies may evolve, but curiosity, adaptability, and a willingness to engage with new ideas remain constant advantages throughout a career.

‍

Moving Quantum from Theory to Deployment

The conversation highlights an important shift occurring across the technology landscape.

Quantum-inspired computing is no longer purely a research topic. Organizations are already using these approaches to improve simulation accuracy, accelerate optimization workloads, and solve engineering challenges across aerospace and defense environments.

The transition to quantum computing will happen over time, but the journey has already begun.

‍

→ Listen to the Episode

‍

Quantum computing is moving beyond theoretical research into real-world engineering and optimization workflows. In this episode, Nate Mason from BQP discusses how quantum-inspired algorithms are already improving GPU efficiency, accelerating complex simulations, and helping organizations prepare for the transition toward quantum-ready infrastructure.

The conversation explores the convergence of quantum computing, AI, aerospace systems, cybersecurity, and large-scale optimization — and why organizations should begin preparing now rather than waiting for fully commercial quantum hardware.

→ Listen Now

‍

Key Takeaways

• Quantum-inspired algorithms already improve real-world optimization workflows

• Quantum readiness is about infrastructure, workflows, and engineering adaptation

• GPU inefficiencies create major compute and energy bottlenecks

• AI and quantum-inspired systems are beginning to converge in practical applications

• Aerospace, defense, and simulation environments are early adoption sectors

‍

From National Security to Quantum Systems

Chris Gannatti:

You have a very unconventional background for someone working in quantum computing. How did that journey happen?

‍

Nate Mason:

My background started in the Air Force working in electrical engineering and cryptography, much of that alongside the National Security Agency. Over time, I became exposed to emerging technologies through venture capital and started looking at the convergence between AI, quantum computing, and national security applications.

What attracted me to quantum was that it is not isolated to one industry. It applies across aerospace, defense, logistics, finance, AI, and engineering optimization.

‍

Quantum Computing Beyond Hardware

Elvira Shishkina:

Many people still associate quantum computing only with hardware. How do you explain what BQP does?

‍

Nate Mason:

At BQP, we focus on quantum-inspired algorithms that run on existing infrastructure. We apply principles from quantum mechanics mathematically to improve optimization and computational efficiency on today’s CPU and GPU systems.

The key point is that organizations do not need to wait for large-scale quantum hardware to start seeing value.

‍

Solving the GPU Efficiency Problem

Chris Gannatti:

What problem are you trying to solve inside modern compute systems?

‍

Nate Mason:

A major issue today is GPU underutilization. Much of the mathematical framework powering current GPU systems was originally developed decades ago for CPUs.

That creates idle compute capacity and forces organizations to scale infrastructure inefficiently. Our quantum-inspired optimization methods improve throughput and reduce the need for excessive hardware expansion.

‍

AI and Quantum Convergence

Elvira Shishkina:

We hear increasingly about the convergence of AI and quantum systems. What are you seeing today?

‍

Nate Mason:

One major area is quantum transfer learning. We have reduced AI model sizes dramatically while maintaining effectiveness.

In one example, we reduced a model with 14.5 million trainable parameters down to approximately 2,000. That creates major gains in efficiency, deployment flexibility, and edge computing capability.

‍

Aerospace and Space Domain Optimization

Chris Gannatti:

You mentioned space systems and orbital propagation work. Where does quantum-inspired optimization fit there?

‍

Nate Mason:

We developed orbital propagation models for space domain awareness challenges. Using quantum-inspired methods, we achieved approximately 250x faster propagation performance while maintaining highly accurate positioning predictions.

The broader challenge is that space infrastructure is scaling rapidly. Optimization becomes essential for managing satellite constellations, collision prediction, and operational efficiency.

‍

Preparing for the Quantum Transition

Elvira Shishkina:

What should organizations be doing today to prepare for quantum computing?

‍

Nate Mason:

Organizations should start building quantum-ready workflows now.

That means:

• Preparing infrastructure
• Training engineering teams
• Understanding optimization-first approaches
• Integrating quantum-inspired methods into existing environments

The transition will not happen overnight. It will be gradual, and organizations that prepare early will have a major competitive advantage.

‍

Quantum Readiness Starts Before Quantum Hardware

The discussion reinforces an important shift happening across the industry:

Quantum adoption is no longer only about future hardware breakthroughs.

It is increasingly about:

• Optimization efficiency
• Workflow modernization
• GPU utilization
• Hybrid compute systems
• Quantum-inspired algorithmic infrastructure

‍

→ Listen to the Episode

‍

Key Takeaways

• The core bottleneck in simulation is mathematical models—not hardware

• Quantum-inspired algorithms are already delivering ~10× improvements on classical systems

• Future gains (~1000×) depend on both algorithmic evolution and hardware maturity

• India’s challenge is not talent, but risk appetite, execution, and ecosystem alignment

• Adoption requires integration with existing tools, not disruption of workflows

‍

Rethinking the Quantum Narrative

Sudhi Sachdev:
Quantum computing is often seen as a hardware race. Do you agree with that framing?

‍

Aditya Singh:
That’s the common perception—but it misses the real issue. The limitation today is the mathematics used in simulations. These models haven’t fundamentally changed in decades. Hardware has improved, but it’s only compensating for inefficient methods.

‍

From Hardware Scaling to Mathematical Innovation

Sudhi Sachdev:
What led BQP to focus on changing the math instead of waiting for quantum hardware?

‍

Aditya Singh:
We saw real problems across industries—simulations taking months, increasing complexity, and no scalable solution. Instead of relying on more compute, we rebuilt the algorithms from scratch using quantum-inspired approaches that work on current systems.

‍

Real-World Impact: Speed and Solution Quality

Sudhi Sachdev:
What kind of improvements are you seeing in practice?

‍

Aditya Singh:
It depends on the use case, but we’ve seen problems go from 12 hours to a few minutes—with the same accuracy. More importantly, we don’t just find one solution. We explore the full design space and provide multiple viable outcomes.

‍

Why Classical Systems Fall Short

Sudhi Sachdev:
Where do traditional approaches struggle the most?

‍

Aditya Singh:
As complexity increases, classical methods require exponentially more hardware. They also tend to get stuck in local optima. That limits both speed and the quality of decisions derived from simulations.

‍

India’s Quantum Opportunity—and Gaps

Sudhi Sachdev:
India has strong talent. What’s holding progress back?

‍

Aditya Singh:
Talent isn’t the issue. The gaps are in risk-taking, collaboration, and execution. We don’t see enough consortium-driven innovation, and adoption is slow because organizations wait for proven results instead of experimenting early.

‍

Bridging the Adoption Barrier

Sudhi Sachdev:
How do you address resistance from industries using legacy systems?

‍

Aditya Singh:
You don’t force change—you integrate. We’ve built our solutions to work with existing tools like MATLAB. Engineers don’t need to relearn systems. That makes experimentation much easier.

‍

Transition to Quantum Computing

Sudhi Sachdev:
How should organizations approach quantum adoption today?

‍

Aditya Singh:
It’s a gradual transition—from classical to quantum-inspired, then hybrid, and eventually full quantum. If you wait for mature hardware, you’ll already be behind. The advantage comes from starting now.

‍

Final Perspective

Quantum computing is no longer just theoretical. The algorithms, infrastructure, and talent are already in place. The key differentiator is who starts experimenting first.

→ Listen to the Episode

‍

Guest: Abhishek Chopra, Founder & CEO, BosonQ Psi (BQP)

Podcast: Tech Scenes Unplugged with Jeff James Martin

BQP’s Founder discusses how BQPhy® uses quantum-inspired algorithms to accelerate simulations by over 10x in aerospace and defense. The conversation spans from the science behind hybrid computing to building scalable software from quantum physics.

‍→ Listen Now

‍

Podcast: Aerospace & Defense Technology

This episode explores how quantum-accelerated simulation is redefining digital twins — fromfaster design cycles to real-time mission analytics. Learn how BQP’s collaboration with the U.S.Air Force Research Laboratory (AFRL) is advancing mission-critical modeling through quantum-inspired solvers that bridge today’s HPC systems with tomorrow’s quantum-readyinfrastructure.

→ Listen Now

Podcast: ASV Ventures Tech Series

Abhishek Chopra reflects on BQP’s journey — from a computational challenge at RPI to pioneering quantum-inspired engineering. This conversation touches on startup lessons,algorithmic breakthroughs, and the human side of innovation — where spirituality, physics, andpurpose converge to shape a new era of intelligent simulation.

→ Learn more

In this episode of Coffee with a Founder, Abhishek Chopra shares how outdated mathematics —not compute power — is slowing aerospace innovation. He explains how BQP uses quantum-inspired algorithms to modernize simulation workflows, reduce aircraft design time, and unlockscalable engineering performance.

‍‍

→ Listen Now

‍

Key Takeaways

• Aerospace simulation bottlenecks are mathematical, not computational

• Quantum-inspired algorithms deliver value today

• Hybrid classical-quantum systems are the future

• Founder success depends on coachability and balance

‍

Beck Bamberger:

Welcome to Coffee with a Founder. Today we’re joined by Abhishek Chopra, founder and CEOof BQP. You’re building next-generation solutions to solve computational challenges in aerospace and defense using quantum-inspired algorithms. What made you leave academiaand start this company?

‍

Abhishek Chopra:

Thank you for having me. I’m an aerospace engineer and computational scientist by training. During graduate school, I developed large-scale turbulence simulations. One simulation took sixmonths on 250,000 computers.

‍

Industry typically runs thousands of simulations per component. When you multiply that across millions of components, it becomes unsustainable.

‍

That’s when I started asking: how can we make this faster and more practical?

‍

The Compute vs Math Problem

‍

Beck Bamberger:

Is the issue simply that we need more powerful computers?

‍

Abhishek Chopra:

No. That’s the surprising part.

We already have immense compute power. The problem is that the mathematical foundations of these tools haven’t changed in forty years.

Even when I migrated codes from CPUs to GPUs, including work with national labs and NVIDIA, we saw only incremental gains. The algorithms themselves were outdated.

So the bottleneck is math — not hardware.

‍

Quantum-Inspired Approach

‍

Beck Bamberger:

What does quantum-inspired mean in this context?

‍

Abhishek Chopra:

Quantum-inspired algorithms use mathematical frameworks from quantum information science but run on classical hardware like CPUs and GPUs.

Quantum computers will eventually become part of data centers. But they will sit alongside CPUs and GPUs, not replace them.

Today, we can extract significantly more performance from existing infrastructure by improving the mathematics.

‍

Real-World Impact

‍

Beck Bamberger:

Can you share an example?

‍

Abhishek Chopra:

We worked with an aerospace company to redesign aircraft wings. By improving simulation efficiency, they were able to reduce weight, cut fuel costs, and accelerate design cycles.

This is what better math enables.

‍

Founder Growth and Mentorship

‍

Beck Bamberger:

What has been your biggest unlock as a founder?

‍

Abhishek Chopra:

penness and coachability.

Coming from academia, it’s easy to carry pride in science. But building a company requires humility. It requires being open to new business models, pricing strategies, and partnerships.

A mentor once told me: always walk in the path of opening more doors. When making decisions, choose the path that expands opportunity.

That principle still guides me

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Work-Life Balance

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Beck Bamberger:

How do you sustain this long-term?

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Abhishek Chopra:

Entrepreneurship is a marathon. Maintaining health, relationships, and balance is critical.

Professional success matters, but personal well-being sustains the journey.


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Podcast: The David Daily Show

Quantum computing is transitioning from theory to real aerospace and defense workflows. Inthis episode, Abhishek discusses how quantum-inspired simulations are already solvingcomplex flight challenges, the importance of partnerships like NVIDIA, and why buildingquantum-ready infrastructure today is critical for long-term competitiveness.

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Key Takeaways

• Quantum-inspired computing already delivers real engineering value

• Aerospace simulation is a leading application domain

• GPU infrastructure is enabling near-term scalability

• Quantum readiness depends on system-level preparation, not just hardware

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David Goecke:

Quantum computing is often discussed as a future technology. From your perspective, where are we seeing real-world applications today?

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Abhishek Chopra:

We are already seeing value in engineering workflows, particularly in simulation. Instead of waiting for large-scale quantum hardware, we use quantum-inspired algorithms that run on classical systems to solve complex optimization and physics problems.

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Quantum in Aerospace Workflows

David Goecke:

What makes aerospace a strong early application area?

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Abhishek Chopra:

Aerospace simulations involve extremely complex physics and large datasets. Traditional approaches require significant simplifications. Quantum-inspired methods help reduce computational bottlenecks and allow higher-fidelity modeling.

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Infrastructure and GPU Ecosystems

David Goecke:

How important is the role of modern computing infrastructure?

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Abhishek Chopra:

It is critical. Advances in GPU computing have enabled us to scale these algorithms effectively. By leveraging high-performance computing systems, we can deliver practical solutions today while building pathways toward future quantum integration.

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Preparing for the Quantum Transition

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David Goecke:

What should organizations focus on now?

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Abhishek Chopra:

They should focus on infrastructure readiness, workforce capabilities, and scalable computational workflows. The transition to quantum computing will be gradual, andpreparation today determines long-term advantage.

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→ Listen to the Episode