Quantum Computing is in production today with Adiabatic Systems. Even NISQ quantum solutions have great potential!

Why Jensen Huang, CEO of Nvidia, was So Wrong About Quantum Computing

Jensen Huang made a very controversial statement during a Q&A at CES 2025.

Basically, he said that practical quantum computers are 20 years in the future and that that was a reasonable estimate.

He is a very smart person, and I respect him a lot, so I’ll assume that he meant quantum computers coming to the individual desktop.

Because quantum computing systems are already in production for adiabatic quantum computers (the major player here being D-Wave).

And Gate-based quantum computing systems, despite facing several challenges, are just the forerunners of a massive revolution that will take place once quantum hardware improves.

Unlike my usual articles, I am going to link to my sources inline so that you can check and verify my claims by yourself while reading this article.

D-Wave’s CEO had a lot to say about Jensen Huang’s statement (full text linked below):

He has good reasons to make this statement.

Industrial Uses Already in Production - Adiabatic Quantum Systems

D-Wave has quantum annealers (the optimization method used in adiabatic systems).

They are the first quantum computing company that has actual solutions in production environments and in the industry today.

Some of their industrial use cases in production today are:

Logistics Optimization

Momentum Worldwide’s 18,000 brand experience tour planning and optimal logistics optimization involved 4500+ stores across the US. What normally takes months of work took D-Wave one hour.

Source: https://www.dwavesys.com/media/t5be11b4/logistics-routing-data-sheet.pdf

Resource Optimization

DOCOMO in Japan needed to configure 270 base stations from the three demonstration regions into different tracking groups. DWave solved the subset of the problem in 40 seconds. Now, the company is exploring putting quantum optimization into place for 250,000 base stations.

Source: https://www.dwavesys.com/media/wagd4haj/ntt-docomo-case-studyv3f.pdf

Workforce Scheduling Optimization

For large companies having the right people at the right places is a very difficult problem. A variation of an optimization problem was used to schedule Pattison Food Group, a company in Canada, resulting in an 80% reduction of the workload.

Source: https://www.dwavesys.com/media/lr0fs3k3/eos_paper_v2-3.pdf

There are hundreds of cases where quantum optimization can help businesses.

Some of the more interesting use cases are linked below, highlighting the versatility and the universality of this algorithm:

Quantum-Classical Optimization for Production Scheduling

Efficient Space Satellites Mission Planning Optimization With Quantum Algorithm

There are hundreds of these applications, linked below:

What About Other Quantum Companies That Use Gate-Based Quantum Technology?

These companies, such as IBM (the leader in this space), Google, Microsoft, Rigetti, and IonQ have significant challenges to overcome.

Significantly, they need to address:

• Qubit Decoherence:
• Qubits are highly sensitive to their environment, and interactions with external factors can cause them to lose their quantum properties, a phenomenon known as decoherence. This limits the time available for computations.

• Scalability
• Current quantum computers are relatively small, and scaling them to hundreds or thousands of qubits while maintaining coherence and low error rates is a major challenge.

• Error Correction
• Quantum systems are prone to errors due to noise and decoherence. Developing reliable quantum error correction techniques is essential for building practical quantum computers.

• Control and Calibration
• As the number of qubits increases, controlling individual qubits becomes increasingly complex. Calibration of control electronics is also challenging.

• Hardware Development
• Creating high-quality quantum hardware, including qubits and control electronics, remains a significant challenge. Different qubit technologies have varying strengths and weaknesses.

• Computational Speed
• Gate operations must occur quickly enough to complete computations before qubits lose coherence. The speed at which gates can be executed is limited by the control electronics.

• Multi-Qubit Networking
• Linking multiple qubits together for gate operations is essential for implementing quantum algorithms. However, creating entanglement across more than two qubits remains a challenge.

Therefore, NISQ systems have very real problems.

This is possibly one of the reasons for Jensen Huang’s statement:

Even quantum computing researchers doubt that real-world algorithms can be built on NISQ systems.

But - we have a very promising future for quantum computing in the near future.

Why?

Fault-Tolerant Quantum Computing (FTQC for short)!

Fault-Tolerant Quantum Computing: The Next Quantum Leap (Double Pun Intended)

Some researchers believe that we are on the cusp of a new era.

Fault-Tolerant Quantum Computing, or FTQC, for short.

FTQC is defined as performing quantum computation reliably even in the case of errors and faults.

For more information, see the link below:

This is the next generation of quantum computing.

As the article states, it could be a slow transition instead of a quantum leap.

Fault-tolerant QC means that we could finally see quantum computing that will lead to computational advantages and true quantum supremacy.

While most people believe that few algorithms run on quantum computers, research shows otherwise.

The following paper is an interesting read for quantum researchers, and it highlights over 150 algorithms for quantum computers.

A Typology of Quantum Algorithms (Research Paper)

The majority of them are used in FTQC, but 53 algorithms run even on the NISQ-era quantum computers.

The variety of algorithms available is a real eye-opener.

A subset of the most important algorithms in this paper is given below:

Due to the length of the original list being very long, the output has been truncated significantly.

This extract from the research paper was generated by Google AI Studio, available at this link:

https://aistudio.google.com/

1. Hidden Subgroup Problems

The Hidden Subgroup Problem is a cornerstone of quantum computing that underpins many significant algorithms and applications. Its study continues to evolve, particularly with advancements in variational methods and applications beyond traditional cryptography.

• Shor's Algorithm: Solves the problem of integer factorization and discrete logarithms. Crucial for breaking widely used cryptographic systems.

• Simon's Algorithm: Finds a hidden period in a function. Demonstrates a quantum speedup over classical algorithms.

• Exact Hidden Subgroup Algorithm: Aims to solve the hidden subgroup problem in a more general setting. This includes Shor and Simon's algorithms as special cases.

• Hallgren's Algorithm: Solves problems related to principal ideals and Pell's equation in number theory. This is a significant algorithm in cryptanalysis.

2. Linear Algebra

Linear Algebra is a foundational mathematical framework for quantum computing, providing the tools necessary to describe quantum states, operations, and algorithms.

• Quantum Fourier Transform (QFT): Performs a Fourier transform on quantum states. Fundamental subroutine for many other quantum algorithms.

• Quantum Phase Estimation (QPE): Estimates the phase of an eigenvector of a unitary operator. A core subroutine for several other quantum algorithms.

• Quantum Amplitude Estimation (QAE): Estimates the amplitude of a specific quantum state. Often used in combination with amplitude amplification.

• Quantum Singular Value Transformation (QSVT): Transforms a singular value of a matrix into a probability distribution. Useful for various tasks including Hamiltonian simulation.

• Harrow-Hassidim-Lloyd (HHL) Algorithm: Solves systems of linear equations. Provides a quantum speedup for specific problem instances.

• Variational Quantum Eigensolver (VQE): Finds the ground state energy of a quantum system. Widely used for simulating molecules in quantum chemistry. (NISQ)

• Variational Quantum Singular Value Decomposition (QSVD): Calculates the singular value decomposition of a quantum state. Finds application in the field of machine learning and data science.

• Quantum Principal Component Analysis: A quantum algorithm for dimensionality reduction. Useful for data analysis and machine learning.

• Quantum Linear Regression: Performs linear regression using quantum techniques. Aims to outperform classical linear regression in specific settings.

• Quantum Support Vector Machine (QSVM): Implements support vector machines on a quantum computer. Used for classification tasks in machine learning.

3. Dynamical Systems

Dynamical Systems for Quantum Computing involves the study and simulation of systems that evolve over time according to specific rules, particularly in the context of quantum mechanics.

• Quantum Approximate Optimization Algorithm (QAOA): Approximates solutions to combinatorial optimization problems. Widely used due to its suitability for NISQ devices. (NISQ)

• Quantum Adiabatic Algorithm (QA): Finds the ground state of a Hamiltonian. Used for solving optimization problems and quantum simulations.

• Variational Quantum Eigensolver (VQE): Finds the ground state energy of a quantum system. Frequently employed for quantum chemistry simulations. (NISQ)

• Q. Imaginary-Time Evolution (QITE): Computes the ground state of a quantum system using imaginary time evolution. Applicable to various quantum systems.

• Variational Protein-Folding Simulation: Simulates protein folding using variational techniques. Addresses a computationally hard problem.

4. Stochastic Processes & Statistics

Stochastic Processes and Statistics for Quantum Computing involve the study of random processes and their statistical properties within the framework of quantum mechanics.

• Gaussian Boson Sampling (GBS): Generates samples from a Gaussian boson distribution. Used for studying quantum phenomena and demonstrating quantum supremacy.

• Gaussian Boson Sampling (GBS) Matrix Point Process: A more advanced form of GBS, suitable for certain statistical analysis problems. Provides insights into correlations in complex systems.

• Quantum Nearest-Neighbor Classification: Classifies data points based on nearest-neighbor distances. A fundamental algorithm in machine learning. (NISQ)

• Quantum Hierarchical Clustering: Performs hierarchical clustering. Builds a tree-like structure of cluster hierarchies. (NISQ)

• Quantum Monte-Carlo Integral: Performs numerical integration using quantum techniques. Useful for various computational tasks.

• Quantum Counting (various implementations): Counts the number of elements that satisfy a given condition. Useful in search algorithms and other combinatorial problems.

5. Optimization

Optimization for Quantum Computing involves leveraging quantum algorithms to solve optimization problems more efficiently than classical methods. This area has gained significant attention due to the potential of quantum computers to outperform classical systems in specific types of optimization tasks.

• Quantum Approximate Optimization Algorithm (QAOA): Approximates solutions to combinatorial optimization problems. Works well on near-term quantum computers. (NISQ)

• Variational Quantum Eigensolver (VQE): Finds the ground state of a Hamiltonian. Used for solving optimization problems and simulating quantum systems. (NISQ)

• Quantum Adiabatic Algorithm (QA): Finds the ground state of a Hamiltonian. Used for solving optimization problems and quantum simulations.

• Reverse-Q.-Annealing Approach to Portfolio Optimization Problems: Optimizes portfolio allocation using reverse quantum annealing. Applies specifically to finance. (NISQ)

• Quantum Linear Programming: Solves linear programming problems using quantum techniques. A powerful tool for optimization.

• Quantum Semi-Definite Programming (QSDP): Solves semi-definite programming problems. Useful in various fields, including optimization and machine learning.

• Quantum Simplex Method: Solves linear programming problems using a quantum version of the simplex method. Offers potential improvements over classical methods.

6. Combinatorics

Combinatorics for Quantum Computers focuses on leveraging quantum computing techniques to solve combinatorial optimization problems, which involve finding the best solution from a finite set of possible solutions. This area has gained significant traction due to quantum computers' potential to outperform classical algorithms in specific combinatorial tasks.

• Grover's Algorithm: Finds a specific item in an unsorted database. Offers a quadratic speedup over classical search.

• Amplitude Amplification (AA): Amplifies the amplitude of a desired quantum state. Crucial for Grover's algorithm and other quantum search algorithms.

• Quantum Walk Search Algorithms: Use quantum walks for searching. Offers a quadratic speedup over classical search in certain scenarios.

• Quantum Counting: Counts the number of solutions to a given problem. Useful in various applications.

• Simon's Algorithm: Finds a hidden period in a function. Demonstrates a quantum speedup over classical algorithms.

All of these algorithms (and many more not listed for brevity) will explode into common use once quantum computing hardware improves to FTQC levels!

Future Outlook

This is only a subset of the vast list of quantum algorithms available to quantum researchers.

When FTQC is developed, we will see a vast explosion of applications in nearly every scientific field available today.

The huge list of quantum algorithms available above illustrates that.

We are seeing worldwide research and development in multiple approaches and multiple paradigms.

Withdrawing funding or losing confidence because of careless comments would be a big step backward.

We are close to the FTQC era, and the transition will likely be gradual.

The last thing we need is to lose our confidence in scientific progress now.

There is a vast amount of research waiting for the next hardware breakthrough to happen.

And with breakthroughs like the Google Willow chip (more details available on the link below), we are coming closer and closer to an explosive new future.

Google’s Willow Chip for Quantum Computing

Willow is a real breakthrough for quantum hardware.

So -

Driven by the promise of the explosion in quantum applications that will take place if reliable quantum hardware becomes available -

All researchers worldwide should be considering quantum computing as a career.

Quantum computing is unlike any other technology man has developed.

It has potential according to the algorithms that are discovered.

And for research in quantum computation, you need to learn quantum mechanics.

Which is a very big hurdle.

But remind yourself that this is the next big step for mankind next to AGI.

Use AI tools like NotebookLM and Google Gemini to teach yourself quantum mechanics.

I recommend Quantum Mechanics - Theory and Applications by Nouredine Zetilli as a good introductory text.

The link to purchase is given below (this is not an affiliate link):

https://www.amazon.in/Quantum-Mechanics-Applications-Nouredine-Zettili

And learn quantum computing.

Jensen Huang’s comments reflect a very, very conservative approach.

I recommend two final articles, that shed more light on that topic.

And this deeply authoritative post on LinkedIn:

Conclusion

This time, the references are in the text, so there is no list of references!

Quantum Computing and AGI are perhaps the two most exciting scientific challenges of our time.

And China is well ahead of the Western world in quantum computing.

The US and EU need to play catch-up!

China spent 15 billion USD on quantum computing in 2024.

In comparison, in the same year, the US spent 5 billion.

Those numbers have to change.

Quantum Computing needs all the research money it can get.

And hopefully - the next wave of quantum computation breakthroughs with FTQC will happen anywhere between 2-4 years.

And quantum computation should be practical in a decade.

Which will happen - with the right mental outlook and research funding.

All the very best of luck in your quantum career!

All Images AI-generated by the author by Canva AI Art Generator, available at this link: https://www.canva.com/ai-art-generator/