Qunova Computing: Shaping the Future of Quantum Chemistry on Commercial Quantum Computers

Quantum computing holds remarkable potential for revolutionizing fields like chemistry, materials discovery, and drug development. 

The reason is intuitive: using one quantum system (a quantum computer) to compute the properties of another (a molecule or material) allows for a more natural and efficient capture of quantum characteristics, such as electron correlations, than a classical computer could ever achieve. 

However, the challenge is each computational step and measurement introduces errors that quickly accumulate, impacting the accuracy of results. How can one develop smarter quantum algorithms that produce valuable results for quantum chemistry while leveraging the capabilities of today’s quantum hardware? 

Qunova Computing was founded in 2021 by Kevin (June-Koo) Rhee, a professor at the Korea Advanced Institute of Science and Technology (KAIST), who has been researching quantum algorithms for a long time. He and his team have developed a quantum algorithm that can solve quantum chemistry problems 1,000 times more efficiently with chemical accuracy—the accuracy level needed to predict chemical behaviors reliably.

In 2023, Qunova Computing raised a pre-Series A round from Hyundai ZER01NE, Smilegate Investment, Laguna Investment, BluePoint Partners, ID Ventures, and KB Investment.

Learn more about the future of quantum chemistry on commercial quantum computers from our interview with the founder and Chief Executive Officer, Kevin (June-Koo) Rhee:

Why Did You Start Qunova Computing?

Being a long-standing member of the Korea Advanced Institute of Science and Technology (KAIST), I have studied quantum computing for 30 years. Already in 1996, I had been dreaming of making quantum computing work, but it was too early. As the technology matured over the years, I taught at university. Then, around 2010, major advancements in qubit coherence times, entanglement control, and early quantum algorithm demonstrations brought a lot of interest back into the field.

I pursued quantum computing in academia for another ten years until I saw that the timing would be right to use quantum computing for commercial applications. From the start, we had a clear idea that the quantum computing market would flourish in 2026 and beyond. I counted how many years earlier I would need to start to build the company, raise funding, and do several rounds of product development to address that emerging quantum market. 

2021 seemed like the right timing, so I started Qunova Computing three and a half years ago as one of Korea’s first quantum computing startups. We went through the Creative Destruction Lab in Toronto and focused on quantum chemistry problems, inventing a new algorithm called Handover Iteration VQE (HiVQE). 

How Does Variational Quantum Eigensolver Work?

Variational Quantum Eigensolver (VQE) is a well-known hybrid quantum-classical algorithm to determine the ground state energy of a quantum system such as a molecule.

The basic idea is to start by guessing a system’s ground state, i.e., prepare a trial quantum state, measure its ground state energy on a quantum computer, and then use a classical optimizer to tweak the trial quantum state’s parameters to lower its ground state energy. The process repeats iteratively, refining the trial quantum state until its ground state energy converges to the lowest possible value, ideally close to the true ground state energy. 

This approach leverages a quantum computer’s strengths in handling complex quantum states while using classical computing for optimization. The challenge is that each step and measurement on a quantum computer accumulates errors impairing the accuracy of the results, so traditional VQE has never achieved chemical accuracy on quantum hardware so far despite requiring extensive computational resources. 

How Does Handover Iteration VQE Work?

We have developed VQE further into what we call Handover Iteration VQE (HiVQE). This improved quantum algorithm is three orders of magnitude faster and may take only ten to fifteen minutes instead of days to determine the ground state energy on the same quantum hardware as compared to traditional VQE.

The key innovation is that we found a way to remove a particular type of quantum measurement, called “Pauli word measurements”—measurements of a quantum state involving a string of Pauli operators to measure the quantum state along different axes. 

Each Pauli operator in the string requires a different basis, so Pauli word measurements are computationally costly, increase computational complexity, and they introduce errors in measuring the energy of a quantum state. This makes the job of the classical optimizers way harder to tweak the quantum state to minimize its ground state energy.  

We can avoid a lot of computational overhead by removing “Pauli word measurements” and identifying only the relevant orbitals of a molecule, omitting empty basis vectors. 

How Did You Benchmark Your Algorithm for Quantum Chemistry Problems?

We have been testing our algorithm for molecules like water, inorganic compounds like lithium sulfide or hydrogen sulfide, or organic compounds like methane or ethylene. Running it on a 20-qubit IQM machine, we could predict ground state energies with an accuracy below 1 kcal/mol or 4 kJ/mol, commonly called ‘chemical accuracy’—the threshold to reliably predict chemical behaviors. It is the first time such an accuracy has been reached on a commercially available quantum computer.

The results show that using our HiVQE solution reduces the computational resources by 1,000 times or more when compared with traditional VQEs. Thus, our algorithm can potentially deliver a quantum advantage for chemical computations over classical computers, using a NISQ machine with as few as 40-60 qubits. 

Running a 40-qubit problem is next on our roadmap, and we expect to see the same scaling behavior. By the end of this year, we want to complete a project involving 60 qubits and be able to address real-world use cases.

In the short term, our key advantage is speed – running such problems on a supercomputer would take days, while we can get you a result within minutes. We’ll be even faster with more qubits available and able to solve problems that supercomputers would never be able to solve on a practical timescale. And that’s our goal: to convert intractable classical problems into tractable computing problems using quantum computers.

What’s the Opportunity for Qunova Computing?

When a pharma company develops a new drug, it typically tests thousands of designs, but as of today, it can’t evaluate all of them against a target using simulations on a supercomputer. With our algorithms, this will become possible.

Also, many drug designers use AI, but the challenge is that AI today is very sensitive to the training data. And if there’s no data, you cannot train AI. Our solutions can help to generate more training data for AI. 

We will start licensing our software product and provide our algorithms as an API or software platform. Developers can choose which quantum computer they like to use, and we’ll make sure that our algorithms perform 1000x better than others. 

Eventually, we will build a whole platform to design a new drug, produce material IP, and license it. It will be especially useful for small molecule drug design and investigating the electronic structure of molecules. We’re also looking into adjacent verticals, such as solving optimization problems, but it’s still very early, and our main focus is on chemical companies and targeting the pharma industry. 

What Advice Would You Give Fellow Deep Tech Founders?

The quantum industry has been experiencing ups and downs and difficulties as quantum startups were started but weren’t providing solutions that would yield commercial value. That’s why the industry is still going through a quantum winter overall. But if you think you know how to bring quantum advantage to real applications, you can think of starting a business in quantum computing. There’s a lot of potential, and you can attract a lot of smart people, but you need to deliver a quantum advantage down the road.

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