Qubit Pharmaceuticals: Shaping the Future of Hybrid Quantum Algorithms for Drug Discovery

Quantum computing holds many promises for solving complex computational problems—and these promises may first come true in understanding the world of atoms and molecules, which are, at heart, quantum systems. 

Quantum computers need to be more mature for use in production. Still, inspiration from quantum mechanics can help develop hybrid quantum-classical algorithms that combine the best of both worlds and tune the level of execution on quantum hardware. Such developments can have practical relevance, for instance, in drug development in the pharmaceutical industry.

Qubit Pharmaceuticals was founded in 2020 by Louis Lagardère, Matthieu Montes, Jean-Philip Piquemal, Jay Ponder, and Pengyu Ren and joined by Robert Marino as the CEO to develop quantum physics-inspired algorithms and reach unparalleled accuracy and precision in drug discovery and design. The company raised €16M in seed funding from Quantonation, XAnge, Omnes Capital, and Octave Klaba, bringing its total funding to over €23M.

Learn more about the future of hybrid quantum algorithms for drug discovery from our interview with the CEO, Robert Marino: 

Why Did You Start Qubit Pharmaceuticals?

Before Qubit Pharmaceuticals, I co-founded and managed Deeptech Founders, a six-month accelerator program in France that helped entrepreneurial scientists turn their research into a deep tech startup. I got in touch with the founders of Qubit Pharmaceuticals through one of their early investors, Quantonation, who really liked the technology but felt that the startup needed a CEO.

We’ve built a multi-disciplinary team from math and chemistry to bioinformatics and quantum physics, where experts from different fields greatly respect each other. This strong team allows us to address very complex problems, such as developing new drugs. 

How Can Quantum Computing Aid Drug Discovery?

One of the main challenges of drug discovery is simulating the interaction between molecules to, for instance, predict a drug’s potency. Accurately simulating the behavior of a single molecule is tricky since the number of possible interactions with other molecules skyrockets as the overall number of molecules increases. 

Computer-aided drug discovery has been around for about 40 years. However, due to limited computational powers, the first software packages had to simplify the physics and depended a lot on experimental validation—which is, to this day, a lot of trial and error. As the computational power of computers increases, and as physics models become more and more complex, we’ll be able to run more accurate simulations that not only spare us a lot of experimental testing but also allow us to develop entirely new drugs. Simplistic models haven’t previously tapped a vast chunk of the chemical search space. 

Quantum computing is still very early, and quantum computers have yet to demonstrate a practical advantage over supercomputers. We don’t use it in production, but we still prepare for it by filing IP, developing quantum algorithms, and ensuring we can move to quantum hardware once it becomes useful. It may help us, in particular, 1) improve accuracy, 2) get to a solution faster, or 3) get as fast and as accurate to a solution but with a smaller carbon footprint since it runs more energy efficiently. Since we’re a business, we need to test and optimize for what makes sense. 

Meanwhile, the heavy lifting is done by GPUs and quantum-inspired algorithms that run classically but incorporate insights from quantum mechanics. In particular, we have developed a novel algorithm for variational quantum eigensolvers (VQE), which is a hybrid quantum-classical algorithm. It can run in parts on a classical computer and in parts on a quantum computer, and we can tune how much quantum we want. 

There aren’t hundreds of useful quantum algorithms already, and it takes smart, multidisciplinary research teams to develop a new one for your particular problem. Bring everyone in the same room and figure out how to solve it—and make sure that it scales to many thousands, even millions of qubits. No one cares about an algorithm for only ten qubits. 

Many startups focus exclusively on fault-tolerant quantum computing (FTQC), but no one knows when we’ll get there, and we might be retired before quantum computers reach fault tolerance. So, our approach is to figure out how to use what we have—noisy, intermediate-scale quantum (NISQ) computers—and start with hybrid computing today. 

Our business model is to find a new molecule that addresses a so-called target—typically a protein—that causes a disease and is known from academia or pharma research. We don’t identify new targets but instead, look for molecules addressing a known target and then work with contract research organizations (CROs) for experimental validation and license the molecule for the production of a drug.

How Did You Evaluate Your Startup Idea?

Pharma research is fairly standardized, so it’s easy to launch a project for a target. The challenge is to know which target to address. Therefore, we need to evaluate many factors, such as a disease’s epidemiology, its impact on quality of life, or the availability of other treatments. We also use typical financial instruments like discounted cash flow, net present value, and cost of goods sold (cogs) to estimate a drug’s financial viability.

What Advice Would You Give Fellow Deep Tech Founders?

Especially in quantum computing, beware of PR announcements. That someone built a quantum machine doesn’t mean it’s available. Quantum computers are scarce, and demand is high, so partner early with hardware vendors and make it worth it for them to spend their time with you. Make sure your proposal provides value to both sides. You’re in for the long game and need to establish trust. 

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