Yudong Cao has a background in Mechanical Engineering and Computer Science. He obtained his PhD in Computer Science from Purdue University in 2016, and after graduation, he joined the Aspuru-Guzik group at Harvard University. The main focus of Yudong’s work at Harvard was on developing and deploying algorithms for noisy intermediate-scale quantum computing (QC) devices. This work has served as the foundation for the applications and solutions Zapata Computing can offer their enterprise clients today. Yudong continues to work on developing quantum and quantum-inspired algorithms for near-term applications.
What kind of immediate and future applications will chemical companies have for quantum computing?
In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in the simulation of quantum systems. Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease.
The advent of quantum computation brings new pathways to navigate this challenging, complex landscape. By manipulating quantum states of matter and taking advantage of unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as analyzing the electronic structure of molecules.
What would you currently estimate is the size of the QC market for chemical applications?
There is no easy way to calculate this and it’s difficult to estimate. We’ve seen reports approaching $100M for total QC spend in 2025, and assume approx. $20-25M in software spend. Current spend is likely in the low double-digit millions.
Is there a particular space where QC could deliver the most value for companies in the chemical space?
It’s difficult to say. Compared with machine learning and optimization, simulation and modeling of quantum systems have a generally stronger scientific case for exponential algorithmic advantage over existing classical methods. On the other hand, the relatively incremental and heuristic algorithmic improvement due to quantum-enhanced machine learning and optimization is amplified by the business value that these use cases generate.
What kind of problems is quantum computing ready to tackle in the chemical industry?
The nearest term opportunities are in optimization and machine learning. Examples across the value chain (not just R&D) include:
- Generative adversarial networks to generate novel chemicals
- Process optimization in chemical reaction networks for maximizing yield
- Manufacturing optimization
- Logistic network optimization
- Advertising/marketing optimization
Further out problems include simulation & modeling problems such as:
- Homogeneous and heterogeneous catalysis modeling using electronic structure calculations
- Singlet-triplet transition energy prediction for OLED molecules
- Quantum-enhanced force-field methods for chemical dynamics simulation
Do you think there are technological and economic challenges that need to be addressed to integrate QC into the chemical industry processes?
Technological challenges: It is one thing to develop quantum algorithms in an R&D environment where benchmarks are carried out on a curated set of problem instances, and an entirely different issue if one would like to deploy an end-to-end quantum solution in a production environment. In the latter case, one needs to grapple with the often heterogeneous legacy enterprise IT systems, along with other issues related to operating systems, networking, database, regulation, and so on. This operationalization problem should not be overlooked if we were to integrate QC into industry processes.
Economic challenges: One key challenge is the “build or buy” decision-making. Some of the forward-looking enterprises in the chemistry space are actively investing resources to build an internal team of quantum computing specialists, while others partner with companies such as Zapata for developing their quantum computing programs. The barrier of entry for doing value-generating work in QC remains high, and any company contemplating entering this space will need to think carefully about the “build or buy” issue.
How are you integrating quantum technologies with the classical computing tools to achieve your goals in the chemical industry?
We address the quantum-classical integration issue from two perspectives: science and engineering.
From the science perspective, we want to transform a computational problem such as electronic structure calculation in a way such that the parts of the problem that can be efficiently addressed classically are delegated to classical computing resources, while the quantum computers are only exposed to parts of the problem that capture its inherent complexity. In the case of electronic structure calculation, this “inherently complex” part is the ability to generate and consistently measure strongly correlated electronic wavefunctions.
From the engineering perspective, we adopt the latest cloud technologies such as containerization and workflow management to make sure that the various parts of our quantum solution built using a diverse collection of tools and quantum programming frameworks can be interoperable, modular, and extensible.
What considerations should a chemical company keep in mind before starting to work with QC?
In the next several years, we will see quantum-enabled solutions delivered as production systems. Then, when increasingly capable quantum devices come online, having deployment architectures in place that can utilize these machines will be critical.
The road to quantum advantage requires the right use case, algorithm, and access to quantum hardware; but moreover, what is key to succeed is having proper, supportive infrastructure and performant classical-quantum approach. A quantum computing solution, delivered in production, only creates business value when it works with the complex, fragmented architectures of data and compute in massive organizations.
That means companies interested in pursuing quantum advantages should figure out how to implement/deploy a quantum solution within the organization’s complex, fragmented architectures of data and compute – they cannot afford to solely focus on use case exploration while they wait for the quantum devices to mature.
And again – we advise pursuing nearer-term use cases in ML and optimization to create business impact sooner and get to production. Then customers will be positioned to swap in hardware as it matures. For an effective partnership, we would bring quantum expertise but want to collaborate with domain experts, end users, and DevOps who will implement solutions.
How are quantum computing projects in chemistry generally financed?
For quantum chemical calculations using purely classical methods, there is already a mature commercial software ecosystem with players such as Schrodinger. Significant funding may be directed to software licensing. For quantum chemical calculations using quantum devices, this is still rather nascent and the funding is concentrated on NRE projects. There are some libraries such as OpenFermion and other proprietary tools built by companies targeting specific use cases. Software licensing remains rare if any at this point. But we expect that to change in the coming years.
How do you establish links between the QC startups to discuss collaborations?
Hard to say, this can be different each time. We are currently partnered with almost all quantum hardware providers – from pureplay hardware providers like IonQ and Honeywell to hybrid players like IBM and cloud providers like AWS. Initiative typically sits with the partner (on the software or hardware side) that has a customer with a problem to solve. For example, we have customers who have a solution that we are benchmarking across quantum hardware backends (including classical simulators) on our platform, Orquestra. This unlocks progress for industry and our entire ecosystem.
