Learn How Quantum Computing Could Revolutionize Chemistry

The consensus among many quantum computing practitioners1, 2 is that chemistry, specifically quantum chemistry, has great potential to be the first field to benefit from quantum computing, as the simulation of molecules can be more efficiently handled using quantum computing methods. This could accelerate materials innovation for large-scale industrial applications, such as catalyst design to facilitate chemical synthesis for more-efficient production of chemical products, the exploration of next-generation battery materials to help build sustainable energy systems, and the discovery of drug compounds for a healthier future for us all. However, it is unlikely that near-term quantum hardware will be able to simulate molecules that are sufficiently large and complex for industry applications. Therefore, reducing the size of these simulations is essential for advancing quantum computation in materials science and chemistry as a whole. Problem decomposition (PD) techniques have been developed for furthering this research.

Problem Decomposition

abstract image for problem decomposition Problem decomposition (PD) is a procedure that solves a complex computational problem by breaking it down into smaller parts that are easier to manage. It is well-suited for quantum chemistry simulations, where a complex molecular system can be broken down into subsystems using a classical algorithm (“classical”, that is, non-quantum). A quantum algorithm can then simulate the quantum nature of the subsystems. 1QBit is one of several companies developing quantum–classical hybrid problem decomposition techniques that are a promising first step toward solving quantum chemistry problems involving complex molecules using near-term quantum hardware. In recent work by 1QBit, the researchers explored various popular techniques within their hybrid framework, all of which performed well.3

The Chemistry Involved

The complex electronic structure of each atom in a quantum system must be taken into account when solving quantum chemistry problems, which quickly becomes intractable with increasing molecular size. Therefore, the efficient simulation of large molecules on quantum devices based on PD techniques used in quantum chemistry has been a long-needed strategy for performing electronic structure calculations. Electronic structure theory in quantum chemistry involves calculating quantum states of electrons, and the forces that exist between the electrons and the nucleus of each atom that make up a given molecule. The forces determine the energies, geometries, and the transitioning states between stable molecular structures. Performing electronic structure and nuclear dynamics calculations is a necessary step in studying the quantum motion of a system. image of servers Currently among the most computationally challenging problems, electronic structure calculations require ever-more-powerful computers as problem size increases. Researchers are increasingly looking toward a promising type of near-term quantum hardware—noisy intermediate-scale quantum (NISQ) devices—to solve such problems. These devices are called “noisy” due to the instability of their qubits. Fully realized quantum computing hinges on the development of more robust qubits (see the Building Better Qubits article.) In the interim, PD methods performed using NISQ devices are prevalent in quantum chemistry. A new PD method well-suited for NISQ devices is discussed below.

A Novel 1QBit–Dow Framework: The Frozen Natural Orbital Based Method of Increments

A popular type of PD-based approach used in quantum chemistry is the method of increments (MI). The method of increments is based on the many-body expansion of the electron correlation energy in terms of occupied molecular orbitals. The MI approach is used to systematically reduce the occupied orbital space of the molecular system, allowing for easier simulation of the system. A new framework has recently been proposed by 1QBit in collaboration with Dow that uses the frozen natural orbitals (FNO) algorithm to build on the MI approach.4 And, as with the MI approach, FNOs are also obtained using many-body perturbation theory. The FNO algorithm reduces the computing resources needed to perform chemistry simulations by truncating the virtual orbital space of a system. In this way, a new MI-FNO approach is constructed for the systematic reduction of both the occupied space and the virtual space in quantum chemistry simulations, allowing for the scaling up of electronic structure calculations. The MI-FNO framework is independent and transferable, not tied to any particular method of obtaining electron correlation energy. For instance, correlation energy can be computed using any conventional quantum chemistry approach, such as coupled-cluster or full configuration interaction. Alternatively, quantum algorithms such as the variational quantum eigensolver or phase estimation could also be used. For a more detailed discussion on these different conventional and quantum methods, please see the 1QBit paper, “Scaling Up Electronic Structure Calculations on Quantum Computers: The Frozen Natural Orbital Based Method of Increments”.4

How Effective is the New Approach?

quantum computing could revolutionize chemistry Using the small molecules BeH2, CH4, NH3, H2O, and HF as proof-of-concept examples, it has been demonstratedthat MI-FNO is a framework for systematically reducing the qubit requirements needed for simulating quantum systems. The efficacy of the framework has been investigated for larger molecules commonly used in realistic industrial applications using a qubit-count estimation on an industrially relevant, medium-sized catalyst molecule. Even by employing a modest truncation of the virtual space, the MI-FNO approach reduces the qubit requirement by a factor of almost one half. Reducing the qubit requirement can facilitate hardware experiments based on smaller, yet more realistic, chemistry problems, assisting in the characterization of NISQ devices. Moreover, reducing the qubit requirement can help scale up the size of molecular systems that can be simulated in quantum chemistry applications. Doing so could greatly enhance real-world computational chemistry studies to accelerate materials innovation for large-scale industrial applications for a healthier and more sustainable future.

References

1 A. J. McCaskey, Z. P. Parks, J. Jakowski, et al., “Quantum chemistry as a benchmark for near-term quantum computers”, npj Quantum Inf 5, 99 (2019).

2 J. Olson, Y. Cao, J. Romero, P. Johnson, P-L. Dallaire-Demers, N.Sawaya, P. Narang, I. Kivlichan, M. Wasielewski, and A. Aspuru-Guzik, “Quantum Information and Computation for Chemistry”, arXiv:1706.05413 (2016).

3 T. Yamazaki, S. Matsuura, A. Narimani, A. Saidmuradov, and A. Zaribafiyan, “Towards the Practical Application of Near-Term Quantum Computers in Quantum Chemistry Simulations: A Problem Decomposition Approach”, arXiv:1806.01305 (2018).

4 P. Verma, L. Huntington, M. Coons, Y. Kawashima, T. Yamazaki, and A. Zaribafiyan, “Scaling Up Electronic Structure Calculations on Quantum Computers: The Frozen Natural Orbital Based Method of Increments”, arXiv:2002.07901 (2020).

Other Blog Articles

From Chemistry to Finance: The Broad Scope of New Quantum Optimization Methods

Quantum computing research and experimentation continues to make progress towards addressing complex computational problems. These include modelling systems in nature as they relate to chemistry and physics. Beyond this, quantum computing research has also shown promise in tackling problems in logistics and finance.

Uncovering Hidden Patterns in Gold Prices

The Federal Reserve Bank of Kansas City’s 44th annual economic policy symposium (i.e., the Jackson Hole Economic Symposium) was held online this year on August 27th and 28th. Its potential effect on gold prices has been widely covered in the news, with a series of policy changes announced going forward. It is expected to contribute to further US dollar depreciation. Historically, financial instruments related to the US dollar have price moves around the event.

How Big Data Can Be Visualized Using Quantum Annealing

The human eye can perceive patterns and meaning in data based on its visual appearance, but it becomes difficult to perceive such patterns when the data is presented in a dense and unorganized manner. The Big Data Visualization Toolkit (BiDViT) enables the study and display of enormous datasets by following the approach described above, that is, reducing them into smaller, yet still sufficiently large, datasets that accurately represent the original dataset, allowing the resulting clustered set to be clearly viewed and analyzed by the human eye.

Quantum Computing and More-Effective Drugs

The need to create new medicines faces major challenges due to the increasing costs of drug development and the high failure rate of drug candidates. These challenges are faced due to the laborious and expensive processes involved. For example, in order to develop an...