Abstract
Quantum computing leverages the quantum resources of superposition and entanglement to efficiently solve computational problems considered intractable for classical computers. Examples include calculating molecular and nuclear structure, simulating strongly interacting electron systems, and modeling aspects of material function. While substantial theoretical advances have been made in mapping these problems to quantum algorithms, there remains a large gap between the resource requirements for solving such problems and the capabilities of currently available quantum hardware. Bridging this gap will require a co-design approach, where the expression of algorithms is developed in conjunction with the hardware itself to optimize execution. Here we describe an extensible co-design framework for solving chemistry problems on a trapped-ion quantum computer and apply it to estimating the ground-state energy of the water molecule using the variational quantum eigensolver (VQE) method. The controllability of the trapped-ion quantum computer enables robust energy estimates using the prepared VQE ansatz states. The systematic and statistical errors are comparable to the chemical accuracy, which is the target threshold necessary for predicting the rates of chemical reaction dynamics, without resorting to any error mitigation techniques based on Richardson extrapolation.
Highlights
Quantum computation has attracted much attention for its potential to solve certain computational problems that are difficult to tackle with classical computers
Given a general unitary coupled-cluster (UCC) ansatz state, interaction terms take the form of a two-electron interaction θpqrscypcyqcrcs
Since the indices p; q; r; s vary over the complete set of molecular states, implementing this interaction requires entangling gates between arbitrary pairs of qubits in the system
Summary
Quantum computation has attracted much attention for its potential to solve certain computational problems that are difficult to tackle with classical computers. Integer factorization[1], unsorted database search[2], and the simulation of quantum systems[3] admit quantum algorithms that outperform the bestknown classical algorithms given a sufficiently large problem size. These algorithms require substantial quantum resources to achieve a practical advantage over classical techniques, limiting their near-term utility on noisy intermediate-scale quantum (NISQ) devices[4] that are severely limited in the number of gates they can perform before errors dominate the output. Any useful quantum computation on a NISQ device will require further advances in hardware performance, as well as advances in algorithmic design. Achieving chemical accuracy would allow computational methods to replace costly experimental procedures in chemical and materials engineering, augmenting these fields to accelerate the pace of discovery
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
