Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer (2405.05068v2)

Abstract: A universal quantum computer can be used as a simulator capable of predicting properties of diverse quantum systems. Electronic structure problems in chemistry offer practical use cases around the hundred-qubit mark. This appears promising since current quantum processors have reached these sizes. However, mapping these use cases onto quantum computers yields deep circuits, and for pre-fault-tolerant quantum processors, the large number of measurements to estimate molecular energies leads to prohibitive runtimes. As a result, realistic chemistry is out of reach of current quantum computers in isolation. A natural question is whether classical distributed computation can relieve quantum processors from parsing all but a core, intrinsically quantum component of a chemistry workflow. Here, we incorporate quantum computations of chemistry in a quantum-centric supercomputing architecture, using up to 6400 nodes of the supercomputer Fugaku to assist a quantum computer with a Heron superconducting processor. We simulate the N$_2$ triple bond breaking in a correlation-consistent cc-pVDZ basis set, and the active-space electronic structure of [2Fe-2S] and [4Fe-4S] clusters, using 58, 45 and 77 qubits respectively, with quantum circuits of up to 10570 (3590 2-qubit) quantum gates. We obtain our results using a class of quantum circuits that approximates molecular eigenstates, and a hybrid estimator. The estimator processes quantum samples, produces upper bounds to the ground-state energy and wavefunctions supported on a polynomial number of states. This guarantees an unconditional quality metric for quantum advantage, certifiable by classical computers at polynomial cost. For current error rates, our results show that classical distributed computing coupled to quantum computers can produce good approximate solutions for practical problems beyond sizes amenable to exact diagonalization.

• The paper demonstrates a hybrid estimator approach that integrates quantum circuits with classical post-processing to approximate ground-state energies of complex molecules.
• It employs superconducting quantum processors combined with classical high-performance computing to execute quantum circuits with up to 10,570 gates for molecules like N₂ and iron-sulfur clusters.
• The authors show that enhanced configuration recovery and noise mitigation pave the way for overcoming traditional limits in electronic structure simulations.

Overview of "Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer"

The paper "Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer" presents a novel approach to tackling complex electronic structure problems in quantum chemistry using a hybrid quantum-classical computational architecture. The authors demonstrate the potential of leveraging current quantum processors in combination with classical high-performance computing resources to solve problems in quantum chemistry that are presently intractable for quantum computers operated in isolation.

Methodology

The core advancement described in the paper involves the integration of classical distributed computation and quantum computing within a quantum-centric supercomputing architecture. The authors utilize a superconducting quantum processor (Heron) and the Fugaku supercomputer to simulate chemical systems, including the nitrogen molecule (N$_2$) and iron-sulfur clusters ([2Fe-2S] and [4Fe-4S]). The approach entails the use of a hybrid estimator that processes quantum measurements to produce upper bounds on the ground-state energies and wavefunctions, with the calculations being verified on classical computers at polynomial cost.

The methodology is segmented into the execution of quantum circuits, which are rigorously designed to approximate molecular eigenstates, followed by classical post-processing. The post-processing step involves configuration recovery, which utilizes averaged orbital occupancy statistics to improve estimates of molecular electronic configurations, enhancing the quality of the quantum data despite the presence of noise.

Key Findings and Results

The authors provide compelling evidence that their hybrid approach can effectively mitigate the limitations of current quantum processors, such as noise and decoherence, by offloading substantial computational tasks to classical nodes. This is exemplified through the simulation of the N$_2$ molecule and iron-sulfur clusters, using quantum circuits with up to 10570 quantum gates and up to 6400 classical nodes for support. The simulations demonstrate that the hybrid estimator can return good approximate solutions for systems beyond the reach of exact diagonalization techniques, typically limited to smaller bases (e.g., 22 electrons in 22 orbitals).

Theoretical and Practical Implications

The technique outlined in the paper holds significant promise for advancing quantum chemistry simulations — a critical area for the development of new materials and drugs. The collaborative approach between classical and quantum computations could open new avenues for achieving quantum advantage, especially in scenarios where the ground state can be represented as a sparse wavefunction in the computational basis. The authors note that by sampling effectively with quantum circuits, challenging classical computation problems may be more feasibly addressed than by using classical heuristics alone.

Future Prospects

The authors envisage that as error rates in quantum devices improve, and classical processing requirements decrease correspondingly, the efficacy and scope of this quantum-centric computing strategy will expand significantly. Future work may focus on the optimization of quantum circuit parameters and the enhancement of configuration recovery methods for broader classes of problems. Such developments would solidify the framework's role in bridging current and fault-tolerant quantum computing, thereby serving as a stepping stone toward achieving quantum advantage in practical applications. The potential inclusion of more intricate physical models could further enhance resource efficiency and accuracy in simulating complex many-body systems.

In conclusion, the paper presents a compelling case for the integration of quantum and classical computations in solving high-impact problems in quantum chemistry, highlighting the ongoing collaboration between advances in both quantum algorithms and supercomputing architectures.