Quantum Computing Achieves Chemical Accuracy with 24 Qubit Molecular Ground State Preparation

Preparing the ground state of molecules represents a fundamental challenge in computational chemistry, and researchers are continually seeking more efficient methods to tackle this problem. Zekun He, Dominika Zgid from the University of Michigan and University of Warsaw, A. F. Kemper, and J. K. Freericks have now developed a new approach, termed the classical reservoir method, which offers a promising pathway towards achieving this goal on emerging quantum hardware. This technique distinguishes itself by requiring only interactions between nearest-neighbouring quantum bits, simplifying the computational demands and allowing exploration of previously inaccessible parameter spaces. The team demonstrates chemical accuracy across a range of molecules, including hydrogen chains, nitrogen, oxygen, and water, and importantly, achieves these results with significantly reduced computational complexity, potentially paving the way for ground state calculations on larger, more complex systems.

Variational Quantum Eigensolver For Chemical Accuracy

The research focuses on achieving chemical accuracy in quantum chemistry calculations using a variational quantum eigensolver (VQE) approach, balancing the expressiveness of the quantum circuit with computational cost. The team explores methods to efficiently train ansatz parameters and achieve reliable results, even in strongly correlated systems where traditional classical methods struggle. Chemical accuracy, a target level of precision, is defined as an error of less than 1 kcal/mol. The method involves a hybrid quantum-classical algorithm, using a quantum computer to prepare a trial wave function and a classical computer to optimize its parameters.

Two initialization strategies were tested, random initialization with multiple trials and constant seed initialization with a short optimization budget. An annealing protocol, sweeping across molecular geometries, stabilizes the energy profile and avoids collapse to excited states, particularly in strongly correlated regions. Results demonstrate that chemical accuracy is achievable with a relatively shallow ansatz, with the number of layers close to the number of spatial orbitals. The research shows that the proposed techniques maintain accuracy even when classical coupled cluster methods become unreliable in strongly correlated regimes. The team’s approach offers a valuable overview of the quantum resources needed for different molecules, paving the way for quantum computers to contribute to drug discovery, materials science, and fundamental chemical research.

Low-Cost Ground State Preparation with Quantum Hardware

Scientists have developed a new approach to ground state preparation for electronic structure calculations, employing a classical reservoir method tailored for near-term quantum hardware. This method requires only nearest-neighbor interactions on a square-lattice connected machine, offering a low-cost alternative to traditional techniques. By utilizing localized molecular orbitals, the team studied previously unexplored regions of the parameter space and achieved chemical accuracy across diverse systems and bond lengths. Experiments demonstrate comparable or better accuracy than state-of-the-art methods, while significantly reducing the number of CNOT gates required.

This efficiency stems from avoiding computationally expensive explicit double excitations and long-range interactions, relying instead on a classical reservoir approach. Benchmarking on molecules including hydrogen chains, nitrogen, oxygen, carbon monoxide, beryllium hydride, and water, with the water calculation corresponding to an effective 24 qubit calculation, confirms the algorithm’s effectiveness. Measurements show the algorithm can reduce the L1 norm of the electronic Hamiltonian by as much as 76% for larger molecules, lowering measurement costs in quantum circuits. The team initialized the system with a total-spin eigenstate and applied classical reservoir operators optimized by gradient descent, successfully driving the energy towards the ground state. This cooling-inspired approach delivers a new pathway for variational quantum algorithms, potentially unlocking more efficient preparation paths.

Locality Improves Quantum Ground State Preparation

This research demonstrates a new approach to preparing ground states for quantum systems, achieving chemical accuracy with reduced computational cost. The team developed a classical reservoir method, an algorithm designed for current quantum hardware, that relies only on nearest-neighbor interactions within a square-lattice structure. Unlike conventional techniques rooted in classical Hartree-Fock theory, this method utilizes localized molecular orbitals, enabling exploration of previously inaccessible regions of the computational parameter space. Results show that enforcing locality in the quantum algorithm lowers circuit complexity and depth, improving accuracy across a range of molecules, including hydrogen chains, nitrogen, oxygen, carbon monoxide, beryllium hydride, and water. The method maintains high fidelity even with realistic limitations on quantum operations, suggesting its potential as an initial state for future fault-tolerant quantum computers. The authors highlight the potential for achieving chemical accuracy without extensive variational optimization on more advanced hardware, suggesting future work should focus on developing quantum-native algorithms for preparing ground states for complex quantum systems.