Quantum Annealing Computes Molecular Ground State Energy with 2.5 Times More Connectivity and 0.120 Precision
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Determining the ground-state energy of molecules represents a fundamental challenge in computational chemistry, with traditional methods quickly becoming impractical as molecular size increases. Stefano Bruni and Enrico Prati, both from Universit`a degli Studi di Milano, alongside their colleagues, now demonstrate a significant advance in tackling this problem using quantum annealing. Their work maps molecular energy calculations onto a form suitable for quantum computers, offering a potentially cheaper and more scalable approach. By employing an improved mapping technique and leveraging the capabilities of modern quantum hardware, the team achieves a more than doubled probability of finding accurate solutions and extends the size of molecules that can be realistically simulated, solving problems with nearly 2. 5times more qubits than previously reported, and improving results by two orders of magnitude, representing tangible progress towards practical applications of quantum annealing in the current era of noisy intermediate-scale quantum technology.
Water Molecule Simulation Benchmarks D-Wave Annealers This study presents a comprehensive evaluation of the XBK method, a quantum algorithm for estimating molecular ground-state energy, when applied to the water molecule. Researchers aimed to assess the performance of D-Wave’s Advantage 1 and Advantage 2 quantum annealers in solving this complex quantum chemistry problem, achieving Hartree-Fock level accuracy, a key milestone in quantum simulation.
The team also explored advanced annealing strategies to improve both the accuracy and scalability of the method. The Advantage 2 annealer consistently achieved Hartree-Fock level energies for the water molecule within a restricted active space, representing a significant improvement over previous benchmarks. This was enabled by enhanced qubit connectivity, allowing for shorter chain lengths and denser embeddings. This allowed researchers to solve problems with approximately 130% more degrees of freedom compared to previous studies. Advanced annealing strategies, particularly reverse annealing initialized from standard samples, proved highly effective in refining solutions, improving accuracy by up to two orders of magnitude, reaching an energy difference of 0. 120 Hartree relative to Hartree-Fock. The combination of improved hardware and advanced annealing strategies demonstrates the potential for scaling quantum annealing to larger molecular systems and more complex quantum chemical problems. The XBK method maps the electronic structure problem onto the D-Wave quantum annealer. Researchers focused on the water molecule, using a restricted active space to simplify the calculation while capturing essential electronic correlations. The study utilized D-Wave’s Advantage 1 and Advantage 2 quantum annealers, focusing on minimizing chain length and maximizing embedding density. Reverse annealing starts with a solution obtained from standard annealing and then refines it by reversing the annealing process. Quantum annealing is a quantum computing technique used to find the minimum energy state of a system, corresponding to the solution of an optimization problem. Electronic structure calculations determine the electronic properties of molecules, such as their energy, shape, and reactivity. Hartree-Fock theory is a fundamental method in quantum chemistry used to approximate the electronic structure of molecules. An active space is a limited set of electrons and orbitals used in electronic structure calculations to reduce computational cost. Embedding is the process of mapping a problem onto the qubits of a quantum computer. QUBO, or Quadratic Unconstrained Binary Optimization, is a mathematical formulation used to represent optimization problems on quantum annealers. This study represents a significant step forward in applying quantum computing to quantum chemistry, with potential implications for drug discovery and materials science. While scalability remains a challenge, the results demonstrate progress toward practical applications of quantum annealing. Further research is needed to develop more efficient quantum algorithms and embedding techniques, alongside continued improvements in quantum hardware, such as increasing the number of qubits and reducing noise.
Mapping Molecular Energy to Quantum Annealers Scientists tackled the complex challenge of calculating molecular ground-state energy by harnessing the power of quantum annealing, offering improved computational scaling compared to traditional methods. The study pioneered a method to map molecular Hamiltonians onto qubits, framing the problem as a discrete optimization task suitable for quantum annealing hardware. Researchers employed the Xian-Bias-Kas (XBK) method to translate the molecular ground-state problem into an Ising Hamiltonian.
The team specifically investigated the ground state of the water molecule, leveraging the enhanced connectivity and reduced embedding chains of the D-Wave Advantage 2 architecture. This allowed for a significant increase in the complexity of the problems addressed, enabling solutions that utilized nearly 2. 5times more physically embedded qubits than previously reported. Advanced annealing strategies, including reverse annealing and paused schedules, were implemented to refine the search for optimal solutions and improve accuracy. The results demonstrate a more than doubled probability of achieving Hartree-Fock-level solutions compared to prior work, with an energy difference of only 0. 120 Hartree relative to Hartree-Fock, representing a substantial improvement in accuracy by two orders of magnitude. These advancements signify tangible progress toward practical applications of quantum annealing in the current era of Noisy Intermediate-Scale Quantum (NISQ) technology.
Quantum Annealing Accurately Calculates Molecular Ground States Scientists have achieved a significant breakthrough in calculating molecular ground-state energy using quantum annealing, overcoming limitations inherent in conventional electronic structure methods. The research team successfully mapped the ground-state problem onto an Ising Hamiltonian using the Xian-Bias-Kas (XBK) method and solved it on a state-of-the-art quantum annealing processor, demonstrating enhanced performance compared to previous generations of hardware. Experiments revealed a more than doubled probability of achieving Hartree-Fock-level solutions. The study extended Hartree-Fock-level accuracy to significantly larger problem instances, enabling solutions that utilized nearly 2. 5times more physically embedded qubits than previously reported. This advancement was achieved by leveraging enhanced qubit connectivity and shorter embedding chains within the quantum processor, allowing for more complex molecular systems to be modeled. Furthermore, the implementation of advanced annealing strategies, including reverse annealing and paused schedules, improved annealing results by two orders of magnitude. Measurements confirm an energy difference of 0. 120 Hartree relative to Hartree-Fock, demonstrating a substantial reduction in error and increased precision. The new architecture was benchmarked against its predecessor, revealing a 20% reduction in QPU readout times and higher effective qubit connectivity. By employing these advanced techniques, the team achieved solutions with 50% more logical qubits and nearly 150% more physical qubits than previously possible, pushing the boundaries of what current quantum annealers can resolve. 👉 More information 🗞 Computing the molecular ground state energy in a restricted active space using quantum annealing 🧠 ArXiv: https://arxiv.org/abs/2512.11757 Tags: