Quantum Algorithm Achieves Sub-Percent Accuracy for 24-Qubit Many-Body Systems
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Understanding the behaviour of complex quantum systems remains a significant challenge in modern physics, and researchers continually seek more efficient methods for simulating these systems on quantum computers. Anutosh Biswas, Sayan Ghosh, and Ritajit Majumdar, alongside colleagues from S. N.
Bose National Centre for Basic Sciences and IBM Quantum, present a new basis adaptive algorithm designed to accurately determine the ground-state properties of many-body systems. This method overcomes limitations found in existing approaches, such as Variational Eigensolver and Phase Estimation, by employing shallow quantum circuits and leveraging the symmetries inherent in the system being studied. Benchmarking the algorithm on a spin-1/2 XXZ chain using up to 24 qubits, the team achieves remarkably high accuracy, with errors below one percent, and importantly, demonstrates superior performance compared to existing techniques like Sampling Krylov Diagonalization, paving the way for more effective simulations of complex materials and quantum phenomena on near-term hardware. The method addresses limitations inherent in algorithms such as the Variational Quantum Eigensolver and Quantum Phase Estimation by employing shallow, Trotterized circuits for short real-time evolution on a quantum processor. This approach enables the sampling of a basis set, which is then rigorously filtered using the symmetries of the Hamiltonian, including conservation of total Sz and lattice reflection symmetry, before classical diagonalization within the reduced Hilbert space. Researchers implemented the algorithm on IBM’s Heron processor, investigating the spin-1/2 XXZ chain with systems up to 24 qubits and achieving sub-percent accuracy in ground-state energies across various anisotropy regimes. This significantly outperforms the Sampling Krylov Quantum Diagonalization method, achieving a substantially lower energy error for comparable reduced-space dimensions. This work establishes symmetry-filtered, real-time sampling as a robust and efficient path for studying correlated quantum systems on present-day hardware, paving the way for more accurate simulations of complex materials and phenomena.
Compact Basis Sets for Quantum Ground State Problems Scientists have developed a new hybrid quantum-classical algorithm for determining the ground-state properties of many-body quantum systems. The method addresses limitations found in existing algorithms by employing shallow quantum circuits combined with symmetry filtering and classical diagonalization. By utilizing short real-time evolution on a quantum processor and carefully selecting a relevant basis, the method efficiently explores the complex Hilbert space required to model these systems. Benchmarking the algorithm on the spin-1/2 XXZ chain, with up to 24 qubits on the Heron processor, demonstrates sub-percent accuracy in calculating ground-state energies across a range of conditions. This symmetry filtering significantly improves efficiency and accuracy, and crucially, the results outperform the Sampling Krylov Quantum Diagonalization method, achieving a significantly lower energy error for comparable computational effort. This validates symmetry-filtered, real-time quantum sampling as a robust and efficient approach for high-accuracy ground-state simulations on current near-term quantum hardware, paving the way for more accurate simulations of complex materials and phenomena. Sub-Percent Accuracy in Ground-State Energy Determination Scientists have achieved sub-percent accuracy in determining the ground-state energies of many-body systems using a new hybrid quantum-classical algorithm. The method addresses limitations found in existing algorithms like Variational Eigensolver and Phase Estimation by utilizing shallow, time-evolved circuits on a quantum processor.
The team implemented this algorithm on IBM’s Heron processor, successfully benchmarking it on the spin-1/2 XXZ chain with systems up to 24 qubits. Experiments demonstrate that the algorithm consistently achieves ground-state energy accuracies of less than one percent across a broad range of anisotropy values. The method involves sampling a basis of states, filtering these states based on the symmetries of the Hamiltonian, and then classically diagonalizing the resulting, reduced Hilbert space. This symmetry filtering significantly improves efficiency and accuracy. This work establishes symmetry-filtered, real-time sampling as a robust and efficient pathway for studying correlated quantum systems on current near-term quantum hardware, paving the way for more accurate simulations of complex materials and phenomena. Symmetry-Filtered Diagonalization Improves Ground State Calculations This research presents a new hybrid quantum-classical algorithm for determining the ground-state properties of many-body quantum systems. The method addresses limitations found in existing methods by employing shallow quantum circuits combined with symmetry filtering and classical diagonalization. By utilizing short real-time evolution on a quantum processor and carefully selecting a relevant basis, the method efficiently explores the complex Hilbert space required to model these systems. Benchmarking the algorithm on the spin-1/2 XXZ chain, with up to 24 qubits on the Heron processor, demonstrates sub-percent accuracy in calculating ground-state energies across a range of conditions. Crucially, the results outperform the Sampling Krylov Quantum Diagonalization method, achieving a significantly lower energy error for comparable computational effort. This validates symmetry-filtered, real-time quantum sampling as a robust and efficient approach for high-accuracy ground-state simulations on current near-term quantum hardware. 👉 More information 🗞 Basis Adaptive Algorithm for Quantum Many-Body Systems on Quantum Computers 🧠 ArXiv: https://arxiv.org/abs/2512.12753 Tags: