论文标题
通过正则压缩双重分解加速化学的量子计算
Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization
论文作者
论文摘要
我们提出了正规化的压缩双重分解方法（RC-DF）方法，以经典计算分子汉密尔顿分子的压缩表示，以促进具有嘈杂的中间尺度（NISQ）和误差校正量子算法的有效仿真。我们发现，对于具有12至20 QUAT的小型系统，与截断的双重分解（DF）相比，所得的NISQ测量方案将测量库的数量减少了大约3倍，而射击计数则达到了三到六倍，而我们看到比Pauli组的数量级提高了。 We demonstrate the scalability of our approach by performing RC-DF on the CpdI species of cytochrome P450 with 58 orbitals and find that using the resulting compressed Hamiltonian cuts the run time of qubitization and truncated DF based error corrected algorithms almost in half and even outperforms the lambda parameters achievable with tensor hypercontraction (THC) while at the same time reducing the CCSD（T）通过数量级的能量误差启发式启发式。
We propose the regularized compressed double factorization (RC-DF) method to classically compute compressed representations of molecular Hamiltonians that enable efficient simulation with noisy intermediate scale (NISQ) and error corrected quantum algorithms. We find that already for small systems with 12 to 20 qubits, the resulting NISQ measurement scheme reduces the number of measurement bases by roughly a factor of three and the shot count to reach chemical accuracy by a factor of three to six compared to truncated double factorization (DF) and we see order of magnitude improvements over Pauli grouping schemes. We demonstrate the scalability of our approach by performing RC-DF on the CpdI species of cytochrome P450 with 58 orbitals and find that using the resulting compressed Hamiltonian cuts the run time of qubitization and truncated DF based error corrected algorithms almost in half and even outperforms the lambda parameters achievable with tensor hypercontraction (THC) while at the same time reducing the CCSD(T) energy error heuristic by an order of magnitude.
