Just Like the Real Thing: Fast Weak Simulation of Quantum Computation

Tools for Quantum Computing Based on Decision Diagrams

“…Measuring the resulting state, i.e., sampling from the corresponding decision diagram, can be eiciently conducted by a randomized single-path traversal of the decision diagram [24]. At each node, the squared magnitude of the left (right) successor gives the probability of the qubit associated to the node being |0⟩ (|1⟩), while the probability of an individual basis state is the product of all probabilities along the path.…”

“…• advanced techniques such as emulation [55], which enable signiicant speedups for certain quantum algorithms, • weak simulation [24], which more faithfully mimics a physical quantum computer, and • approximating simulation [23,49], which enables a inely controlled trade-of between accuracy and runtime. • noise-aware simulation [21,22], which allows to classically simulate the efects of noise on the execution of a quantum circuit.…”

“…Non-unitary operations such as measurements require separate handling in the program. Other paradigms such as approximating simulation [23,49], weak simulation [24], noise-aware simulation [22], and hybrid Schrodinger-Feynman simulation [8] are also supported and easy to extend.…”

“…Due to their success in the past, the use of decision diagrams has also been proposed in the domain of quantum computing [26, 30, 35, 41ś43, 50]. In particular for design tasks such as simulation [24,41,42,53], synthesis [1,34,39,52], and veriication [13,25,38,43] of quantum circuits, they found great interest recently.…”

“…Normalization can be performed by, e.g., dividing the weight of the outgoing edges of a node by the norm of the vector containing both edge weights and multiplying this factor to the incoming edgesÐallowing for eicient sampling from the resulting decision diagram[24].…”