QCSim, Quantum Computation Simulator

Paul E. Black, Andrew W. Lane

A simulator for quantum information systems cannot be both general, that is, easily used for every possible system, and efficient. Therefore, some systems will have aspects which can only be simulated by cunning modeling. On the other hand, a simulation may conveniently do extra-systemic processing that would be impractical in a real system. We illustrate with examples from our quantum computing simulator, QCSim. We model the [3,1] Hamming code in the presence of random bit flip or generalized amplitude damping noise, and calculate the expected result in one simulation run, as opposed to, say, a Monte Carlo simulation, and keep the original state to compute the chance of successful transmission, too. We also model the BB84 protocol with eavesdropping and random choice of basis and compute the chance of information received faithfully. Finally, we present our simulation of teleportation as an example of the trade-off between complexity of the simulation model and complexity of simulation inputs and as an example of modeling measurements and classical bits.

Xi Deng, Yuxin Deng

Salvador Elías Venegas-Andraca, José Luis Gómez-Muñoz, Marco O. Lanzagorta +1 more

George F. Viamontes, Héctor J. García, Igor L. Markov +3 more

THE UNIVERSITY OF MICHIGANQuIDDPro is a fast, scalable, and easy-to-use computational interface for generic quantum circuit simulation. It supports state vectors, density matrices, and related operations using the Quantum Information Decision Diagram (QuIDD) data structure [1, 2, 4]. Software packages including Matlab, Octave, QCSim [5], and libquantum [6], have also been used to simulate quantum circuits. However, unlike these packages, QuIDDPro does not always suffer from the exponential blow-up in size of the matrices required to simulate quantum circuits. As a result, we have found that QuIDDPro is much faster and uses much less memory as compared to other generic simulation methods for some useful circuits with much more than 10 qubits [1, 2, 4].

George F. Viamontes, Igor L. Markov, John P. Hayes

Quantum-mechanical phenomena are playing an increasing role in information processing, as transistor sizes approach the nanometer level, and quantum circuits and data encoding methods appear in the securest forms of communication. Simulating such phenomena efficiently is exceedingly difficult because of the vast size of the quantum state space involved. A major complication is caused by errors (noise) due to unwanted interactions between the quantum states and the environment. Consequently, simulating quantum circuits and their associated errors using the density matrix representation is potentially significant in many applications, but is well beyond the computational abilities of most classical simulation techniques in both time and memory resources. The size of a density matrix grows exponentially with the number of qubits simulated, rendering array-based simulation techniques that explicitly store the density matrix intractable. In this work, we propose a new technique aimed at efficiently simulating quantum circuits that are subject to errors. In particular, we describe new graph-based algorithms implemented in the simulator QuIDDPro/D. While previously reported graph-based simulators operate in terms of the state-vector representation, these new algorithms use the density matrix representation. To gauge the improvements offered by QuIDDPro/D, we compare its simulation performance with an optimized array-based simulator called QCSim. Empirical results, generated by both simulators on a set of quantum circuit benchmarks involving error correction, reversible logic, communication, and quantum search, show that the graph-based approach far outperforms the array-based approach.

铁 邹