Speed of computation and simulation

Minoru Fujishima, Keiji Saito, Masafumi Onouchi +1 more

A high-speed and large-scale processor dedicated to quantum computing is proposed, which has the minimum operation function needed for execution of a quantum algorithm. In this processor, the probability amplitudes determined by the states of the quantum bits in the quantum computer are described using only one bit, and we named it a logic quantum processor. It is found that for this processor an execution speed 275 times higher than that of a software simulation is obtained when the processor executing the quantum algorithm for 16 qubit has been implemented in a FPGA. As an example application, it is shown that the processor can solve the satisfiability problem and that it can also simulate the decoherence in the quantum computer at high speed.

Masanori Ohya, Igor Vasil'evich Volovich

Gen Chen, Lin Wang

Since the beginning of the 21st century, the scale and complexity of financial derivatives have increased significantly. Meanwhile, the pricing models of derivatives have become increasingly complex, and have involved larger-scale data, posing unprecedented challenges to algorithms and computing speed. Although classical computing can still solve the pricing problem of some complex derivatives by adopting the big data simulation algorithms, its speed has become more and more difficult to meet the needs of real-time computing. In recent years, the emerging quantum computing makes full use of the advantages of quantum superposition to improve the computing speed and accuracy. It has been proven to be an efficient computing mode in big data simulation. This paper takes the pricing of European options and defaultable bonds as examples to explore the application of Quantum Monte Carlo Simulation of complicated derivative pricing in big data. The innovative comprehensive method of pricing derivatives by Quantum Monte Carlo Simulation provides new development for the application of quantum computing in the pricing of complicated financial derivatives (taking defaultable bond as example) with big data simulation. In the case of defaultable bond pricing, the Quantum Amplitude Estimation has been employed to speed up the Monte Carlo Simulation and the price of it has been successfully obtained with at least the same accuracy, which demonstrates that the Quantum Monte Carlo Simulation is a prospective approach to price the complicated derivatives based on the big data since the quantum bits has dramatically increased.

Scott Kirkpatrick

Keisuke Fukada, Tatsuhiko Shirai, Mikio Morita +3 more

Quantum simulators have been actively developed that faithfully simulate the quantum computer operations on a classical computer. Since the amount of memory and operations required by quantum simulators increases exponentially with the number of qubits, MPI (Message Passing Interface) and distributed processing, which are methods to effectively use a large number of computing resources, can be used to achieve higher computation speed. In this paper, we profile the performance of the variational quantum eigensolver (VQE) algorithm by applying it to a multi-node quantum simulator to calculate the energy values of carbon dioxide (CO2) molecule. We also discuss how the size of CI (configuration interaction) ansatz and the convergence threshold of the classical optimization change the energy values. Experimental results demonstrate that the computational time is reduced as the number of computation nodes increases. The speed-up ratio is increased as the number of qubits increases. When the qubit size is 30, a maximum of 12.2 times speed-up is achieved if the computing node size is increased from 1 to 128.

A. Kitaev, Alexander Khanievich Shen', M. Vyalyi