Psitrum: An Open Source Simulator for Universal Quantum Computers

Physical Review Applied, 2021

With the increasing size of quantum processors, sub-modules that constitute the processor hardware will become too large to accurately simulate on a classical computer. Therefore, one would soon have to fabricate and test each new design primitive and parameter choice in time-consuming coordination between design, fabrication, and experimental validation. Here we show how one can design and test the performance of next-generation quantum hardware-by using existing quantum computers. Focusing on superconducting transmon processors as a prominent hardware platform, we compute the static and dynamic properties of individual and coupled transmons. We show how the energy spectra of transmons can be obtained by variational hybrid quantum-classical algorithms that are well-suited for near-term noisy quantum computers. In addition, single-and two-qubit gate simulations are demonstrated via Suzuki-Trotter decomposition. Our methods pave a promising way towards designing candidate quantum processors when the demands of calculating sub-module properties exceed the capabilities of classical computing resources.

… on Electronic System …, 2010

Work is in progress throughout the globe to efficiently use the potential of the quantum theory of computation over their classical counterpart and hence there is a need of building quantum computing hardware. The target of building quantum computers can be achieved if we have better tools for the design of quantum hardware. Quantum circuit simulators are tools for the logic verification of quantum circuits and they can be an essential component of quantum CAD tools in the future. This work is the extension of the previous work by the authors, in order to increase the efficiency of the simulator and to incorporate more components in the gate library. In this paper we have proposed an efficient simulator which can simulate a quantum circuit specified using the proposed quantum hardware descriptive language (QHDL).

arXiv (Cornell University), 2022

Quantum computing is changing the way we think about computing. Significant strides in research and development for managing and harnessing the power of quantum systems has been made in recent years, demonstrating the potential for transformative quantum technology. Quantum phenomena like superposition, entanglement, and interference can be exploited to solve issues that are difficult for traditional computers. IBM's first public access to true quantum computers through the cloud, as well as Google's demonstration of quantum supremacy, are among the accomplishments. Besides, a slew of other commercial, government, and academic projects are in the works to create next-generation hardware, a software stack to support the hardware ecosystem, and viable quantum algorithms. This chapter covers various quantum computing architectures including many hardware technologies that are being investigated. We also discuss a variety of challenges, including numerous errors/noise that plague the quantum computers. An overview of literature investigating noise-resilience approaches is also presented.

arXiv preprint arXiv:2005.12874, 2020

Noisy, intermediate-scale quantum computers come with intrinsic limitations in terms of the number of qubits (circuit "width") and decoherence time (circuit "depth") they can have. Here, for the first time, we demonstrate a recently introduced method that breaks a circuit into smaller subcircuits or fragments, and thus makes it possible to run circuits that are either too wide or too deep for a given quantum processor. We investigate the behavior of the method on one of IBM's 20-qubit superconducting quantum processors with various numbers of qubits and fragments. We build noise models that capture decoherence, readout error, and gate imperfections for this particular processor. We then carry out noisy simulations of the method in order to account for the observed experimental results. We find an agreement within 20% between the experimental and the simulated success probabilities, and we observe that recombining noisy fragments yields overall results that can outperform the results without fragmentation.

Microprocessors and Microsystems, 2015

In one-way quantum computation (1WQC) model, universal quantum computations are performed using measurements to designated qubits in a highly entangled state. The choices of bases for these measurements as well as the structure of the entanglements specify a quantum algorithm. As scalable and reliable quantum computers have not been implemented yet, quantum computation simulators are the only widely available tools to design and test quantum algorithms. However, simulating the quantum computations on a standard classical computer in most cases requires exponential memory and time. In this paper, a general direct simulator for 1WQC, called OWQS, is presented. Some techniques such as qubit elimination, pattern reordering and implicit simulation of actions are used to considerably reduce the time and memory needed for the simulations. Moreover, our simulator is adjusted to simulate the measurement patterns with a generalized flow without calculating the measurement probabilities which is called extended one-way quantum computation simulator (EOWQS). Experimental results validate the feasibility of the proposed simulators and that OWQS and EOWQS are faster as compared with the well-known quantum circuit simulators, i.e., QuIDDPro and libquantum for simulating 1WQC model 1 .

Lecture Notes in Computer Science, 2009

In this paper we briefly describe a Mathematica package for simulation of quantum circuits and illustrate some of its features by simple examples. Unlike other Mathematica-based quantum simulators, our program provides a user-friendly graphical interface for generating quantum circuits and computing the circuit unitary matrices. It can be used for designing and testing different quantum algorithms. As an example we consider a quantum circuit implementing Grover's search algorithm and show that it gives a quadratic speed-up in solving the search problem.

EPJ Quantum Technology, 2024

The potential of achieving computational hardware with quantum advantage depends heavily on the quality of quantum gate operations. However, the presence of imperfect two-qubit gates poses a significant challenge and acts as a major obstacle in developing scalable quantum information processors. Google's Quantum AI and collaborators claimed to have conducted a supremacy regime experiment. In this experiment, a new two-qubit universal gate called the Sycamore gate is constructed and employed to generate random quantum circuits (RQCs), using a programmable quantum processor with 53 qubits. These computations were carried out in a computational state space of size 9 × 10 15. Nevertheless, even in strictly-controlled laboratory settings, quantum information on quantum processors is susceptible to various disturbances, including undesired interaction with the surroundings and imperfections in the quantum state. To address this issue, we conduct both quantum state tomography (QST) and quantum process tomography (QPT) experiments on Google's Sycamore gate using different artificial architectural superconducting quantum computer. Furthermore, to demonstrate how errors affect gate fidelity at the level of quantum circuits, we design and conduct full QST experiments for the five-qubit eight-cycle circuit, which was introduced as an example of the programability of Google's Sycamore quantum processor. These quantum tomography experiments are conducted in three distinct environments: noise-free, noisy simulation, and on IBM Quantum's genuine quantum computer. Our results offer valuable insights into the performance of IBM Quantum's hardware and the robustness of Sycamore gates within this experimental setup. These findings contribute to our understanding of quantum hardware performance and provide valuable information for optimizing quantum algorithms for practical applications.

Open Science Articles (OSAs), 2025

Quantum computers promise to revolutionize science and technology by offering the potential to solve complex problems intractable with classical approaches. However, realizing this potential hinges on effectively managing the noise and errors inherent in quantum systems, which threaten computational accuracy. This work has explored a broad spectrum, from the fundamentals of quantum computation to strategies for enhancing the performance of devices in the Noisy Intermediate-Scale Quantum (NISQ) era, with a particular focus on the critical role of quantum error correction (QEC) codes and the decoder algorithms developed for them. While various methods exist for characterizing and manipulating quantum states, the scalability of these methods becomes a significant issue as the number of qubits increases. The measurement process itself also requires careful planning as it perturbs the quantum state. QEC codes, especially topological codes like surface codes, developed to overcome these challenges, form the foundation of fault-tolerant quantum computation. The success of a QEC code largely depends on the performance of its decoder algorithm, which analyses error syndromes to detect and correct the most probable errors. Alongside classical approaches like Minimum-Weight Perfect Matching (MWPM) and Union-Find, newer and potentially more powerful methods such as Maximum-Likelihood Decoders (MLD) and Neural Networkbased Decoders (NNbD) are active areas of research. A prominent aspect of this study is the demonstration that, even with limited classical computing resources, the theoretical scalability of quantum error correction mechanisms can be pushed to remarkable limits using sophisticated simulation techniques and algorithmic ingenuity. Notably, striking results such as the simulation and verification of surface code error correction algorithms for systems of 25 million theoretical qubits have been achieved on a personal computer. Furthermore, the graphical visualization of error correction solutions for systems exceeding 100,000 theoretical qubits underscores the analysability of such complex systems. These findings indicate that error correction principles are theoretically applicable to very large systems and that classical simulations continue to be a valuable tool in this exploratory journey. In the future, key objectives will include the development of more efficient and scalable decoders, the discovery of new QEC codes, the creation of realistic noise models, advancements in hardware-software co-design, and the execution of complex algorithms on logical qubits. Quantum error correction will continue to play a central role on the path to fault-tolerant quantum computation, and theoretical and simulational work in this area will offer significant contributions to the realization of practical quantum computers. Large-scale simulation achievements driven by the creativity of individual researchers, as highlighted here, bolster hopes for the future of the field.

CompSciRN: Problem Solving (Topic), 2016

This paper presents basic understanding of quantum computing, which is one of the hot topics in today's research area. The discussion starts to clarify the basic difference between a quantum computer and a classical computer at different levels such as bit level, register level, logic gate level, and circuit level. The main purpose of this paper is to deepen the understanding of the basic model of quantum computing by building a relationship between quantum gates, reversible logic gates, and classical logic gates. This paper demonstrates the quantum bit or qubit, quantum register, and quantum gates because they are important elements in the understanding of the quantum circuit model. The quantum computer simulator has been used to implement quantum circuits and quantum algorithms. The famous Deutsch's algorithm has been described and a corresponding quantum circuit is presented. The last demonstration is converting a combinational logic circuit to a quantum circuit for the s...

Computer Physics Communications, 2011

This Mathematica 7.0/8.0 package upgrades and extends the quantum computer simulation code called QDENSITY. Use of the density matrix was emphasized in QDENSITY, although that code was also applicable to a quantum state description. In the present version, the quantum state version is stressed and made amenable to future extensions to parallel computer simulations. The add-on QCWAVE extends QDENSITY in several ways. The first way is to describe the action of one, two and three-qubit quantum gates as a set of small (2 × 2, 4 × 4 or 8 × 8) matrices acting on the 2 nq amplitudes for a system of n q qubits. This procedure was described in our parallel computer simulation QCMPI and is reviewed here. The advantage is that smaller storage demands are made, without loss of speed, and that the procedure can take advantage of message passing interface (MPI) techniques, which will hopefully be generally available in future Mathematica versions.