175 research outputs found
On Testing Quantum Programs
A quantum computer (QC) can solve many computational problems more
efficiently than a classic one. The field of QCs is growing: companies (such as
DWave, IBM, Google, and Microsoft) are building QC offerings. We position that
software engineers should look into defining a set of software engineering
practices that apply to QC's software. To start this process, we give examples
of challenges associated with testing such software and sketch potential
solutions to some of these challenges.Comment: A condensed version to appear in Proceedings of the 41st
International Conference on Software Engineering (ICSE 2019
Cross-level Validation of Topological Quantum Circuits
Quantum computing promises a new approach to solving difficult computational
problems, and the quest of building a quantum computer has started. While the
first attempts on construction were succesful, scalability has never been
achieved, due to the inherent fragile nature of the quantum bits (qubits). From
the multitude of approaches to achieve scalability topological quantum
computing (TQC) is the most promising one, by being based on an flexible
approach to error-correction and making use of the straightforward
measurement-based computing technique. TQC circuits are defined within a large,
uniform, 3-dimensional lattice of physical qubits produced by the hardware and
the physical volume of this lattice directly relates to the resources required
for computation. Circuit optimization may result in non-intuitive mismatches
between circuit specification and implementation. In this paper we introduce
the first method for cross-level validation of TQC circuits. The specification
of the circuit is expressed based on the stabilizer formalism, and the
stabilizer table is checked by mapping the topology on the physical qubit
level, followed by quantum circuit simulation. Simulation results show that
cross-level validation of error-corrected circuits is feasible.Comment: 12 Pages, 5 Figures. Comments Welcome. RC2014, Springer Lecture Notes
on Computer Science (LNCS) 8507, pp. 189-200. Springer International
Publishing, Switzerland (2014), Y. Shigeru and M.Shin-ichi (Eds.
A comprehensive survey on quantum computer usage: How many qubits are employed for what purposes?
Quantum computers (QCs), which work based on the law of quantum mechanics,
are expected to be faster than classical computers in several computational
tasks such as prime factoring and simulation of quantum many-body systems. In
the last decade, research and development of QCs have rapidly advanced. Now
hundreds of physical qubits are at our disposal, and one can find several
remarkable experiments actually outperforming the classical computer in a
specific computational task. On the other hand, it is unclear what the typical
usages of the QCs are. Here we conduct an extensive survey on the papers that
are posted in the quant-ph section in arXiv and claim to have used QCs in their
abstracts. To understand the current situation of the research and development
of the QCs, we evaluated the descriptive statistics about the papers, including
the number of qubits employed, QPU vendors, application domains and so on. Our
survey shows that the annual number of publications is increasing, and the
typical number of qubits employed is about six to ten, growing along with the
increase in the quantum volume (QV). Most of the preprints are devoted to
applications such as quantum machine learning, condensed matter physics, and
quantum chemistry, while quantum error correction and quantum noise mitigation
use more qubits than the other topics. These imply that the increase in QV is
fundamentally relevant, and more experiments for quantum error correction, and
noise mitigation using shallow circuits with more qubits will take place.Comment: 14 pages, 5 figures, figures regenerate
Trustworthy Quantum Computation through Quantum Physical Unclonable Functions
Quantum computing is under rapid development, and today there are several
cloud-based, quantum computers (QCs) of modest size (>100s of physical qubits).
Although these QCs, along with their highly-specialized classical support
infrastructure, are in limited supply, they are readily available for remote
access and programming. This work shows the viability of using intrinsic
quantum hardware properties for fingerprinting cloud-based QCs that exist
today. We demonstrate the reliability of intrinsic fingerprinting with real QC
characterization data, as well as simulated QC data, and we detail a quantum
physically unclonable function (Q-PUF) scheme for secure key generation using
unique fingerprint data combined with fuzzy extraction. We use fixed-frequency
transmon qubits for prototyping our methods
Subspace Variational Quantum Simulator
Quantum simulation is one of the key applications of quantum computing, which
can accelerate research and development in chemistry, material science, etc.
Here, we propose an efficient method to simulate the time evolution driven by a
static Hamiltonian, named subspace variational quantum simulator (SVQS). SVQS
employs the subspace-search variational eigensolver (SSVQE) to find a
low-energy subspace and further extends it to simulate dynamics within the
low-energy subspace. More precisely, using a parameterized quantum circuit, the
low-energy subspace of interest is encoded into a computational subspace
spanned by a set of computational basis, where information processing can be
easily done. After the information processing, the computational subspace is
decoded to the original low-energy subspace. This allows us to simulate the
dynamics of low-energy subspace with lower overhead compared to existing
schemes. While the dimension is restricted for feasibility on near-term quantum
devices, the idea is similar to quantum phase estimation and its applications
such as quantum linear system solver and quantum metropolis sampling. Because
of this simplicity, we can successfully demonstrate the proposed method on the
actual quantum device using Regetti Quantum Cloud Service. Furthermore, we
propose a variational initial state preparation for SVQS, where the initial
states are searched from the simulatable eigensubspace. Finally, we demonstrate
SVQS on Rigetti Quantum Cloud Service
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