6 research outputs found
A generative modeling approach for benchmarking and training shallow quantum circuits
Hybrid quantum-classical algorithms provide ways to use noisy
intermediate-scale quantum computers for practical applications. Expanding the
portfolio of such techniques, we propose a quantum circuit learning algorithm
that can be used to assist the characterization of quantum devices and to train
shallow circuits for generative tasks. The procedure leverages quantum hardware
capabilities to its fullest extent by using native gates and their qubit
connectivity. We demonstrate that our approach can learn an optimal preparation
of the Greenberger-Horne-Zeilinger states, also known as "cat states". We
further demonstrate that our approach can efficiently prepare approximate
representations of coherent thermal states, wave functions that encode
Boltzmann probabilities in their amplitudes. Finally, complementing proposals
to characterize the power or usefulness of near-term quantum devices, such as
IBM's quantum volume, we provide a new hardware-independent metric called the
qBAS score. It is based on the performance yield in a specific sampling task on
one of the canonical machine learning data sets known as Bars and Stripes. We
show how entanglement is a key ingredient in encoding the patterns of this data
set; an ideal benchmark for testing hardware starting at four qubits and up. We
provide experimental results and evaluation of this metric to probe the trade
off between several architectural circuit designs and circuit depths on an
ion-trap quantum computer.Comment: 16 pages, 9 figures. Minor revisions. As published in npj Quantum
Informatio
Dynamics of a two-level system controlled by a field external coupled to a nonlinear detector
Se presenta un qubit de flujo controlado por medio de un campo externo periódico en el tiempo, con un transistor electrónico simple en su régimen no lineal, como detector. Se considera el ambiente del qubit representado por el detector, el cual a su vez está acoplado a un ambiente Ohmico. Por medio de la ecuación maestra de Floquet-Born-Markov se analiza el comportamiento de la diferencia de poblaciones en el qubit, presentando el efecto del acoplamiento del sistema de dos niveles con el detector: este exhibe picos antiresonantes debido a transiciones multifotónicas entre estados de Floquet.We study a qubit system externally driven by a time periodic external field, with a single electron transistor in the nonlinear regime as the detector of the qubit’s state. The qubit’s dissipative dynamics mainly takes place due to the linear coupling with the detector; the latter is, in turn, coupled to an Ohmic environment. In the Born-Markov regime, we calculate the qubit’s populations difference in presence of the detector: we find antiresonant peaks due to multiphoton transitions between Floquet states.Departamento Administrativo de Ciencia, TecnologÃa e Innovación [CO] Colciencias1106-452-21296Control cuántico de las propiedades electrónicas y de espÃn en nanoestructuras inorgánicas, orgánicas y biológicasn
Composable Programming of Hybrid Workflows for Quantum Simulation
We present a composable design scheme for the development of hybrid
quantum/classical algorithms and workflows for applications of quantum
simulation. Our object-oriented approach is based on constructing an expressive
set of common data structures and methods that enable programming of a broad
variety of complex hybrid quantum simulation applications. The abstract core of
our scheme is distilled from the analysis of the current quantum simulation
algorithms. Subsequently, it allows a synthesis of new hybrid algorithms and
workflows via the extension, specialization, and dynamic customization of the
abstract core classes defined by our design. We implement our design scheme
using the hardware-agnostic programming language QCOR into the QuaSiMo library.
To validate our implementation, we test and show its utility on commercial
quantum processors from IBM, running some prototypical quantum simulations
QuaSiMo: A Composable Library to Program Hybrid Workflows for Quantum Simulation
We present a composable design scheme for the development of hybrid
quantum/classical algorithms and workflows for applications of quantum
simulation. Our object-oriented approach is based on constructing an expressive
set of common data structures and methods that enable programming of a broad
variety of complex hybrid quantum simulation applications. The abstract core of
our scheme is distilled from the analysis of the current quantum simulation
algorithms. Subsequently, it allows a synthesis of new hybrid algorithms and
workflows via the extension, specialization, and dynamic customization of the
abstract core classes defined by our design. We implement our design scheme
using the hardware-agnostic programming language QCOR into the QuaSiMo library.
To validate our implementation, we test and show its utility on commercial
quantum processors from IBM and Rigetti, running some prototypical quantum
simulations
Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research
Quantum computing offers a new paradigm for advancing high-energy physics research by enabling novel methods for representing and reasoning about fundamental quantum mechanical phenomena. Realizing these ideals will require the development of novel computational tools for modeling and simulation, detection and classification, data analysis, and forecasting of high-energy physics (HEP) experiments. While the emerging hardware, software, and applications of quantum computing are exciting opportunities, significant gaps remain in integrating such techniques into the HEP community research programs. Here we identify both the challenges and opportunities for developing quantum computing systems and software to advance HEP discovery science. We describe opportunities for the focused development of algorithms, applications, software, hardware, and infrastructure to support both practical and theoretical applications of quantum computing to HEP problems within the next 10 years