69 research outputs found
Learning-based quantum error mitigation
If NISQ-era quantum computers are to perform useful tasks, they will need to
employ powerful error mitigation techniques. Quasi-probability methods can
permit perfect error compensation at the cost of additional circuit executions,
provided that the nature of the error model is fully understood and
sufficiently local both spatially and temporally. Unfortunately these
conditions are challenging to satisfy. Here we present a method by which the
proper compensation strategy can instead be learned ab initio. Our training
process uses multiple variants of the primary circuit where all non-Clifford
gates are substituted with gates that are efficient to simulate classically.
The process yields a configuration that is near-optimal versus noise in the
real system with its non-Clifford gate set. Having presented a range of
learning strategies, we demonstrate the power of the technique both with real
quantum hardware (IBM devices) and exactly-emulated imperfect quantum
computers. The systems suffer a range of noise severities and types, including
spatially and temporally correlated variants. In all cases the protocol
successfully adapts to the noise and mitigates it to a high degree.Comment: 28 pages, 19 figure
Error statistics and scalability of quantum error mitigation formulas
Quantum computing promises advantages over classical computing in many
problems. Nevertheless, noise in quantum devices prevents most quantum
algorithms from achieving the quantum advantage. Quantum error mitigation
provides a variety of protocols to handle such noise using minimal qubit
resources . While some of those protocols have been implemented in experiments
for a few qubits, it remains unclear whether error mitigation will be effective
in quantum circuits with tens to hundreds of qubits. In this paper, we apply
statistics principles to quantum error mitigation and analyse the scaling
behaviour of its intrinsic error. We find that the error increases linearly
with the gate number before mitigation and sub-linearly
after mitigation, where ,
is the error rate of a quantum gate, and is a
protocol-dependent factor. The scaling is a consequence of the law
of large numbers, and it indicates that error mitigation can suppress the error
by a larger factor in larger circuits. We propose the importance Clifford
sampling as a key technique for error mitigation in large circuits to obtain
this result
TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit ans\"atze
Adaptive quantum variational algorithms are particularly promising for
simulating strongly correlated systems on near-term quantum hardware, but they
are not yet viable due, in large part, to the severe coherence time limitations
on current devices. In this work, we introduce an algorithm called
TETRIS-ADAPT-VQE, which iteratively builds up variational ans\"atze a few
operators at a time in a way dictated by the problem being simulated. This
algorithm is a modified version of the ADAPT-VQE algorithm in which the
one-operator-at-a-time rule is lifted to allow for the addition of multiple
operators with disjoint supports in each iteration. TETRIS-ADAPT-VQE results in
denser but significantly shallower circuits, without increasing the number of
CNOT gates or variational parameters. Its advantage over the original algorithm
in terms of circuit depths increases with the system size. Moreover, the
expensive step of measuring the energy gradient with respect to each candidate
unitary at each iteration is performed only a fraction of the time compared to
ADAPT-VQE. These improvements bring us closer to the goal of demonstrating a
practical quantum advantage on quantum hardware.Comment: 10 pages, 7 figure
Responses of community traits and soil characteristics of Achnatherum inebrians-type degraded grassland to grazing systems in alpine meadows on the Qinghai-Tibet Plateau
IntroductionScientific grazing management is of great significance for the ecological health and sustainable use of alpine meadows.MethodsTo explore appropriate management methods of alpine grasslands of the Qinghai-Tibet Plateau degraded by Achnatherum inebrians (Hance) Keng ex Tzvele presence, we studied the effects of different grazing systems on the A. inebrians population, grassland vegetation community traits, soil characteristics and soil microbial community structure for cold- season grazing plus supplementary feeding pasture (CSF) and four-season open public pasture (FOP) in Tianzhu County, Gansu Province.ResultsCompared with FOP, the CSF site showed significantly inhibited reproduction of A. inebrians, especially the crown width, seed yield and number of reproductive branches per plant were as high as 50%, significantly increased the aboveground biomass of edible forage and soil water content by 57% and 43β55%, better soil nutrients, and significantly reduced soil bulk density by 10β 29%. Different grazing systems affected the composition and diversity of soil microbial communities, with a greater effect on fungi than on bacterial flora. The most abundant phyla of bacteria and fungi were Proteobacteria and Ascomycota for CSF (by 30β38% and 24β28%) and for FOP (by 67β70% and 68β73%), and the relative abundance and species of bacterial and fungal genera were greater for CSF than FOP. The Ξ±-diversity indexes of fungi were improved, and the Ξ²-diversity of fungi was significant difference between CSF and FOP. However, the grazing utilization time was prolonged in FOP, which reduced the diversity and abundance of soil bacteria and increased soil spatial heterogeneity. The use of A. inebrians-type degraded grassland in the cold season, and as a winter supplementary feeding and resting ground, could effectively inhibit expansion of A. inebrians, promote edible forage growth, enhance grassland productivity and community stability, and improve soil structure. DiscussionThe results guide healthy and sustainable utilization of A. inebrians-type degraded grassland in the Qinghai-Tibet Plateau
- β¦