295 research outputs found
Tensor network study of the <i>m</i> = 1/2 magnetization plateau in the Shastry-Sutherland model at finite temperature
Diagnosing barren plateaus with tools from quantum optimal control
Variational Quantum Algorithms (VQAs) have received considerable attention due to their potential for achieving near-term quantum advantage. However, more work is needed to understand their scalability. One known scaling result for VQAs is barren plateaus, where certain circumstances lead to exponentially vanishing gradients. It is common folklore that problem-inspired ansatzes avoid barren plateaus, but in fact, very little is known about their gradient scaling. In this work we employ tools from quantum optimal control to develop a framework that can diagnose the presence or absence of barren plateaus for problem-inspired ansatzes. Such ansatzes include the Quantum Alternating Operator Ansatz (QAOA), the Hamiltonian Variational Ansatz (HVA), and others. With our framework, we prove that avoiding barren plateaus for these ansatzes is not always guaranteed. Specifically, we show that the gradient scaling of the VQA depends on the degree of controllability of the system, and hence can be diagnosed through the dynamical Lie algebra g obtained from the generators of the ansatz. We analyze the existence of barren plateaus in QAOA and HVA ansatzes, and we highlight the role of the input state, as different initial states can lead to the presence or absence of barren plateaus. Taken together, our results provide a framework for trainability-aware ansatz design strategies that do not come at the cost of extra quantum resources. Moreover, we prove no-go results for obtaining ground states with variational ansatzes for controllable system such as spin glasses. Our work establishes a link between the existence of barren plateaus and the scaling of the dimension of g
Unifying and benchmarking state-of-the-art quantum error mitigation techniques
Error mitigation is an essential component of achieving practical quantum
advantage in the near term, and a number of different approaches have been
proposed. In this work, we recognize that many state-of-the-art error
mitigation methods share a common feature: they are data-driven, employing
classical data obtained from runs of different quantum circuits. For example,
Zero-noise extrapolation (ZNE) uses variable noise data and Clifford-data
regression (CDR) uses data from near-Clifford circuits. We show that Virtual
Distillation (VD) can be viewed in a similar manner by considering classical
data produced from different numbers of state preparations. Observing this fact
allows us to unify these three methods under a general data-driven error
mitigation framework that we call UNIfied Technique for Error mitigation with
Data (UNITED). In certain situations, we find that our UNITED method can
outperform the individual methods (i.e., the whole is better than the
individual parts). Specifically, we employ a realistic noise model obtained
from a trapped ion quantum computer to benchmark UNITED, as well as
state-of-the-art methods, for problems with various numbers of qubits, circuit
depths and total numbers of shots. We find that different techniques are
optimal for different shot budgets. Namely, ZNE is the best performer for small
shot budgets (), while Clifford-based approaches are optimal for larger
shot budgets (), and for our largest considered shot budget
(), UNITED gives the most accurate correction. Hence, our work
represents a benchmarking of current error mitigation methods, and provides a
guide for the regimes when certain methods are most useful.Comment: 13 pages, 4 figure
The battle of clean and dirty qubits in the era of partial error correction
When error correction becomes possible it will be necessary to dedicate a
large number of physical qubits to each logical qubit. Error correction allows
for deeper circuits to be run, but each additional physical qubit can
potentially contribute an exponential increase in computational space, so there
is a trade-off between using qubits for error correction or using them as noisy
qubits. In this work we look at the effects of using noisy qubits in
conjunction with noiseless qubits (an idealized model for error-corrected
qubits), which we call the "clean and dirty" setup. We employ analytical models
and numerical simulations to characterize this setup. Numerically we show the
appearance of Noise-Induced Barren Plateaus (NIBPs), i.e., an exponential
concentration of observables caused by noise, in an Ising model Hamiltonian
variational ansatz circuit. We observe this even if only a single qubit is
noisy and given a deep enough circuit, suggesting that NIBPs cannot be fully
overcome simply by error-correcting a subset of the qubits. On the positive
side, we find that for every noiseless qubit in the circuit, there is an
exponential suppression in concentration of gradient observables, showing the
benefit of partial error correction. Finally, our analytical models corroborate
these findings by showing that observables concentrate with a scaling in the
exponent related to the ratio of dirty-to-total qubits.Comment: 27 pages, 15 figures, (v2) minor change
Variational quantum eigensolver with reduced circuit complexity
The variational quantum eigensolver (VQE) is one of the most promising algorithms to find eigenstates of a given Hamiltonian on noisy intermediate-scale quantum devices (NISQ). The practical realization is limited by the complexity of quantum circuits. Here we present an approach to reduce quantum circuit complexity in VQE for electronic structure calculations. Our ClusterVQE algorithm splits the initial qubit space into clusters which are further distributed on individual (shallower) quantum circuits. The clusters are obtained based on mutual information reflecting maximal entanglement between qubits, whereas inter-cluster correlation is taken into account via a new “dressed” Hamiltonian. ClusterVQE therefore allows exact simulation of the problem by using fewer qubits and shallower circuit depth at the cost of additional classical resources, making it a potential leader for quantum chemistry simulations on NISQ devices. Proof-of-principle demonstrations are presented for several molecular systems based on quantum simulators as well as IBM quantum devices
Mitiq : a software package for error mitigation on noisy quantum computers
We introduce Mitiq, a Python package for error mitigation on noisy quantum computers. Error mitigation techniques can reduce the impact of noise on near-term quantum computers with minimal overhead in quantum resources by relying on a mixture of quantum sampling and classical post-processing techniques. Mitiq is an extensible toolkit of different error mitigation methods, including zero-noise extrapolation, probabilistic error cancellation, and Clifford data regression. The library is designed to be compatible with generic backends and interfaces with different quantum software frameworks. We describe Mitiq using code snippets to demonstrate usage and discuss features and contribution guidelines. We present several examples demonstrating error mitigation on IBM and Rigetti superconducting quantum processors as well as on noisy simulators
Abnormal Segregation of Alleles and Haplotypes at the Polymorphic Site of the PRNP Gene Within Promoter and Intron 1 Regions in Polish Holstein–Friesian Cattle
Unambiguous detection of nitrated explosive vapours by fluorescence quenching of dendrimer films
Unambiguous and selective standoff (non-contact) infield detection of nitro-containingexplosives and taggants is an important goal but difficult to achieve with standard analyticaltechniques. Oxidative fluorescence quenching is emerging as a high sensitivity method fordetecting such materials but is prone to false positives—everyday items such as perfumeselicit similar responses. Here we report thin films of light-emitting dendrimers that detectvapours of explosives and taggants selectively—fluorescence quenching is not observed for arange of common interferents. Using a combination of neutron reflectometry, quartz crystalmicrobalance and photophysical measurements we show that the origin of the selectivity isprimarily electronic and not the diffusion kinetics of the analyte or its distribution in the film.The results are a major advance in the development of sensing materials for the standoffdetection of nitro-based explosive vapours, and deliver significant insights into the physicalprocesses that govern the sensing efficacy
The effect of solvent on the catalytic properties of microperoxidase-11
peer-reviewedThe effect of a range of solvents on the catalytic oxidation of methyl phenyl sulfide to methyl phenyl sulfoxide by MP-11 and by a cyclodextrin derivative of MP-11 was examined. The addition of low concentrations of alcohols enhanced the initial rate of sulfoxidation, most likely due to dispersion of MP-11 aggregates. Higher alcohol concentrations resulted in a decrease in activity arising from solvation of the hydrophobic sulfide, disrupting binding to the catalyst. In alcohols, the yield of product was decreased due to increased rates of MP-11 deactivation via the formation of aldehydes (for primary alcohols) or by peroxide-based deactivation. The catalytic activity of the cyclodextrin modified MP-11 was similar to that of MP-11 itself, demonstrating that it is the N-terminal side of MP-11 which is the determinant of catalytic activity.ACCEPTEDpeer-reviewe
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