66 research outputs found
Rashba-splitting-induced topological flat band detected by anomalous resistance oscillations beyond the quantum limit in ZrTe
Topological flat band, on which the kinetic energy of topological electrons
is quenched, represents a platform for investigating the topological properties
of correlated systems. Recent experimental studies on flattened electronic
bands have mainly concentrated on 2-dimensional materials created by van der
Waals heterostructure-based engineering. Here, we report the observation of a
topological flat band formed by polar-distortion-assisted Rashba splitting in a
3-dimensional Dirac material ZrTe. The polar distortion and resulting
Rashba splitting on the band are directly detected by torque magnetometry and
the anomalous Hall effect, respectively. The local symmetry breaking further
flattens the band, on which we observe resistance oscillations beyond the
quantum limit. These oscillations follow the temperature dependence of the
Lifshitz-Kosevich formula but are evenly distributed in B instead of 1/B in
high magnetic fields. Furthermore, the cyclotron mass anomalously gets enhanced
about 10 times at field ~20 T. These anomalous properties of oscillations
originate from a topological flat band with quenched kinetic energy. The
topological flat band, realized by polar-distortion-assisted Rashba splitting
in the 3-dimensional Dirac system ZrTe, signifies an intrinsic platform
without invoking moir\'e or order-stacking engineering, and also opens the door
for studying topologically correlated phenomena beyond the dimensionality of
two.Comment: 32 pages, 11 figures; Version of original submissio
SpacePulse: Combining Parameterized Pulses and Contextual Subspace for More Practical VQE
In this paper, we explore the integration of parameterized quantum pulses
with the contextual subspace method. The advent of parameterized quantum pulses
marks a transition from traditional quantum gates to a more flexible and
efficient approach to quantum computing. Working with pulses allows us to
potentially access areas of the Hilbert space that are inaccessible with a
CNOT-based circuit decomposition. Compared to solving the complete Hamiltonian
via the traditional Variational Quantum Eigensolver (VQE), the computation of
the contextual correction generally requires fewer qubits and measurements,
thus improving computational efficiency. Plus a Pauli grouping strategy, our
framework, SpacePulse, can minimize the quantum resource cost for the VQE and
enhance the potential for processing larger molecular structures
PAN: Pulse Ansatz on NISQ Machines
Variational quantum algorithms (VQAs) have demonstrated great potentials in
the NISQ era. In the workflow of VQA, the parameters of ansatz are iteratively
updated to approximate the desired quantum states. We have seen various efforts
to draft better ansatz with less gates. In quantum computers, the gate ansatz
will eventually be transformed into control signals such as microwave pulses on
transmons. And the control pulses need elaborate calibration to minimize the
errors such as over-rotation and under-rotation. In the case of VQAs, this
procedure will introduce redundancy, but the variational properties of VQAs can
naturally handle problems of over-rotation and under-rotation by updating the
amplitude and frequency parameters. Therefore, we propose PAN, a native-pulse
ansatz generator framework for VQAs. We generate native-pulse ansatz with
trainable parameters for amplitudes and frequencies. In our proposed PAN, we
are tuning parametric pulses, which are natively supported on NISQ computers.
Considering that parameter-shift rules do not hold for native-pulse ansatz, we
need to deploy non-gradient optimizers. To constrain the number of parameters
sent to the optimizer, we adopt a progressive way to generate our native-pulse
ansatz. Experiments are conducted on both simulators and quantum devices to
validate our methods. When adopted on NISQ machines, PAN obtained improved the
performance with decreased latency by an average of 86%. PAN is able to achieve
99.336% and 96.482% accuracy for VQE tasks on H2 and HeH+ respectively, even
with considerable noises in NISQ machines.Comment: 13 pages, 13 figure
Towards Advantages of Parameterized Quantum Pulses
The advantages of quantum pulses over quantum gates have attracted increasing
attention from researchers. Quantum pulses offer benefits such as flexibility,
high fidelity, scalability, and real-time tuning. However, while there are
established workflows and processes to evaluate the performance of quantum
gates, there has been limited research on profiling parameterized pulses and
providing guidance for pulse circuit design. To address this gap, our study
proposes a set of design spaces for parameterized pulses, evaluating these
pulses based on metrics such as expressivity, entanglement capability, and
effective parameter dimension. Using these design spaces, we demonstrate the
advantages of parameterized pulses over gate circuits in the aspect of duration
and performance at the same time thus enabling high-performance quantum
computing. Our proposed design space for parameterized pulse circuits has shown
promising results in quantum chemistry benchmarks.Comment: 11 Figures, 4 Table
Magnetic-field-induced nonlinear transport in HfTe5
The interplay of electron correlations and topological phases gives rise to
various exotic phenomena including fractionalization, excitonic instability,
and axionic excitation. Recently-discovered transition-metal pentatellurides
can reach the ultra-quantum limit in low magnetic fields and serve as good
candidates for achieving such a combination. Here, we report evidences of
density wave and metal-insulator transition in HfTe5 induced by intense
magnetic fields. Using the nonlinear transport technique, we detect a distinct
nonlinear conduction behavior in the longitudinal resistivity within the a-c
plane, corresponding to the formation of a density wave induced by magnetic
fields. In high fields, the onset of the nonlinear conduction in the Hall
resistivity indicates an impurity-pinned magnetic freeze-out as the possible
origin of the insulating behavior. These frozen electrons can be gradually
re-activated into mobile states above a threshold electric field. These
experimental evidences call for further investigations into the underlying
mechanism for the bulk quantum Hall effect and field-induced phase transtions
in pentatellurides.Comment: 13 pages, 4 figure
RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training
Quantum state preparation, a crucial subroutine in quantum computing,
involves generating a target quantum state from initialized qubits. Arbitrary
state preparation algorithms can be broadly categorized into arithmetic
decomposition (AD) and variational quantum state preparation (VQSP). AD employs
a predefined procedure to decompose the target state into a series of gates,
whereas VQSP iteratively tunes ansatz parameters to approximate target state.
VQSP is particularly apt for Noisy-Intermediate Scale Quantum (NISQ) machines
due to its shorter circuits. However, achieving noise-robust parameter
optimization still remains challenging.
We present RobustState, a novel VQSP training methodology that combines high
robustness with high training efficiency. The core idea involves utilizing
measurement outcomes from real machines to perform back-propagation through
classical simulators, thus incorporating real quantum noise into gradient
calculations. RobustState serves as a versatile, plug-and-play technique
applicable for training parameters from scratch or fine-tuning existing
parameters to enhance fidelity on target machines. It is adaptable to various
ansatzes at both gate and pulse levels and can even benefit other variational
algorithms, such as variational unitary synthesis.
Comprehensive evaluation of RobustState on state preparation tasks for 4
distinct quantum algorithms using 10 real quantum machines demonstrates a
coherent error reduction of up to 7.1 and state fidelity improvement
of up to 96\% and 81\% for 4-Q and 5-Q states, respectively. On average,
RobustState improves fidelity by 50\% and 72\% for 4-Q and 5-Q states compared
to baseline approaches.Comment: Accepted to FASTML @ ICCAD 2023. 14 pages, 20 figure
- …