311 research outputs found
Leakage in restless quantum gate calibration
Quantum computers require high fidelity quantum gates. These gates are
obtained by routine calibration tasks that eat into the availability of
cloud-based devices. Restless circuit execution speeds-up characterization and
calibration by foregoing qubit reset in between circuits. Post-processing the
measured data recovers the desired signal. However, since the qubits are not
reset, leakage -- typically present at the beginning of the calibration -- may
cause issues. Here, we develop a simulator of restless circuit execution based
on a Markov Chain to study the effect of leakage. In the context of error
amplifying single-qubit gates sequences, we show that restless calibration
tolerates up to 0.5% of leakage which is large compared to the gate
fidelity of modern single-qubit gates. Furthermore, we show that restless
circuit execution with leaky gates reduces by 33% the sensitivity of the ORBIT
cost function developed by J. Kelly et al. which is typically used in
closed-loop optimal control~[Phys. Rev. Lett. 112, 240504 (2014)]. Our results
are obtained with standard qubit state discrimination showing that restless
circuit execution is resilient against misclassified non-computational states.
In summary, the restless method is sufficiently robust against leakage in both
standard and closed-loop optimal control gate calibration to provided accurate
results
A SAT approach to the initial mapping problem in SWAP gate insertion for commuting gates
Most quantum circuits require SWAP gate insertion to run on quantum hardware
with limited qubit connectivity. A promising SWAP gate insertion method for
blocks of commuting two-qubit gates is a predetermined swap strategy which
applies layers of SWAP gates simultaneously executable on the coupling map. A
good initial mapping for the swap strategy reduces the number of required swap
gates. However, even when a circuit consists of commuting gates, e.g., as in
the Quantum Approximate Optimization Algorithm (QAOA) or trotterized
simulations of Ising Hamiltonians, finding a good initial mapping is a hard
problem. We present a SAT-based approach to find good initial mappings for
circuits with commuting gates transpiled to the hardware with swap strategies.
Our method achieves a 65% reduction in gate count for random three-regular
graphs with 500 nodes. In addition, we present a heuristic approach that
combines the SAT formulation with a clustering algorithm to reduce large
problems to a manageable size. This approach reduces the number of swap layers
by 25% compared to both a trivial and random initial mapping for a random
three-regular graph with 1000 nodes. Good initial mappings will therefore
enable the study of quantum algorithms, such as QAOA and Ising Hamiltonian
simulation applied to sparse problems, on noisy quantum hardware with several
hundreds of qubits.Comment: 7 page
Optimized Noise Suppression for Quantum Circuits
Quantum computation promises to advance a wide range of computational tasks.
However, current quantum hardware suffers from noise and is too small for error
correction. Thus, accurately utilizing noisy quantum computers strongly relies
on noise characterization, mitigation, and suppression. Crucially, these
methods must also be efficient in terms of their classical and quantum
overhead. Here, we efficiently characterize and mitigate crosstalk noise, which
is a severe error source in, e.g., cross-resonance based superconducting
quantum processors. For crosstalk characterization, we develop a simplified
measurement experiment. Furthermore, we analyze the problem of optimal
experiment scheduling and solve it for common hardware architectures. After
characterization, we mitigate noise in quantum circuits by a noise-aware qubit
routing algorithm. Our integer programming algorithm extends previous work on
optimized qubit routing by swap insertion. We incorporate the measured
crosstalk errors in addition to other, more easily accessible noise data in the
objective function. Furthermore, we strengthen the underlying integer linear
model by proving a convex hull result about an associated class of polytopes,
which has applications beyond this work. We evaluate the proposed method by
characterizing crosstalk noise for a complete 27 qubit chip and leverage the
resulting data to improve the approximation ratio of the Quantum Approximate
Optimization Algorithm by up to 10 % compared to other established noise-aware
routing methods. Our work clearly demonstrates the gains of including noise
data when mapping abstract quantum circuits to hardware native ones
Option Pricing using Quantum Computers
We present a methodology to price options and portfolios of options on a
gate-based quantum computer using amplitude estimation, an algorithm which
provides a quadratic speedup compared to classical Monte Carlo methods. The
options that we cover include vanilla options, multi-asset options and
path-dependent options such as barrier options. We put an emphasis on the
implementation of the quantum circuits required to build the input states and
operators needed by amplitude estimation to price the different option types.
Additionally, we show simulation results to highlight how the circuits that we
implement price the different option contracts. Finally, we examine the
performance of option pricing circuits on quantum hardware using the IBM Q
Tokyo quantum device. We employ a simple, yet effective, error mitigation
scheme that allows us to significantly reduce the errors arising from noisy
two-qubit gates.Comment: Fixed a typo. This article has been accepted in Quantu
Adiabatic quantum simulations with driven superconducting qubits
We propose a quantum simulator based on driven superconducting qubits where
the interactions are generated parametrically by a polychromatic magnetic flux
modulation of a tunable bus element. Using a time-dependent Schrieffer-Wolff
transformation, we analytically derive a multi-qubit Hamiltonian which features
independently tunable and -type interactions as well as local bias
fields over a large parameter range. We demonstrate the adiabatic simulation of
the ground state of a hydrogen molecule using two superconducting qubits and
one tunable bus element. The time required to reach chemical accuracy lies in
the few microsecond range and therefore could be implemented on currently
available superconducting circuits. Further applications of this technique may
also be found in the simulation of interacting spin systems.Comment: 11 pages, 6 figure
Squeezing and quantum approximate optimization
Variational quantum algorithms offer fascinating prospects for the solution
of combinatorial optimization problems using digital quantum computers.
However, the achievable performance in such algorithms and the role of quantum
correlations therein remain unclear. Here, we shed light on this open issue by
establishing a tight connection to the seemingly unrelated field of quantum
metrology: Metrological applications employ quantum states of spin-ensembles
with a reduced variance to achieve an increased sensitivity, and we cast the
generation of such squeezed states in the form of finding optimal solutions to
a combinatorial MaxCut problem with an increased precision. By solving this
optimization problem with a quantum approximate optimization algorithm (QAOA),
we show numerically as well as on an IBM quantum chip how highly squeezed
states are generated in a systematic procedure that can be adapted to a wide
variety of quantum machines. Moreover, squeezing tailored for the QAOA of the
MaxCut permits us to propose a figure of merit for future hardware benchmarks.Comment: 8+7 pages, 4+8 figure
- …