8,615 research outputs found
Optimizing the Number of Gates in Quantum Search
In its usual form, Grover's quantum search algorithm uses
queries and other elementary gates to find a solution in
an -bit database. Grover in 2002 showed how to reduce the number of other
gates to for the special case where the database has a
unique solution, without significantly increasing the number of queries. We
show how to reduce this further to gates for any
constant , and sufficiently large . This means that, on average, the
gates between two queries barely touch more than a constant number of the qubits on which the algorithm acts. For a very large that is a power of
2, we can choose such that the algorithm uses essentially the minimal
number of queries, and only
other gates.Comment: 11 pages LaTeX. Version 2: small improvements in the proof
QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits
Parameterized Quantum Circuits (PQC) have obtained increasing popularity
thanks to their great potential for near-term Noisy Intermediate-Scale Quantum
(NISQ) computers. Achieving quantum advantages usually requires a large number
of qubits and quantum circuits with enough capacity. However, limited coherence
time and massive quantum noises severely constrain the size of quantum circuits
that can be executed reliably on real machines. To address these two pain
points, we propose QuantumSEA, an in-time sparse exploration for noise-adaptive
quantum circuits, aiming to achieve two key objectives: (1) implicit circuits
capacity during training - by dynamically exploring the circuit's sparse
connectivity and sticking a fixed small number of quantum gates throughout the
training which satisfies the coherence time and enjoy light noises, enabling
feasible executions on real quantum devices; (2) noise robustness - by jointly
optimizing the topology and parameters of quantum circuits under real device
noise models. In each update step of sparsity, we leverage the moving average
of historical gradients to grow necessary gates and utilize salience-based
pruning to eliminate insignificant gates. Extensive experiments are conducted
with 7 Quantum Machine Learning (QML) and Variational Quantum Eigensolver (VQE)
benchmarks on 6 simulated or real quantum computers, where QuantumSEA
consistently surpasses noise-aware search, human-designed, and randomly
generated quantum circuit baselines by a clear performance margin. For example,
even in the most challenging on-chip training regime, our method establishes
state-of-the-art results with only half the number of quantum gates and ~2x
time saving of circuit executions. Codes are available at
https://github.com/VITA-Group/QuantumSEA.Comment: IEEE International Conference on Quantum Computing and Engineering
(QCE 2023
Hybrid Optimization Schemes for Quantum Control
Optimal control theory is a powerful tool for solving control problems in
quantum mechanics, ranging from the control of chemical reactions to the
implementation of gates in a quantum computer. Gradient-based optimization
methods are able to find high fidelity controls, but require considerable
numerical effort and often yield highly complex solutions. We propose here to
employ a two-stage optimization scheme to significantly speed up convergence
and achieve simpler controls. The control is initially parametrized using only
a few free parameters, such that optimization in this pruned search space can
be performed with a simplex method. The result, considered now simply as an
arbitrary function on a time grid, is the starting point for further
optimization with a gradient-based method that can quickly converge to high
fidelities. We illustrate the success of this hybrid technique by optimizing a
holonomic phasegate for two superconducting transmon qubits coupled with a
shared transmission line resonator, showing that a combination of Nelder-Mead
simplex and Krotov's method yields considerably better results than either one
of the two methods alone.Comment: 17 pages, 5 figures, 2 table
Full-Stack, Real-System Quantum Computer Studies: Architectural Comparisons and Design Insights
In recent years, Quantum Computing (QC) has progressed to the point where
small working prototypes are available for use. Termed Noisy Intermediate-Scale
Quantum (NISQ) computers, these prototypes are too small for large benchmarks
or even for Quantum Error Correction, but they do have sufficient resources to
run small benchmarks, particularly if compiled with optimizations to make use
of scarce qubits and limited operation counts and coherence times. QC has not
yet, however, settled on a particular preferred device implementation
technology, and indeed different NISQ prototypes implement qubits with very
different physical approaches and therefore widely-varying device and machine
characteristics.
Our work performs a full-stack, benchmark-driven hardware-software analysis
of QC systems. We evaluate QC architectural possibilities, software-visible
gates, and software optimizations to tackle fundamental design questions about
gate set choices, communication topology, the factors affecting benchmark
performance and compiler optimizations. In order to answer key cross-technology
and cross-platform design questions, our work has built the first top-to-bottom
toolflow to target different qubit device technologies, including
superconducting and trapped ion qubits which are the current QC front-runners.
We use our toolflow, TriQ, to conduct {\em real-system} measurements on 7
running QC prototypes from 3 different groups, IBM, Rigetti, and University of
Maryland. From these real-system experiences at QC's hardware-software
interface, we make observations about native and software-visible gates for
different QC technologies, communication topologies, and the value of
noise-aware compilation even on lower-noise platforms. This is the largest
cross-platform real-system QC study performed thus far; its results have the
potential to inform both QC device and compiler design going forward.Comment: Preprint of a publication in ISCA 201
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