6 research outputs found
An Architecture for Improved Surface Code Connectivity in Neutral Atoms
In order to achieve error rates necessary for advantageous quantum
algorithms, Quantum Error Correction (QEC) will need to be employed, improving
logical qubit fidelity beyond what can be achieved physically. As today's
devices begin to scale, co-designing architectures for QEC with the underlying
hardware will be necessary to reduce the daunting overheads and accelerate the
realization of practical quantum computing. In this work, we focus on logical
computation in QEC. We address quantum computers made from neutral atom arrays
to design a surface code architecture that translates the hardware's higher
physical connectivity into a higher logical connectivity. We propose groups of
interleaved logical qubits, gaining all-to-all connectivity within the group
via efficient transversal CNOT gates. Compared to standard lattice surgery
operations, this reduces both the overall qubit footprint and execution time,
lowering the spacetime overhead needed for small-scale QEC circuits. We also
explore the architecture's scalability. We look at using physical atom movement
schemes and propose interleaved lattice surgery which allows an all-to-all
connectivity between qubits in adjacent interleaved groups, creating a higher
connectivity routing space for large-scale circuits. Using numerical
simulations, we evaluate the total routing time of interleaved lattice surgery
and atom movement for various circuit sizes. We identify a cross-over point
defining intermediate-scale circuits where atom movement is best and
large-scale circuits where interleaved lattice surgery is best. We use this to
motivate a hybrid approach as devices continue to scale, with the choice of
operation depending on the routing distance
QContext: Context-Aware Decomposition for Quantum Gates
In this paper we propose QContext, a new compiler structure that incorporates
context-aware and topology-aware decompositions. Because of circuit equivalence
rules and resynthesis, variants of a gate-decomposition template may exist.
QContext exploits the circuit information and the hardware topology to select
the gate variant that increases circuit optimization opportunities. We study
the basis-gate-level context-aware decomposition for Toffoli gates and the
native-gate-level context-aware decomposition for CNOT gates. Our experiments
show that QContext reduces the number of gates as compared with the
state-of-the-art approach, Orchestrated Trios.Comment: 10 page
VarSaw: Application-tailored Measurement Error Mitigation for Variational Quantum Algorithms
For potential quantum advantage, Variational Quantum Algorithms (VQAs) need
high accuracy beyond the capability of today's NISQ devices, and thus will
benefit from error mitigation. In this work we are interested in mitigating
measurement errors which occur during qubit measurements after circuit
execution and tend to be the most error-prone operations, especially
detrimental to VQAs. Prior work, JigSaw, has shown that measuring only small
subsets of circuit qubits at a time and collecting results across all such
subset circuits can reduce measurement errors. Then, running the entire
(global) original circuit and extracting the qubit-qubit measurement
correlations can be used in conjunction with the subsets to construct a
high-fidelity output distribution of the original circuit. Unfortunately, the
execution cost of JigSaw scales polynomially in the number of qubits in the
circuit, and when compounded by the number of circuits and iterations in VQAs,
the resulting execution cost quickly turns insurmountable.
To combat this, we propose VarSaw, which improves JigSaw in an
application-tailored manner, by identifying considerable redundancy in the
JigSaw approach for VQAs: spatial redundancy across subsets from different VQA
circuits and temporal redundancy across globals from different VQA iterations.
VarSaw then eliminates these forms of redundancy by commuting the subset
circuits and selectively executing the global circuits, reducing computational
cost (in terms of the number of circuits executed) over naive JigSaw for VQA by
25x on average and up to 1000x, for the same VQA accuracy. Further, it can
recover, on average, 45% of the infidelity from measurement errors in the noisy
VQA baseline. Finally, it improves fidelity by 55%, on average, over JigSaw for
a fixed computational budget. VarSaw can be accessed here:
https://github.com/siddharthdangwal/VarSaw.Comment: Appears at the International Conference on Architectural Support for
Programming Languages and Operating Systems (ASPLOS) 2024. First two authors
contributed equall
Clifford Assisted Optimal Pass Selection for Quantum Transpilation
The fidelity of quantum programs in the NISQ era is limited by high levels of
device noise. To increase the fidelity of quantum programs running on NISQ
devices, a variety of optimizations have been proposed. These include mapping
passes, routing passes, scheduling methods and standalone optimisations which
are usually incorporated into a transpiler as passes. Popular transpilers such
as those proposed by Qiskit, Cirq and Cambridge Quantum Computing make use of
these extensively. However, choosing the right set of transpiler passes and the
right configuration for each pass is a challenging problem. Transpilers often
make critical decisions using heuristics since the ideal choices are impossible
to identify without knowing the target application outcome. Further, the
transpiler also makes simplifying assumptions about device noise that often do
not hold in the real world. As a result, we often see effects where the
fidelity of a target application decreases despite using state-of-the-art
optimisations. To overcome this challenge, we propose OPTRAN, a framework for
Choosing an Optimal Pass Set for Quantum Transpilation. OPTRAN uses classically
simulable quantum circuits composed entirely of Clifford gates, that resemble
the target application, to estimate how different passes interact with each
other in the context of the target application. OPTRAN then uses this
information to choose the optimal combination of passes that maximizes the
target application's fidelity when run on the actual device. Our experiments on
IBM machines show that OPTRAN improves fidelity by 87.66% of the maximum
possible limit over the baseline used by IBM Qiskit. We also propose low-cost
variants of OPTRAN, called OPTRAN-E-3 and OPTRAN-E-1 that improve fidelity by
78.33% and 76.66% of the maximum permissible limit over the baseline at a
58.33% and 69.44% reduction in cost compared to OPTRAN respectively