40 research outputs found
316LNステンレス鋼の高温における低サイクル疲労-高サイクル疲労の相互作用
国立大学法人長岡技術科学大
QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
In this article, we present QuASeR, a reference-free DNA sequence
reconstruction implementation via de novo assembly on both gate-based and
quantum annealing platforms. Each one of the four steps of the implementation
(TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept
examples to target both the genomics research community and quantum application
developers in a self-contained manner. The details of the implementation are
discussed for the various layers of the quantum full-stack accelerator design.
We also highlight the limitations of current classical simulation and available
quantum hardware systems. The implementation is open-source and can be found on
https://github.com/prince-ph0en1x/QuASeR.Comment: 24 page
Quantum Accelerated Causal Tomography: Circuit Considerations Towards Applications
In this research we study quantum computing algorithms for accelerating
causal inference. Specifically, we investigate the formulation of causal
hypothesis testing presented in [\textit{Nat Commun} 10, 1472 (2019)]. The
theoretical description is constructed as a scalable quantum gate-based
algorithm on qiskit. We present the circuit construction of the oracle
embedding the causal hypothesis and assess the associated gate complexities.
Our experiments on a simulator platform validates the predicted speedup. We
discuss applications of this framework for causal inference use cases in
bioinformatics and artificial general intelligence.Comment: 9 pages, 5 figure
KetGPT - Dataset Augmentation of Quantum Circuits using Transformers
Quantum algorithms, represented as quantum circuits, can be used as
benchmarks for assessing the performance of quantum systems. Existing datasets,
widely utilized in the field, suffer from limitations in size and versatility,
leading researchers to employ randomly generated circuits. Random circuits are,
however, not representative benchmarks as they lack the inherent properties of
real quantum algorithms for which the quantum systems are manufactured. This
shortage of `useful' quantum benchmarks poses a challenge to advancing the
development and comparison of quantum compilers and hardware.
This research aims to enhance the existing quantum circuit datasets by
generating what we refer to as `realistic-looking' circuits by employing the
Transformer machine learning architecture. For this purpose, we introduce
KetGPT, a tool that generates synthetic circuits in OpenQASM language, whose
structure is based on quantum circuits derived from existing quantum algorithms
and follows the typical patterns of human-written algorithm-based code (e.g.,
order of gates and qubits). Our three-fold verification process, involving
manual inspection and Qiskit framework execution, transformer-based
classification, and structural analysis, demonstrates the efficacy of KetGPT in
producing large amounts of additional circuits that closely align with
algorithm-based structures. Beyond benchmarking, we envision KetGPT
contributing substantially to AI-driven quantum compilers and systems
Visualizing Quantum Circuit Probability -- estimating computational action for quantum program synthesis
This research applies concepts from algorithmic probability to Boolean and
quantum combinatorial logic circuits. A tutorial-style introduction to states
and various notions of the complexity of states are presented. Thereafter, the
probability of states in the circuit model of computation is defined. Classical
and quantum gate sets are compared to select some characteristic sets. The
reachability and expressibility in a space-time-bounded setting for these gate
sets are enumerated and visualized. These results are studied in terms of
computational resources, universality and quantum behavior. The article
suggests how applications like geometric quantum machine learning, novel
quantum algorithm synthesis and quantum artificial general intelligence can
benefit by studying circuit probabilities.Comment: 17 page