Variational quantum solutions to the Shortest Vector Problem
@article{Albrecht2022VariationalQS, title={Variational quantum solutions to the Shortest Vector Problem}, author={Martin R. Albrecht and Milovs Prokop and Yixin Shen and Petros Wallden}, journal={IACR Cryptol. ePrint Arch.}, year={2022}, volume={2022}, pages={233} }
. A fundamental computational problem is to find a shortest non-zero vector in Euclidean lattices, a problem known as the Shortest Vector Problem (SVP). This problem is believed to be hard even on quantum computers and thus plays a pivotal role in post-quantum cryptography. In this work we explore how (efficiently) Noisy Intermediate Scale Quantum (NISQ) devices may be used to solve SVP. Specifically, we map the problem to that of finding the ground state of a suitable Hamiltonian. In particular, (i…
References
SHOWING 1-10 OF 37 REFERENCES
Improving Variational Quantum Optimization using CVaR
- PhysicsQuantum
- 2020
This paper empirically shows that the Conditional Value-at-Risk as an aggregation function leads to faster convergence to better solutions for all combinatorial optimization problems tested in this study.
Exploring entanglement and optimization within the Hamiltonian Variational Ansatz
- PhysicsArXiv
- 2020
This paper focuses on a special family of quantum circuits called the Hamiltonian Variational Ansatz (HVA), which takes inspiration from the quantum approximation optimization algorithm and adiabatic quantum computation and exhibits favorable structural properties and numerically observes that the optimization landscape of HVA becomes almost trap free when the ansatz is over-parameterized.
Variational Quantum Algorithms
- Physics, Computer ScienceNature Reviews Physics
- 2021
An overview of the field of Variational Quantum Algorithms is presented and strategies to overcome their challenges as well as the exciting prospects for using them as a means to obtain quantum advantage are discussed.
The theory of variational hybrid quantum-classical algorithms
- Computer Science
- 2016
The concept of quantum variational error suppression that allows some errors to be suppressed naturally in this algorithm on a pre-threshold quantum device is introduced and the use of modern derivative free optimization techniques can offer dramatic computational savings of up to three orders of magnitude over previously used optimization techniques.
Lattice sieving via quantum random walks
- Computer Science, MathematicsIACR Cryptol. ePrint Arch.
- 2021
An improvement over Laarhoven’s result and an algorithm that has a (heuristic) running time of 20.2570d+o(d) where d is the lattice dimension is presented.
Generating hard instances of lattice problems (extended abstract)
- Mathematics, Computer ScienceSTOC '96
- 1996
We give a random class of lattices in Zn whose elements can be generated together with a short vector in them so that, if there is a probabilistic polynomial time algorithm which finds a short vector…
A Quantum Approximate Optimization Algorithm
- Computer Science, Mathematics
- 2014
A quantum algorithm that produces approximate solutions for combinatorial optimization problems that depends on a positive integer p and the quality of the approximation improves as p is increased, and is studied as applied to MaxCut on regular graphs.
Evolving objective function for improved variational quantum optimization
- Computer SciencePhysical Review Research
- 2022
It is shown that Ascending-CVaR in all cases performs better than standard objective functions or the “constant” CVaR of Barkoutsos et al and that it can be used as a heuristic for avoiding sub-optimal minima.
Meta-Variational Quantum Eigensolver: Learning Energy Profiles of Parameterized Hamiltonians for Quantum Simulation
- Physics
- 2020
We present the meta-VQE, an algorithm capable to learn the ground state energy profile of a parametrized Hamiltonian. By training the meta-VQE with a few data points, it delivers an initial circuit…
On lattices, learning with errors, random linear codes, and cryptography
- Computer ScienceSTOC '05
- 2005
A public-key cryptosystem whose hardness is based on the worst-case quantum hardness of SVP and SIVP, and an efficient solution to the learning problem implies a <i>quantum</i>, which can be made classical.