Quantum variational algorithms are swamped with traps

Anschuetz, Eric R.; Kiani, Bobak T.

doi:10.1038/s41467-022-35364-5

Cited by 80 publications

(27 citation statements)

References 41 publications

(60 reference statements)

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“…In principle, the initial rotation layer is sufficient to express the solution, since it is a computational basis state because the Hamiltonian is diagonal. However, the optimization for such an ansatz gets easily stuck in local minima [14]. The idea behind the additional layers is, that they might allow the algorithm to escape from these minima.…”

Section: Layer Variational Quantum Eigensolvermentioning

confidence: 99%

Particle track reconstruction with noisy intermediate-scale quantum computers

Schwägerl¹,

İşsever²,

Jansen³

et al. 2023

Preprint

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The reconstruction of trajectories of charged particles is a key computational challenge for current and future collider experiments. Considering the rapid progress in quantum computing, it is crucial to explore its potential for this and other problems in high-energy physics. The problem can be formulated as a quadratic unconstrained binary optimization (QUBO) and solved using the variational quantum eigensolver (VQE) algorithm. In this work the effects of dividing the QUBO into smaller sub-QUBOs that fit on the hardware available currently or in the near term are assessed. Then, the performance of the VQE on small sub-QUBOs is studied in an ideal simulation, using a noise model mimicking a quantum device and on IBM quantum computers. This work serves as a proof of principle that the VQE could be used for particle tracking and investigates modifications of the VQE to make it more suitable for combinatorial optimization.

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Section: Layer Variational Quantum Eigensolvermentioning

confidence: 99%

Particle track reconstruction with noisy intermediate-scale quantum computers

Schwägerl¹,

İşsever²,

Jansen³

et al. 2023

Preprint

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“…Often QAOA fails to produces optimized solution to a problem [1]. Hardware-efficient ansatzes are used in PQC due to their simplicity and efficiency.…”

Section: Work Related To the Hurdles Of Qaoamentioning

confidence: 99%

A quantum algorithm for syndrome decoding of classical error-correcting linear block codes

Bhattacharyya

Raina

2022

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)

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We construct a quantum-classical Viterbi decoder for the classical errorcorrecting codes. Viterbi decoding is a trellis-based procedure for maximum likelihood decoding of classical error-correcting codes. In this article, we show that any number of paths with the minimum Hamming distance with respect to the received erroneous vector present in the trellis can be found using the quantum approximate optimization algorithm. We construct a generalized method to map the Viterbi decoding problem into a parameterized quantum circuit for any classical linear block codes. We propose a uniform parameter optimization strategy to optimize the parameterized quantum circuit. We observe that the proposed method is efficient for generating low-depth trainable parameterized quantum circuits. This renders the hybrid decoder more efficient than previous attempts at making quantum Viterbi algorithm. We show that using uniform parameter optimization, we obtain parameters more efficiently for the parameterized quantum circuit than many previous attempts made through random sampling and fixing the parameters.

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“…The problems encountered in optimization on quantum hardware share some similarity with classical optimization problems, but some problems are unique. While large scale optimization has many applications on classical hardware, the idea of barren plateaus [12,39,62] has largely been identified as a problem by communities looking at optimization on quantum hardware. Here we consider approaches in which high precision classical simulations can be used to find regions of convergence, and afterwards quantum hardware can be used to refine the optimization.…”

Section: Introductionmentioning

confidence: 99%

Large-scale sparse wavefunction circuit simulator for applications with the variational quantum eigensolver

Mullinax¹,

Tubman²

2023

Preprint

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The standard paradigm for state preparation on quantum computers for the simulation of physical systems in the near term has been widely explored with different algorithmic methods. One such approach is the optimization of parameterized circuits, but this becomes increasingly challenging with circuit size. As a consequence, the utility of large-scale circuit optimization is relatively unknown. In this work we demonstrate that purely classical resources can be used to optimize quantum circuits in an approximate but robust manner such that we can bridge the resources that we have from high performance computing and see a direct transition to quantum advantage. We show this through sparse wavefunction circuit solvers, which we detail here, and demonstrate a region of efficient classic simulation. With such tools, we can avoid the many problems that plague circuit optimization for circuits with hundreds of qubits using only practical and reasonable classical computing resources. These tools allow us to probe the true benefit of variational optimization approaches on quantum computers, thus opening the window to what can be expected with near term hardware for physical systems. We demonstrate this with a unitary coupled cluster ansatz on various molecules up to 64 qubits with tens of thousands of variational parameters.

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Quantum variational algorithms are swamped with traps

Cited by 80 publications

References 41 publications

Particle track reconstruction with noisy intermediate-scale quantum computers

Particle track reconstruction with noisy intermediate-scale quantum computers

A quantum algorithm for syndrome decoding of classical error-correcting linear block codes

Large-scale sparse wavefunction circuit simulator for applications with the variational quantum eigensolver

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