Quantum circuit learning

Mitarai, Kosuke; Negoro, Makoto; Kitagawa, Masahiro; Fujii, Keisuke

doi:10.1103/physreva.98.032309

Cited by 1,198 publications

(1,138 citation statements)

References 20 publications

Supporting

Mentioning

1,135

Contrasting

Unclassified

Order By: Relevance

“…To optimize these parameters, we employ a gradient-descent approach. This approach exploits the fact that the gradient with respect to a of C HST , C LHST , C LET , and C LLET can be computed by using the circuits for HST, LHST, LET, and LLET, respectively [43,19]. We remark that we used different gradient-based approaches for the shallow and deep ansatz cases, since the latter requires a more sophisticated and efficient optimizer.…”

Section: ( ) ( )mentioning

confidence: 99%

Noise resilience of variational quantum compiling

et al. 2020

View full text Add to dashboard Cite

Variational hybrid quantum-classical algorithms (VHQCAs) are near-term algorithms that leverage classical optimization to minimize a cost function, which is efficiently evaluated on a quantum computer. Recently VHQCAs have been proposed for quantum compiling, where a target unitary U is compiled into a short-depth gate sequence V. In this work, we report on a surprising form of noise resilience for these algorithms. Namely, we find one often learns the correct gate sequence V (i.e. the correct variational parameters) despite various sources of incoherent noise acting during the costevaluation circuit. Our main results are rigorous theorems stating that the optimal variational parameters are unaffected by a broad class of noise models, such as measurement noise, gate noise, and Pauli channel noise. Furthermore, our numerical implementations on IBM's noisy simulator demonstrate resilience when compiling the quantum Fourier transform, Toffoli gate, and W-state preparation. Hence, variational quantum compiling, due to its robustness, could be practically useful for noisy intermediate-scale quantum devices. Finally, we speculate that this noise resilience may be a general phenomenon that applies to other VHQCAs such as the variational quantum eigensolver.

show abstract

Section: ( ) ( )mentioning

confidence: 99%

Noise resilience of variational quantum compiling

et al. 2020

View full text Add to dashboard Cite

show abstract

“…It may not be obvious how to optimize algorithms for a given connectivity and a given gate set. This motivates the idea of an automated approach for discovering and optimizing quantum algorithms [6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Learning the quantum algorithm for state overlap

et al. 2018

View full text Add to dashboard Cite

Short-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing the overlap rs ( ) Tr between two quantum states ρ and σ. The standard algorithm for this task, known as the Swap Test, is used in many applications such as quantum support vector machines, and, when specialized to ρ=σ, quantifies the Renyi entanglement. Here, we find algorithms that have shorter depths than the Swap Test, including one that has a constant depth (independent of problem size). Furthermore, we apply our approach to the hardware-specific connectivity and gate sets used by Rigetti's and IBM's quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error-compared to the Swap Test-on these computers.

show abstract

“…To start with, we need to compute the gradient of the value function with respect to the parameters. The favorable properties of the tensor products of Pauli matrices appearing in our gate definitions allow computation of the analytical gradient using the method proposed in [23]. For the generator, the partial derivatives read…”

Section: Near-term Implementation On Nisq Computersmentioning

confidence: 99%

“…Such approach requires the ability to execute complex controlled operations and is expected to require error correction. Our approach and others' [23,24] require much simpler circuits, which is desirable for implementation on NISQ computers.…”

Section: Near-term Implementation On Nisq Computersmentioning

confidence: 99%

Adversarial quantum circuit learning for pure state approximation

et al. 2019

View full text Add to dashboard Cite

Adversarial learning is one of the most successful approaches to modeling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers. Two parametrized circuits are optimized in tandem: one tries to approximate the target state, the other tries to distinguish between target and approximated state. Supported by numerical simulations, we show that resilient backpropagation algorithms perform remarkably well in optimizing the two circuits. We use the bipartite entanglement entropy to design an efficient heuristic for the stopping criterion. Our approach may find application in quantum state tomography.

show abstract

Quantum circuit learning

Cited by 1,198 publications

References 20 publications

Noise resilience of variational quantum compiling

Noise resilience of variational quantum compiling

Learning the quantum algorithm for state overlap

Adversarial quantum circuit learning for pure state approximation

Contact Info

Product

Resources

About