Quantum Error Suppression with Subgroup Stabilisation
- URL: http://arxiv.org/abs/2404.09973v2
- Date: Thu, 13 Jun 2024 14:12:05 GMT
- Title: Quantum Error Suppression with Subgroup Stabilisation
- Authors: Bo Yang, Elham Kashefi, Dominik Leichtle, Harold Ollivier,
- Abstract summary: Quantum state purification is the functionality that, given multiple copies of an unknown state, outputs a state with increased purity.
We propose an effective state purification gadget with a moderate quantum overhead by projecting $M$ noisy quantum inputs to their subspace.
Our method, applied in every short evolution over $M$ redundant copies of noisy states, can suppress both coherent and errors by a factor of $1/M$, respectively.
- Score: 3.4719087457636792
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum state purification is the functionality that, given multiple copies of an unknown state, outputs a state with increased purity. This will be an essential building block for near- and middle-term quantum ecosystems before the availability of full fault tolerance, where one may want to suppress errors not only in expectation values but also in quantum states. We propose an effective state purification gadget with a moderate quantum overhead by projecting $M$ noisy quantum inputs to their symmetric subspace defined by a set of projectors forming a symmetric subgroup with order $M$. Our method, applied in every short evolution over $M$ redundant copies of noisy states, can suppress both coherent and stochastic errors by a factor of $1/M$, respectively. This reduces the circuit implementation cost $M$ times smaller than the state projection to the full symmetric subspace proposed by Barenco et al. more than two decades ago. We also show that our gadget purifies the depolarised inputs with probability $p$ to asymptotically $O\left(p^{2}\right)$ with an optimal choice of $M$ when $p$ is small. The sampling cost scales $O\left(p^{-1}\right)$ for small $p$, which is also shown to be asymptotically optimal. Our method provides flexible choices of state purification depending on the hardware restrictions before fully fault-tolerant computation is available.
Related papers
- Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs [56.237917407785545]
We consider the problem of learning an $varepsilon$-optimal policy in a general class of continuous-space Markov decision processes (MDPs) having smooth Bellman operators.
Key to our solution is a novel projection technique based on ideas from harmonic analysis.
Our result bridges the gap between two popular but conflicting perspectives on continuous-space MDPs.
arXiv Detail & Related papers (2024-05-10T09:58:47Z) - Towards large-scale quantum optimization solvers with few qubits [59.63282173947468]
We introduce a variational quantum solver for optimizations over $m=mathcalO(nk)$ binary variables using only $n$ qubits, with tunable $k>1$.
We analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature.
arXiv Detail & Related papers (2024-01-17T18:59:38Z) - Quantum eigenstate broadcasting assisted by a coherent link [0.0]
We show that the circuit depth of the eigenstate preparation algorithm can be reduced when the devices can share limited entanglement.
Our approach requires only a single auxiliary qubit per device to be entangled with the outside.
We show that, in the near-convergent regime, the average relative suppression of unwanted amplitudes is improved to $1/(2sqrte) approx 0.30$ per run of the protocol.
arXiv Detail & Related papers (2023-02-06T18:56:08Z) - Best Policy Identification in Linear MDPs [70.57916977441262]
We investigate the problem of best identification in discounted linear Markov+Delta Decision in the fixed confidence setting under a generative model.
The lower bound as the solution of an intricate non- optimization program can be used as the starting point to devise such algorithms.
arXiv Detail & Related papers (2022-08-11T04:12:50Z) - How to simulate quantum measurement without computing marginals [3.222802562733787]
We describe and analyze algorithms for classically computation measurement of an $n$-qubit quantum state $psi$ in the standard basis.
Our algorithms reduce the sampling task to computing poly(n)$ amplitudes of $n$-qubit states.
arXiv Detail & Related papers (2021-12-15T21:44:05Z) - Random quantum circuits transform local noise into global white noise [118.18170052022323]
We study the distribution over measurement outcomes of noisy random quantum circuits in the low-fidelity regime.
For local noise that is sufficiently weak and unital, correlations (measured by the linear cross-entropy benchmark) between the output distribution $p_textnoisy$ of a generic noisy circuit instance shrink exponentially.
If the noise is incoherent, the output distribution approaches the uniform distribution $p_textunif$ at precisely the same rate.
arXiv Detail & Related papers (2021-11-29T19:26:28Z) - Optimal Control for Closed and Open System Quantum Optimization [0.0]
We provide a rigorous analysis of the quantum optimal control problem in the setting of a linear combination $s(t)B+ (1-s(t))C$ of two noncommuting Hamiltonians.
The target is to minimize the energy of the final problem'' Hamiltonian $C$, for a time-dependent and bounded control schedule.
arXiv Detail & Related papers (2021-07-07T22:57:57Z) - Efficient Verification of Anticoncentrated Quantum States [0.38073142980733]
I present a novel method for estimating the fidelity $F(mu,tau)$ between a preparable quantum state $mu$ and a classically specified target state $tau$.
I also present a more sophisticated version of the method, which uses any efficiently preparable and well-characterized quantum state as an importance sampler.
arXiv Detail & Related papers (2020-12-15T18:01:11Z) - Exponential Error Suppression for Near-Term Quantum Devices [0.0]
In NISQ era, complexity and scale required to adopt even the smallest QEC is prohibitive.
We show that for the crucial case of estimating expectation values of observables one can indeed achieve an effective exponential suppression.
arXiv Detail & Related papers (2020-11-11T18:00:38Z) - Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and
Variance Reduction [63.41789556777387]
Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP)
We show that the number of samples needed to yield an entrywise $varepsilon$-accurate estimate of the Q-function is at most on the order of $frac1mu_min (1-gamma)5varepsilon2+ fract_mixmu_min (1-gamma)$ up to some logarithmic factor.
arXiv Detail & Related papers (2020-06-04T17:51:00Z) - Quantum Gram-Schmidt Processes and Their Application to Efficient State
Read-out for Quantum Algorithms [87.04438831673063]
We present an efficient read-out protocol that yields the classical vector form of the generated state.
Our protocol suits the case that the output state lies in the row space of the input matrix.
One of our technical tools is an efficient quantum algorithm for performing the Gram-Schmidt orthonormal procedure.
arXiv Detail & Related papers (2020-04-14T11:05:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.