Scalable estimation of pure multi-qubit states
- URL: http://arxiv.org/abs/2107.05691v1
- Date: Mon, 12 Jul 2021 19:02:56 GMT
- Title: Scalable estimation of pure multi-qubit states
- Authors: L. Pereira, L. Zambrano, and A. Delgado
- Abstract summary: We introduce an inductive $n$-qubit pure-state estimation method.
The proposed method exhibits a very favorable scaling in the number of qubits when compared to other estimation methods.
We experimentally demonstrate the proposed method in one of IBM's quantum processors.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce an inductive $n$-qubit pure-state estimation method. This is
based on projective measurements on states of $2n+1$ separable bases or $2$
entangled bases plus the computational basis. Thus, the total number of
measurement bases scales as $O(n)$ and $O(1)$, respectively. Thereby, the
proposed method exhibits a very favorable scaling in the number of qubits when
compared to other estimation methods. Monte Carlo numerical experiments show
that the method can achieve a high estimation fidelity. For instance, an
average fidelity of $0.88$ on the Hilbert space of $10$ qubits is achieved with
$21$ separable bases. The use of separable bases makes our estimation method
particularly well suited for applications in noisy intermediate-scale quantum
computers, where entangling gates are much less accurate than local gates. We
experimentally demonstrate the proposed method in one of IBM's quantum
processors by estimating 4-qubit Greenberger-Horne-Zeilinger states with a
fidelity close to $0.875$ via separable bases. Other $10$-qubit separable and
entangled states achieve an estimation fidelity in the order of $0.85$ and
$0.7$, respectively.
Related papers
- Efficient nonclassical state preparation via generalized parity measurement [1.99945301851239]
We propose a measurement-based protocol that leverages the Jaynes-Cummings interaction of the bosonic mode with an ancillary two-level atom.<n>We can efficiently filter out the unwanted population and push the target mode conditionally toward the desired Fock state.<n>Our protocol can also be used to prepare a large Dicke state $|J1000,0rangle$ of a spin ensemble with a sufficiently high fidelity by less than $3$ measurements.
arXiv Detail & Related papers (2025-08-20T14:50:27Z) - Optimal Quantum Algorithm for Estimating Fidelity to a Pure State [3.2951511916931167]
We present an optimal quantum algorithm for fidelity estimation between two quantum states when one of them is pure.<n>To the best of our knowledge, this is the first query-optimal approach to fidelity estimation involving mixed states.
arXiv Detail & Related papers (2025-06-30T09:24:03Z) - Beyond likelihood ratio bias: Nested multi-time-scale stochastic approximation for likelihood-free parameter estimation [49.78792404811239]
We study inference in simulation-based models where the analytical form of the likelihood is unknown.<n>We use a ratio-free nested multi-time-scale approximation (SA) method that simultaneously tracks the score and drives the parameter update.<n>We show that our algorithm can eliminate the original bias $Obig(sqrtfrac1Nbig)$ and accelerate the convergence rate from $Obig(beta_k+sqrtfracalpha_kNbig)$.
arXiv Detail & Related papers (2024-11-20T02:46:15Z) - 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) - Simulation of IBM's kicked Ising experiment with Projected Entangled
Pair Operator [71.10376783074766]
We perform classical simulations of the 127-qubit kicked Ising model, which was recently emulated using a quantum circuit with error mitigation.
Our approach is based on the projected entangled pair operator (PEPO) in the Heisenberg picture.
We develop a Clifford expansion theory to compute exact expectation values and use them to evaluate algorithms.
arXiv Detail & Related papers (2023-08-06T10:24:23Z) - Quantum Metropolis-Hastings algorithm with the target distribution
calculated by quantum Monte Carlo integration [0.0]
Quantum algorithms for MCMC have been proposed, yielding the quadratic speedup with respect to the spectral gap $Delta$ compered to classical counterparts.
We consider not only state generation but also finding a credible interval for a parameter, a common task in Bayesian inference.
In the proposed method for credible interval calculation, the number of queries to the quantum circuit to compute $ell$ scales on $Delta$, the required accuracy $epsilon$ and the standard deviation $sigma$ of $ell$ as $tildeO(sigma/epsilon
arXiv Detail & Related papers (2023-03-10T01:05:16Z) - Quantum Approximation of Normalized Schatten Norms and Applications to
Learning [0.0]
This paper addresses the problem of defining a similarity measure for quantum operations that can be textitefficiently estimated
We develop a quantum sampling circuit to estimate the normalized Schatten 2-norm of their difference and prove a Poly$(frac1epsilon)$ upper bound on the sample complexity.
We then show that such a similarity metric is directly related to a functional definition of similarity of unitary operations using the conventional fidelity metric of quantum states.
arXiv Detail & Related papers (2022-06-23T07:12:10Z) - Quantum Resources Required to Block-Encode a Matrix of Classical Data [56.508135743727934]
We provide circuit-level implementations and resource estimates for several methods of block-encoding a dense $Ntimes N$ matrix of classical data to precision $epsilon$.
We examine resource tradeoffs between the different approaches and explore implementations of two separate models of quantum random access memory (QRAM)
Our results go beyond simple query complexity and provide a clear picture into the resource costs when large amounts of classical data are assumed to be accessible to quantum algorithms.
arXiv Detail & Related papers (2022-06-07T18:00:01Z) - TURF: A Two-factor, Universal, Robust, Fast Distribution Learning
Algorithm [64.13217062232874]
One of its most powerful and successful modalities approximates every distribution to an $ell$ distance essentially at most a constant times larger than its closest $t$-piece degree-$d_$.
We provide a method that estimates this number near-optimally, hence helps approach the best possible approximation.
arXiv Detail & Related papers (2022-02-15T03:49:28Z) - Nystr\"om Kernel Mean Embeddings [92.10208929236826]
We propose an efficient approximation procedure based on the Nystr"om method.
It yields sufficient conditions on the subsample size to obtain the standard $n-1/2$ rate.
We discuss applications of this result for the approximation of the maximum mean discrepancy and quadrature rules.
arXiv Detail & Related papers (2022-01-31T08:26:06Z) - 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) - Computationally Efficient Quantum Expectation with Extended Bell
Measurements [7.620967781722716]
We propose a method for evaluating an expectation value of an arbitrary observable $Ainmathbb C2ntimes 2n$ through na"ive Pauli measurements.
This analytical method quickly assembles the $4n$ matrix elements into at most $2n+1$ groups for simultaneous measurements.
arXiv Detail & Related papers (2021-10-19T05:06:56Z) - Divide-and-conquer verification method for noisy intermediate-scale
quantum computation [0.0]
noisy intermediate-scale quantum computations can be regarded as logarithmic-depth quantum circuits on a sparse quantum computing chip.
We propose a method to efficiently verify such noisy intermediate-scale quantum computation.
arXiv Detail & Related papers (2021-09-30T08:56:30Z) - Improved Sample Complexity for Incremental Autonomous Exploration in
MDPs [132.88757893161699]
We learn the set of $epsilon$-optimal goal-conditioned policies attaining all states that are incrementally reachable within $L$ steps.
DisCo is the first algorithm that can return an $epsilon/c_min$-optimal policy for any cost-sensitive shortest-path problem.
arXiv Detail & Related papers (2020-12-29T14:06:09Z) - Estimation of pure states using three measurement bases [0.0]
We introduce a new method to estimate unknown pure $d$-dimensional quantum states using the probability distributions associated with only three measurement bases.
The viability of the protocol is experimentally demonstrated using two different and complementary high-dimensional quantum information platforms.
arXiv Detail & Related papers (2020-06-05T03:28:51Z)
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.