Distributed Quantum Faithful Simulation and Function Computation Using
Algebraic Structured Measurements
- URL: http://arxiv.org/abs/2101.02360v3
- Date: Thu, 14 Oct 2021 20:06:58 GMT
- Title: Distributed Quantum Faithful Simulation and Function Computation Using
Algebraic Structured Measurements
- Authors: Touheed Anwar Atif and S. Sandeep Pradhan
- Abstract summary: We consider the task of faithfully simulating a quantum measurement acting on a joint bipartite quantum state in a distributed manner.
The computation is performed on the fly, thus obviating the need to reconstruct individual measurement outcomes at Charlie.
- Score: 8.594140167290098
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we consider the task of faithfully simulating a quantum
measurement, acting on a joint bipartite quantum state, in a distributed
manner. In the distributed setup, the constituent sub-systems of the joint
quantum state are measured by two agents, Alice and Bob. A third agent, Charlie
receives the measurement outcomes sent by Alice and Bob. Charlie uses local and
pairwise shared randomness to compute a bivariate function of the measurement
outcomes. The objective of three agents is to faithfully simulate the given
distributed quantum measurement acting on the given quantum state while
minimizing the communication and shared randomness rates. We demonstrate a new
achievable information-theoretic rate-region that exploits the bivariate
function using random structured POVMs based on asymptotically good algebraic
codes. The algebraic structure of these codes is matched to that of the
bivariate function that models the action of Charlie. The conventional approach
for this class of problems has been to reconstruct individual measurement
outcomes corresponding to Alice and Bob, at Charlie, and then compute the
bivariate function, achieved using mutually independent approximating POVMs
based on random unstructured codes. In the present approach, using algebraic
structured POVMs, the computation is performed on the fly, thus obviating the
need to reconstruct individual measurement outcomes at Charlie. Using this, we
show that a strictly larger rate region can be achieved. One of the challenges
in analyzing these structured POVMs is that they exhibit only pairwise
independence and induce only uniform single-letter distributions. To address
this, we use nesting of algebraic codes and develop a covering lemma applicable
to pairwise-independent POVM ensembles. Combining these techniques, we provide
a multi-party distributed faithful simulation and function computation
protocol.
Related papers
- Efficient Pseudomode Representation and Complexity of Quantum Impurity Models [0.7373617024876725]
Out-of-equilibrium fermionic quantum impurity models (QIM) describe a small interacting system coupled to a continuous fermionic bath.
We find efficient bath representations as that of approximating a kernel of the bath's Feynman-Vernon influence functional by a sum of complex exponentials.
To relate our findings to QIM, we derive an explicit Liouvillian that describes the time evolution of the combined impurity-pseudomodes system.
arXiv Detail & Related papers (2024-09-13T13:31:53Z) - Random-matrix models of monitored quantum circuits [0.0]
We study the competition between Haar-random unitary dynamics and measurements for unstructured systems of qubits.
For projective measurements, we derive various properties of the statistical ensemble of Kraus operators.
We expect that the statistical properties of Kraus operators will serve as a model for the entangling phase of monitored quantum systems.
arXiv Detail & Related papers (2023-12-14T18:46:53Z) - Typical bipartite steerability and generalized local quantum
measurements [0.0]
Recently proposed correlation-matrix based sufficient conditions for bipartite steerability from Alice to Bob are applied.
It is shown that this sufficient condition exhibits a peculiar scaling property.
Results are compared with a recently proposed method which reduces the determination of bipartite steerability from Alice's qubit to Bob's arbitrary dimensional quantum system.
arXiv Detail & Related papers (2023-05-29T09:48:12Z) - Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation [59.45669299295436]
We propose a Monte Carlo PDE solver for training unsupervised neural solvers.
We use the PDEs' probabilistic representation, which regards macroscopic phenomena as ensembles of random particles.
Our experiments on convection-diffusion, Allen-Cahn, and Navier-Stokes equations demonstrate significant improvements in accuracy and efficiency.
arXiv Detail & Related papers (2023-02-10T08:05:19Z) - One-Shot Distributed Source Simulation: As Quantum as it Can Get [16.75857332621569]
Distributed source simulation is the task where two (or more) parties share some randomness correlated and use local communication to convert this into some target correlation.
We do this by introducing one-shot operational quantities and correlation measures that characterize them.
In doing so, we consider technical points in one-shot network information theory and generalize the support lemma to the classical-quantum setting.
arXiv Detail & Related papers (2023-01-11T04:33:46Z) - Importance sampling for stochastic quantum simulations [68.8204255655161]
We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients.
We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage.
Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
arXiv Detail & Related papers (2022-12-12T15:06:32Z) - The vacuum provides quantum advantage to otherwise simulatable
architectures [49.1574468325115]
We consider a computational model composed of ideal Gottesman-Kitaev-Preskill stabilizer states.
We provide an algorithm to calculate the probability density function of the measurement outcomes.
arXiv Detail & Related papers (2022-05-19T18:03:17Z) - Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency [111.83670279016599]
We study reinforcement learning for partially observed decision processes (POMDPs) with infinite observation and state spaces.
We make the first attempt at partial observability and function approximation for a class of POMDPs with a linear structure.
arXiv Detail & Related papers (2022-04-20T21:15:38Z) - Efficient simulation of Gottesman-Kitaev-Preskill states with Gaussian
circuits [68.8204255655161]
We study the classical simulatability of Gottesman-Kitaev-Preskill (GKP) states in combination with arbitrary displacements, a large set of symplectic operations and homodyne measurements.
For these types of circuits, neither continuous-variable theorems based on the non-negativity of quasi-probability distributions nor discrete-variable theorems can be employed to assess the simulatability.
arXiv Detail & Related papers (2022-03-21T17:57:02Z) - Simple and practical DIQKD security analysis via BB84-type uncertainty
relations and Pauli correlation constraints [0.0]
In this work, we describe how the conditional entropy can be bounded in the 2-input/2-output setting.
We illustrate the approach on a variant of the device-independent CHSH QKD protocol where both bases are used to generate the key.
arXiv Detail & Related papers (2021-07-19T14:08:43Z) - Simulation of Thermal Relaxation in Spin Chemistry Systems on a Quantum
Computer Using Inherent Qubit Decoherence [53.20999552522241]
We seek to take advantage of qubit decoherence as a resource in simulating the behavior of real world quantum systems.
We present three methods for implementing the thermal relaxation.
We find excellent agreement between our results, experimental data, and the theoretical prediction.
arXiv Detail & Related papers (2020-01-03T11:48:11Z)
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.